dataset.json

{
    "3d-SPADE": {
        "Name": "3d-SPADE",
        "Description": "3d-SPADE is a method to find reoccurring spike patterns in parallel spike train data, and to determine their statistical significance. It is a part of Elephant.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "AGS_ACN": {
        "Name": "AGS ACN",
        "Description": "> CORRECT NAME OF TOOL COULD ALSO BE 'ags.sh acn.sh tools', 'AGS', 'ACN' | Fast and accurate average genome size and 16S rRNA gene average copy number computation in metagenomic data | Here, we present the ags.sh and acn.sh tools dedicated to the computation of the Average Genome Size (AGS) and 16S rRNA gene Average Copy Number (ACN), respectively. The ags.sh and acn.sh tools compute these metagenomic traits based on the (ultra-fast) annotation of 35 universally distributed single-copy genes in unassembled metagenomic data | Pereira-Flores, E., Gl\u00f6ckner F. O., and Fernandez-Guerra A. Fast and accurate average genome size and 16S rRNA gene average copy number computation in metagenomic data. BMC Bioinformatics. 2019;20(1):453. doi:10.1186/s12859-019-3031-y",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "AIKYATAN": {
        "Name": "AIKYATAN",
        "Description": "mapping distal regulatory elements using convolutional learning on GPU.\n\nBACKGROUND:The data deluge can leverage sophisticated ML techniques for functionally annotating the regulatory non-coding genome. The challenge lies in selecting the appropriate classifier for the specific functional annotation problem, within the bounds of the hardware constraints and the model's complexity. In our system AIKYATAN, we annotate distal epigenomic regulatory sites, e.g., enhancers. Specifically, we develop a binary classifier that classifies genome sequences as distal regulatory regions or not, given their histone modifications' combinatorial signatures. This problem is challenging because the regulatory regions are distal to the genes, with diverse signatures across classes (e.g., enhancers and insulators) and even within each class (e.g., different enhancer sub-classes)",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "APPAGATO": {
        "Name": "APPAGATO",
        "Description": "APproximate PArallel and stochastic GrAph querying TOol for biological networks.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "Arteria": {
        "Name": "Arteria",
        "Description": "An automation system for a sequencing core facility.\n\nStackStorm packs to automate sequencing center operations.\n\nEvent-driven sequencing center automation.\n\nIt forms the core of the Arteria automation system, which you can read about on our website or preprint. This pack integrates with a series of bioinformatic micro-services, which can be found at https://github.com/arteria-project.\n\nThis pack provides re-usable units for automating tasks at a sequencing core facility using the StackStorm event-driven automation platform.\n\nHandling sequencing data from massive parallel sequencing can be a daunting task! And while the process of handling sequencing data will share many of its characteristics across centers, the current norm is one center one solution. This creates a situation where reuse is difficult to achieve and the wheel is invented over and over again. This is a situation that we hope can be remedied in the form of the Arteria project",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "BART-Seq": {
        "Name": "BART-Seq",
        "Description": "cost-effective massively parallelized targeted sequencing for genomics, transcriptomics, and single-cell analysis | Software required for Bart-Seq technology \u2013 a cheap technology to analyze (single) cells using forward and reverse barcoding for target genes | Demultiplexing pipeline for BARTSeq | Software required for Bart-Seq \u2013 a cost-effective target enrichment technology using forward and reverse barcoding to analyze selected set of genes in single cells and/or bulk RNA/DNA samples | The pipeline can be run via snakemake [-j 4] [-s \u2026/bartseq/Snakefile] [-d \u2026/mydata], where -j specifies the number of threads, and the other parameters default to ./Snakefile and ., respectively",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "BIRD": {
        "Name": "BIRD",
        "Description": "Bayesian Estimation of Genetic Regulatory Effects in High-throughput Reporter Assays | Bayesian Inference of Regulatory Differences | Photo by Bill Majoros. Used with permission | [6/16/2018] First version of BIRD released - The first version of BIRD has been released on GitHub at https://github.com/bmajoros/BIRD | [8/23/2018] Experiment design web tool released - A web tool is now available for power and sample size estimation: http://67.159.92.22:8080/ | BIRD (Bayesian Inference of Regulatory Differences) is a software suite for identifying regulatory variants in data from STARR-seq and other massively parallel reporter assays (MPRAs)",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "CLIJ": {
        "Name": "CLIJ",
        "Description": "GPU-accelerated image processing for everyone.\n\nCLIJ is an OpenCL - ImageJ bridge and a Fiji plugin allowing users with entry-level skills in programming to build GPU-accelerated workflows to speed up their image processing. Increased efforts were put on documentation, code examples, interoperability, and extensibility. CLIJ is based on ClearCL, JOCL, Imglib2, ImageJ and SciJava",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "CrystaLattE": {
        "Name": "CrystaLattE",
        "Description": "Automated computation of lattice energies of organic crystals exploiting the many-body expansion to achieve dual-level parallelism.\n\nSet of scripts to automate the calculation of crystal lattice energies.\n\nAutomated calculation of crystal lattice energies with the many-body expansion.\n\nCrystaLattE has an interface with the quantum chemistry package PSI4. To run, the code requires a crystallographic information file containing structural information of the crystal and an input file specifying execution details. Work continues in the creation of a CrystaLattE pip package. So, for the moment, the instructions to download and install CrystaLattE and to create a conda environment that includes PSI4 are presented below.\n\nCyrstaLattE is a software that automates the computation of crystal lattice energies using the many-body cluster expansion",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "Cytokit": {
        "Name": "Cytokit",
        "Description": "A single-cell analysis toolkit for high dimensional fluorescent microscopy imaging | Microscopy Image Cytometry Toolkit | Cytokit is a collection of tools for quantifying and analyzing properties of individual cells in large fluorescent microscopy datasets with a focus on those generated from multiplexed staining protocols. This includes a GPU-accelerated image processing pipeline (via TensorFlow), CLI tools for batch processing of experimental replicates (often requiring conditional configuration, as things tend go wrong when capturing hundreds of thousands of microscope images over a period of hours or days), and visualization UIs (either Cytokit Explorer or CellProfiler Analyst) | Med Google Cloud Platform kan du oprette, implementere og skalere apps, websites og tjenester p\u00e5 den samme infrastruktur som Google | Forts\u00e6t til Google Cloud Platform | Skriv den tekst, du h\u00f8rer eller ser",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "DDVFA": {
        "Name": "DDVFA",
        "Description": "Distributed dual vigilance fuzzy adaptive resonance theory learns online, retrieves arbitrarily-shaped clusters, and mitigates order dependence. Dual Vigilance Fuzzy ART - Companion MATLAB Code. Distributed Dual Vigilance Fuzzy ART - Companion MATLAB Code.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "DiversityExsiccatae": {
        "Name": "DiversityExsiccatae",
        "Description": "DiversityExsiccatae (DWB-DE) is an application of Diversity Workbench (DWB). It might be installed as part of a DWB environment, but also as a stand-alone application, e. g., in a local installation. Each DWB module is devoted to a specific data domain. DE is currently used in a core installation at the SNSB, Botanische Staatssammlung M\u00fcnchen to manage bibliographic information and standardized abbreviations of botanical and mycological exsiccatae in a global index. Exsiccatae are series of specimen collections which are distributed as a publication together with assigned printed matters. They are usually available in multiple copies worldwide. This standard list for Exsiccatal series with unique and stable series identifers is increasing the efficiency of the digitization of botanical and mycological collections.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "Dr.seq2": {
        "Name": "Dr.seq2",
        "Description": "A quality control and analysis pipeline for parallel single cell transcriptome and epigenome data. It provides quality control and analysis functionalities for three data types: single cell transcriptome data, Drop-ChIP data and scATAC-seq data.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "DstarM": {
        "Name": "DstarM",
        "Description": "> MEDIUM CONFIDENCE! | > HOMEPAGE MISSING! | > CORRECT NAME OF TOOL COULD ALSO BE 'nondecision', 'DM', 'two-choice' | an R package for analyzing two-choice reaction time data with the D\u2217M method | The decision process in choice reaction time data is traditionally described in detail with diffusion models. However, the total reaction time is assumed to consist of the sum of a decision time (as modeled by the diffusion process) and the time devoted to nondecision processes (e.g., perceptual and motor processes). It has become standard practice to assume that the nondecision time is uniformly distributed. However, a misspecification of the nondecision time distribution introduces bias in the parameter estimates for the decision model. Recently, a new method has been proposed (called the D\u2217M method) that allows the estimation of the decision model parameters, while leaving the nondecision time distribution unspecified",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "EPA-ng": {
        "Name": "EPA-ng",
        "Description": "Complete reimplementation of the evolutionary placement algorithm (EPA) that is substantially faster, offers a distributed memory parallelization, and integrates concepts from both, RAxML-EPA and PPLACER.",
        "parallel": true,
        "distributed": true,
        "GPU": false
    },
    "EVR": {
        "Name": "EVR",
        "Description": "EVR is a chromosome 3D structure reconstruction tool using an Error-Vector Resultant algorithm based on DNA interaction data. With normalized or unnormalized IF matrix as input, the program generate a chromosome 3D structure output in a .pdb file. Using Cython and OpenCL, the program can run on CPUs/GPUS/APUs and thus usually faster than similar tools.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "Eoulsan": {
        "Name": "Eoulsan",
        "Description": "A versatile framework based on the Hadoop implementation of the MapReduce algorithm, dedicated to high throughput sequencing data analysis on distributed computers. With Eoulsan, users can easily set up a cloud computing cluster and automate the analysis of several samples at once using various software solutions available. Working either on standalone workstations or cloud computing clusters, Eoulsan provides an integrated and flexible solution for RNA-Seq data analysis of differential expression.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "FB5P-seq": {
        "Name": "FB5P-seq",
        "Description": "FACS-based 5-prime end single-cell RNAseq for integrative analysis of transcriptome and antigen receptor repertoire in B and T cells.\n\nFB5P-seq: FACS-based 5'-end single-cell RNA-seq.\n\nCopyright 2019: PMlab, Centre d'Immunologie de Marseille-Luminy This work is distributed under the terms of the GNU General Public License. It is free to use for all purposes.\n\nFB5P-seq is a computational pipeline to process single-cell RNA sequencing (scRNAseq) data produced with the FB5P-seq protocol designed by the Milpied lab at Centre d'Immunologie de Marseille-Luminy. The pipeline relies on 5 main softwares:",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "FQStat": {
        "Name": "FQStat",
        "Description": "A parallel architecture for very high-speed assessment of sequencing quality metrics | BACKGROUND:High throughput DNA RNA sequencing has revolutionized biological and clinical research. Sequencing is widely used, and generates very large amounts of data, mainly due to reduced cost and advanced technologies. Quickly assessing the quality of giga-to-tera base levels of sequencing data has become a routine but important task. Identification and elimination of low-quality sequence data is crucial for reliability of downstream analysis results. There is a need for a high-speed tool that uses optimized parallel programming for batch processing and simply gauges the quality of sequencing data from multiple datasets independent of any other processing steps. RESULTS:FQStat is a stand-alone, platform-independent software tool that assesses the quality of FASTQ files using parallel programming",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "FastFeatGen": {
        "Name": "FastFeatGen",
        "Description": "Faster parallel feature extraction from genome sequences and efficient prediction of DNA N6-methyladenine sites.\n\nFaster parallel feature extraction from genome sequence.\n\nThis is a tool for faster feature extraction from genome sequences and making efficient prediction. To build efficient prediction model, user can go through the following instructions step by step. If user only wants to predict query sequences from our built model, then just go to step Make prediction for query sequences. Currently, FastFeatGen supports text file as shown in dataset.txt file in datasets directory; however, fasta file can also be used with a simple preprocessing",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "Flint": {
        "Name": "Flint",
        "Description": "Large scale microbiome profiling in the cloud | Main repository of the Flint project for Spark and Amazon EMR | This is the main repository of the Flint project for Amazon Web Services. Flint is a metagenomics profiling pipeline that is built on top of the Apache Spark framework, and is designed for fast real-time profiling of metagenomic samples against a large collection of reference genomes. Flint takes advantage of Spark's built-in parallelism and streaming engine architecture to quickly map reads against a large reference collection of bacterial genomes",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "FunGeCo": {
        "Name": "FunGeCo",
        "Description": "A web based tool for estimation of Functional potential of bacterial genomes and microbiomes using Gene Context information.\n\nFunctional potential of bacterial genomes and microbiomes from gene context information.\n\nThis feature allows the user to input a newly sequenced genome and annotate it using gene context based modules generated using extensive literature mining and manual curation. Users can also carry out comparative analysis (synteny view using parallel coordinates) of the uploaded genome with genomes already sequenced in NCBI using interactive visualizations.\n\nThis feature allows comparison of functional modules in sequenced genomes obtained from NCBI. Users can interactively select upto five genomes which are compared using a synteny based visualization (parallel coordinates) and circular genomic representations. Information about individual modules in all these genomes can also be viewed as tabular outputs",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "GASAL2": {
        "Name": "GASAL2",
        "Description": "a GPU accelerated sequence alignment library for high-throughput NGS data.\n\nGASAL2 - GPU-accelerated DNA alignment library.\n\nGASAL2 is an easy-to-use CUDA library for DNA/RNA sequence alignment algorithms. Currently it supports different kind of alignments:.\n\nA Linux platform with CUDA toolkit 8 or higher is required, along with usual build environment for C and C++ code. GASAL2 has been tested over NVIDIA GPUs with compute capabilities of 2.0, 3.5 and 5.0. Although lower versions of the CUDA framework might work, they have not been tested",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "GET-IT": {
        "Name": "GET-IT",
        "Description": "Exploiting observations and measurement data standard for distributed LTER-Italy freshwater sites. Water quality issues.\n\nGeoinformation Enabling ToolkIT starterkit.\n\nGeoinformation Enabling ToolkIT starterkit ®.\n\nGET-IT allows you to easily share geospatial data on the web with simple actions.\n\nShare your maps, measurements, and sensors.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "GLM-PCA": {
        "Name": "GLM-PCA",
        "Description": "Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model.\n\nDimension Reduction of Non-Normally Distributed Data.\n\nImplements a generalized version of principal components analysis (GLM-PCA) for dimension reduction of non-normally distributed data such as counts or binary matrices.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "GWAS-Flow": {
        "Name": "GWAS-Flow",
        "Description": "A GPU accelerated framework for efficient permutation based genome-wide association studies | GPU accelerated GWAS framework based on TensorFlow | GWAS-Flow was written and published in the hope that you might find it useful. If you do and use it for your research please cite the paper published alongside the software, which is currently publicly accessible on the BiorXiv preprint server. https://www.biorxiv.org/content/10.1101/783100v1 doi: 10.1101/783100",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "Gene2vec": {
        "Name": "Gene2vec",
        "Description": "Machine learning method that utilizes transcriptome-wide gene co-expression to generate a distributed representation of genes.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "GeneRax": {
        "Name": "GeneRax",
        "Description": "A tool for species tree-aware maximum likelihood based gene tree inference under gene duplication, transfer, and loss | GeneRax is a parallel tool for species tree-aware maximum likelihood based gene tree inference under gene duplication, transfer, and loss",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "GenomeScope_2.0": {
        "Name": "GenomeScope 2.0",
        "Description": "Reference-free profiling of polyploid genomes | We have developed GenomeScope 2.0, which applies classical insights from combinatorial theory to establish a detailed mathematical model of how k-mer frequencies will be distributed in heterozygous and polyploid genomes | Average k-mer coverage for polyploid genome | Upload results from running Jellyfish or KMC",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "GenomicsDB": {
        "Name": "GenomicsDB",
        "Description": "Advancing clinical cohort selection with genomics analysis on a distributed platform.\n\nHighly performant data storage in C++ for importing, querying and transforming variant data with Java/Spark. Used in gatk4.\n\nSparse Array Storage for Genomics.\n\nGenomicsDB, originally from Intel Health and Lifesciences, is built on top of a fork of htslib and a tile-based array storage system for importing, querying and transforming variant data.\n\nOpen source project providing a collaboration to optimizing sparse array storage for genomics.\n\nUsing high-level APIs provided in C++, Java*, and Spark*, users can both write and read variant records to and from GenomicsDB shared-nothing instances in parallel using multiple processes in a Single Process Multiple Data (SPMD) manner.",
        "parallel": true,
        "distributed": true,
        "GPU": false
    },
    "GoldenMutagenesis": {
        "Name": "GoldenMutagenesis",
        "Description": "The Golden Gate cloning technique has been proven to be a highly efficient toolbox for a variety of cloning setups. Based on its modular concept it is particularly suitable for the use in multiple-site mutagenesis approaches. In this technical note we developed a protocol termed Golden Mutagenesis for the rapid, easy, reliable and cheap formation of mutagenesis libraries. One to five positions could be altered in parallel or simultaneously within two days. To facilitate the implementation of this technique, this R-library has been developed for the automated primer design and the graphical evaluation of sequencing results to determine the quality of the library.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "GsVec": {
        "Name": "GsVec",
        "Description": "Comprehensive biological interpretation of gene signatures using semantic distributed representation.\n\nGsVec (Gene signature Vector) is an analysis method that supports the biological interpretation of Gene signature obtained by gene expression analysis of Bioinformatics. The association between the gene signature to be interpreted and the gene signature of the Pathway / Gene Ontology data base is performed by natural language processing",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "HIrisPlex-S": {
        "Name": "HIrisPlex-S",
        "Description": "Massively parallel sequencing solutions for two common forensically used platforms | HIrisPlex-S Eye, Hair and Skin Colour DNA Phenotyping Webtool | 8px 9px 10px 11px 12px 13px 14px 15px 16px 17px 18px | With the advancement of DNA phenotyping as a tool in Forensic and Anthropological usage, we now provide an easy to use interactive website to predict eye, hair and skin colour from DNA using the IrisPlex, HIrisPlex and HIrisPlex-S systems",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "HPCCS": {
        "Name": "HPCCS",
        "Description": "Collision Cross Section Calculations Using HPCCS.\n\nThe High Performance Collision Cross Section (HPCCS) is a new software for fast and accurate calculation of CCS for molecular ions. Based on the Trajectory Method (TM), HPCCS was parallelized and optimized to be an user-friendly program.\n\nHigh Performance Collision Cross Section Calculation \u2013 HPCCS",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "HiPFSTA": {
        "Name": "HiPFSTA",
        "Description": "Gradient-based, GPU-accelerated, high-precision contour-segmentation algorithm with application to cell membrane fluctuation spectroscopy.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "ICGRM": {
        "Name": "ICGRM",
        "Description": "Integrative construction of genomic relationship matrix combining multiple genomic regions for big dataset.\n\nBACKGROUND:Genomic prediction is an advanced method for estimating genetic values, which has been widely accepted for genetic evaluation in animal and disease-risk prediction in human. It estimates genetic values with genome-wide distributed SNPs instead of pedigree. The key step of it is to construct genomic relationship matrix (GRM) via genome-wide SNPs; however, usually the calculation of GRM needs huge computer memory especially when the SNP number and sample size are big, so that sometimes it will become computationally prohibitive even for super computer clusters. We herein developed an integrative algorithm to compute GRM.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "MAP": {
        "Name": "MAP",
        "Description": "Model-based analysis of proteomic data to detect proteins with significant abundance changes.\n\nMAP (Model-based Analysis of Proteomic data), is designed to statistically compare the proteomic profiles generated from different biological samples using the isotope labeling based mass spectrometry (MS) technique and directly identify proteins with significant abundance changes. Unlike many existing tools for this purpose, it does not require parallel/additional technical replicates to fathom technical variations; instead, MAP uses a novel step-by-step regression analysis to directly model technical variations from the profiles under comparison. Therefore, experimental designs and their expenses can be simplified and reduced for more practices",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "MARGO": {
        "Name": "MARGO",
        "Description": "MARGO (Massively Automated Real-time GUI for Object-tracking).\n\nFast object tracking in real time allows convenient tracking of very large numbers of animals and closed-loop experiments that control stimuli for many animals in parallel. We developed MARGO, a MATLAB-based, real-time animal tracking suite for custom behavioral experiments. We demonstrated that MARGO can rapidly and accurately track large numbers of animals in parallel over very long timescales, typically when spatially separated such as in multiwell plates. We incorporated control of peripheral hardware, and implemented a flexible software architecture for defining new experimental routines. These features enable closed-loop delivery of stimuli to many individuals simultaneously. We highlight MARGO's ability to coordinate tracking and hardware control with two custom behavioral assays (measuring phototaxis and optomotor response) and one optogenetic operant conditioning assay.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "MCSeEd": {
        "Name": "MCSeEd",
        "Description": "A reference-free, whole genome profiling system to address cytosine/adenine methylation changes.\n\nMethods for investigating DNA methylation nowadays either require a reference genome and high coverage, or investigate only CG methylation. Moreover, no large-scale analysis can be performed for N6-methyladenosine (6 mA) at an affordable price. Here we describe the methylation content sensitive enzyme double-digest restriction-site-associated DNA (ddRAD) technique (MCSeEd), a reduced-representation, reference-free, cost-effective approach for characterizing whole genome methylation patterns across different methylation contexts (e.g., CG, CHG, CHH, 6 mA). MCSeEd can also detect genetic variations among hundreds of samples. MCSeEd is based on parallel restrictions carried out by combinations of methylation insensitive and sensitive endonucleases, followed by next-generation sequencing.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "MMCL": {
        "Name": "MMCL",
        "Description": "GPU-accelerated mesh-based Monte Carlo photon transport simulations.\n\n===============================================================================.\n\n= Mesh-based Monte Carlo (MMC) =.\n\n= Supporting both OpenCL and Multi-threading with SSE4 =.\n\nA GPU-accelerated photon transport simulator.\n\nDownload v2019.4 or Nightly-build.\n\n'mmc -L' or 'mmclab('gpuinfo')' to list).\n\nDesigning MATLAB-streamlined MC-based analysis using MCXLAB and MMCLAB.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "MPRAnalyze": {
        "Name": "MPRAnalyze",
        "Description": "Statistical framework for massively parallel reporter assays | Statistical Analysis of MPRA data | MPRAnalyze provides statistical framework for the analysis of data generated by Massively Parallel Reporter Assays (MPRAs), used to directly measure enhancer activity. MPRAnalyze can be used for quantification of enhancer activity, classification of active enhancers and comparative analyses of enhancer activity between conditions. MPRAnalyze construct a nested pair of generalized linear models (GLMs) to relate the DNA and RNA observations, easily adjustable to various experimental designs and conditions, and provides a set of rigorous statistical testig schemes",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "MPRAnator": {
        "Name": "MPRAnator",
        "Description": "Web-based tool for the design of massively parallel reporter assay experiments.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "MRUniNovo": {
        "Name": "MRUniNovo",
        "Description": "Tool for de novo peptide sequencing utilizing the hadoop distributed computing framework.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "MasterOfPores": {
        "Name": "MasterOfPores",
        "Description": "Parallel and scalable workflow for the analysis of Oxford Nanopore direct RNA sequencing datasets.\n\nNextflow pipeline for analysis of Nanopore reads (from RNA/cDNA/DNA).\n\nPlease read the documentation here: https://biocorecrg.github.io/master_of_pores/.\n\nNextflow pipeline for analysis of Nanopore data from direct RNA sequencing. This is a joint project between CRG bioinformatics core and Epitranscriptomics and RNA Dynamics research group.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "MaveDB": {
        "Name": "MaveDB",
        "Description": "An open-source platform to distribute and interpret data from multiplexed assays of variant effect.\n\nTable of Multiplexed Assay of Variant Effect (MAVE) studies.\n\nMaveDB - A repository for MAVE assay datasets.\n\nTo cite this document, please use the citation details for MaveDB.\n\nMaveDB is a public repository for datasets from Multiplexed Assays of Variant Effect (MAVEs), such as those generated by deep mutational scanning (DMS) or massively parallel reporter assay (MPRA) experiments.\n\nWelcome to our table of Multiplexed Assay of Variant Effect (MAVE) studies. To contribute a study or amend/expand an existing entry, please use the GitHub issue tracker or create a pull request",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "MetaSanity": {
        "Name": "MetaSanity",
        "Description": "An integrated, customizable microbial genome evaluation and annotation pipeline.\n\nPipeline for major biological analyses.\n\nMetaSanity v1.1.1 - 2020 version.\n\nMetaSanity v1.1.1 provides a unified workflow for genome assessment and functional annotation that combines all outputs into a single queryable database \u2013 all within an easily distributed Docker image",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "MetaSpark": {
        "Name": "MetaSpark:",
        "Description": "Spark-based distributed processing tool to recruit metagenomic reads to reference genomes.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "Metage2Metabo": {
        "Name": "Metage2Metabo",
        "Description": "metabolic complementarity applied to genomes of large-scale microbiotas for the identification of keystone species.\n\nFrom annotated genomes to metabolic screening in large scale microbiotas.\n\nMetage2metabo is a Python3 (Python >= 3.6) tool to perform graph-based metabolic analysis starting from annotated genomes (reference genomes or metagenome-assembled genomes). It uses Pathway Tools in a automatic and parallel way to reconstruct metabolic networks for a large number of genomes. The obtained metabolic networks are then analyzed individually and collectively in order to get the added value of metabolic cooperation in microbiota over individual metabolism and to identify and screen interesting organisms among all.\n\nm2m \u2014 metage2metabo documentation.\n\nFree document hosting provided by Read the Docs",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "MobCal-MPI": {
        "Name": "MobCal-MPI",
        "Description": "Programfor calculation of  ion or molecule collision cross sections on parallelized computing architectures.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "MySeq": {
        "Name": "MySeq",
        "Description": "Privacy-protecting browser-based personal Genome analysis for genomics education and exploration.\n\nMySeq is a web-application for privacy-protecting interactive analysis of personal genomes (distributed as compressed-and-indexed VCF files) inspired by GENOtation (previously the Interpretome) and DNA.LAND Compass. MySeq is intended for use as a genomics educational platform.\n\nAnalyzing the PTC Tasting Phenotype with MySeq.\n\nThis page is an example analysis of the 'bitter tasting' trait using the MySeq application in an embedded context. Here MySeq is used to both query a whole genome VCF for NA12878 (from Genome in a Bottle) by genomic coordinates and predict the bitter tasting phenotype directly. All of the queries demonstrated here are performed 'live' in the browser, that is these are not pre-generated results. Try MySeq as a 'standalone' application.\n\nMySeq is a single-page web application for privacy-protecting personal genome analysis",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "NCPHLDA": {
        "Name": "NCPHLDA",
        "Description": "A novel method for human lncRNA-disease association prediction based on network consistency projection.\n\nCopyright (C) 2019 Zecheng Huang(2111705162@mail2.gdut.edu.cn),Guobo Xie(xiegb@gdut.edu.cn).\n\nThis program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.\n\nType: Package Title: NCPHLDA:A Novel Method for Human LncRNA-Disease Association Prediction Based on Network Consistency Projection.\n\nTo analyze these data on NCPHLDA to further infer potential associations between lncRNAs and diseases, you should input the appropriate code in the matlab Command Window:",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "NMF-RI": {
        "Name": "NMF-RI",
        "Description": "Blind spectral unmixing of highly mixed multispectral flow and image cytometry data.\n\nMatlab code to spectrally unmix highly mixed multispectral flow and image cytometry data.\n\nTo use NMF-RI on new data (tissue or flow cytometry) we recommend to use the \u00b4main.m\u00b4 code distributed and load new cytometry data to the existing folder hierarchy.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "NUFEB": {
        "Name": "NUFEB",
        "Description": "Massively Parallel Simulator for Individual-based Modelling of Microbial Communities.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "OptiJ": {
        "Name": "OptiJ",
        "Description": "Open-source optical projection tomography of large organ samples.\n\nThe three-dimensional imaging of mesoscopic samples with Optical Projection Tomography (OPT) has become a powerful tool for biomedical phenotyping studies. OPT uses visible light to visualize the 3D morphology of large transparent samples. To enable a wider application of OPT, we present OptiJ, a low-cost, fully open-source OPT system capable of imaging large transparent specimens up to 13 mm tall and 8 mm deep with 50 \u00b5m resolution. OptiJ is based on off-the-shelf, easy-to-assemble optical components and an ImageJ plugin library for OPT data reconstruction. The software includes novel correction routines for uneven illumination and sample jitter in addition to CPU GPU accelerated reconstruction for large datasets. We demonstrate the use of OptiJ to image and reconstruct cleared lung lobes from adult mice.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "P3BSseq": {
        "Name": "P3BSseq",
        "Description": "Parallel processing pipeline software for automatic analysis of bisulfite sequencing data.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "PRINCESS": {
        "Name": "PRINCESS",
        "Description": "Privacy-preserving international collaboration framework for analyzing rare disease genetic data that are distributed across different continents.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "PVCTools": {
        "Name": "PVCTools",
        "Description": "parallel variation calling tools.\n\nPVCTools is Parallel Variation Calling Tools, which tries to call variation using multiple threads. Basically, the reference genome will be splited into small pieces, and corresponding alignment BAM files will be extracted. Under this way, it will speed up the process of variation calling a lot",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "PaSGAL": {
        "Name": "PaSGAL",
        "Description": "PaSGAL (Parallel Sequence to Graph Aligner) is designed to accelerate local sequence alignment of sequences to directed acyclic sequence graphs (DAGs), e.g., variation graphs, splicing graphs.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "ParGenes": {
        "Name": "ParGenes",
        "Description": "Tool for massively parallel model selection and phylogenetic tree inference on thousands of genes.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "ParLECH": {
        "Name": "ParLECH",
        "Description": "A hybrid and scalable error correction algorithm for indel and substitution errors of long reads.\n\nBACKGROUND:Long-read sequencing has shown the promises to overcome the short length limitations of second-generation sequencing by providing more complete assembly. However, the computation of the long sequencing reads is challenged by their higher error rates (e.g., 13% vs. 1%) and higher cost ($0.3 vs. $0.03 per Mbp) compared to the short reads. METHODS:In this paper, we present a new hybrid error correction tool, called ParLECH (Parallel Long-read Error Correction using Hybrid methodology). The error correction algorithm of ParLECH is distributed in nature and efficiently utilizes the k-mer coverage information of high throughput Illumina short-read sequences to rectify the PacBio long-read sequences.",
        "parallel": true,
        "distributed": true,
        "GPU": false
    },
    "PathFlowAI": {
        "Name": "PathFlowAI",
        "Description": "A High-Throughput Workflow for Preprocessing, Deep Learning and Interpretation in Digital Pathology | A Convenient High-Throughput Workflow for Preprocessing, Deep Learning Analytics and Interpretation in Digital Pathology | MedRxiv Manuscript: https://www.medrxiv.org/content/10.1101/19003897v1 | Fig. 1. PathFlowAI Framework: a) Annotations and whole slide images are preprocessed in parallel using Dask; b) Deep learning prediction model is trained on the model; c) Results are visualized; d) UMAP embeddings provide diagnostics; e) SHAP framework is used to find important regions for the prediction",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "PepVis": {
        "Name": "PepVis",
        "Description": "PepVis tool is a python based GUI pipeline which can be used to model and prepare large-scale peptide structure from the sequence and also to perform large-scale peptide virtual screening. PepVis integrates ModPep and Gromacs for modelling and structure optimization of the peptides, while it integrates AutoDock Vina,ZDOCK, AutoDock CrankPep(ADCP) for performing peptide virtual screening. \nThe protein-peptide complexes can be rescored using ZRANK2 and the flexible refinement of the large protein-peptide complexes can also be performed using FlexPepDock. The parallel job execution has been implemented using GNU parallel and the user can provide inputs using GUI which will produce the bash script based on the customized input provided by the user and can be run in terminal.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "Phen2Gene": {
        "Name": "Phen2Gene",
        "Description": "Phen2Gene is a phenotype-driven gene prioritization tool, that takes HPO (Human Phenotype Ontology) IDs as inputs, searches and prioritizes candidate causal disease genes. It is distributed under the MIT License by Wang Genomics Lab. Additionally, we have provided a web server and an associated RESTful API service for running Phen2Gene. Finally, a mobile app for Phen2Gene and several other genetic diagnostic tools from our lab is being tested and will be available soon.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "PyBDA": {
        "Name": "PyBDA",
        "Description": "A command-line tool for analysis of big biological data sets for distributed HPC clusters.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "RelocaTE2": {
        "Name": "RelocaTE2",
        "Description": "a high resolution transposable element insertion site mapping tool for population resequencing.\n\nThis tool is for mapping TEs from resequencing data: Stajich lab.\n\nRelocaTE2: a high resolution transposable element insertion sites mapping tool for population resequencing.\n\nRelocaTE2 is an improved version of RelocaTE (Robb et al., 2013). RelocaTE2 is highly sensitive and accurate in mapping transposable elements (TE) polymorphisms at single base pair resolution. RelocaTE2 uses the reads associated with TEs as seeds to cluster the read pairs on chromosomes. It automatically detects the target site duplication (TSD) of a TE insertion from alignments in each cluster, which enable high resolution mapping of TE polymorphisms. Unlike parallel searching of multi-TE elements in RelocaTE, RelocaTE2 searches all TEs in one cycle, which enables us find polymorphisms of thousands of TEs in an individual genome or large population in a reasonable timeframe without losing sensitivity and specificity",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "SDR": {
        "Name": "SDR",
        "Description": "A GRASS tool for the Sediment Delivery Ratio mapping.\n\nThis work involves a tool implementation for evaluating Sediment Delivery Ratio (SDR) in a river basin, through GRASS GIS software. The definition of a spatially distributed value of SDR is a very important task as the sediment routing can affects solid transport in stream channels, water quality degradation, and frequency increase of natural disasters such as debris flows and mudflows. For such reason the SDR evaluation, coupled with GIS approach, has been extensively used in scientific literature. Geographic information systems provide a fundamental support for a better prediction of SDR, since it can consider the space variability of factors influencing the sediment routing processes. Actually a specific GIS module to estimate the spatial variability of SDR does not exist.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "SIIM": {
        "Name": "SIIM",
        "Description": "SIIM is a professional organization at the nexus of medical imaging informatics and healthcare technologies. \nSIIM provides an unparalleled opportunity to not only hear from the best, brightest and most forward thinkers in imaging space but talk to them face to face in a collegial setting.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "SW-Tandem": {
        "Name": "SW-Tandem",
        "Description": "Tool for large-scale peptide identification with parallel spectrum dot product on Sunway TaihuLight.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "SWAPCounter": {
        "Name": "SWAPCounter",
        "Description": "Counting Kmers for Biological Sequences at Large Scale.\n\nThis is a distributed kmer counting tools for TB-PB sequencing dataset",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "SigUNet": {
        "Name": "SigUNet",
        "Description": "signal peptide recognition based on semantic segmentation.\n\nA signal peptide predictor based on deep learning.\n\nFor CPU: pip3 install -r requirement.cpu.txt.\n\nFor GPU (suggest): pip3 install -r requirement.gpu.txt",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "SpliceVec-g": {
        "Name": "SpliceVec-g",
        "Description": "SpliceVec: Distributed feature representations for splice junction prediction",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "Treerecs": {
        "Name": "Treerecs",
        "Description": "Treerecs is an open-source (species- and gene-) tree reconciliation software distributed under the GNU AGPL licence.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "VGE": {
        "Name": "VGE",
        "Description": "Virtual Grid Engine (VGE) is a kind of middleware for running bioinformatics software pipelines on large-scale supercomputers which do not support any grid engine survices. VGE employs master-worker model. It first reserves processors and/or cores by running the job which is parallelized by MPI, then asign divided small tasks onto its worker processes. VGE is written in python.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "Wang-xiaoheng": {
        "Name": "Wang-xiaoheng",
        "Description": "Prediction of protein structural classes by different feature expressions based on 2-D wavelet denoising and fusion.\n\n2D-wavelet-for-protein-structural-classes-prediction We constructed a prediction model based on wavelet denoising using different feature expression methods. A new fusion idea, first fuse and then denoise, is proposed in this article. Two types of pseudo amino acid compositions are utilized to distill feature vectors. Then, a two-dimensional (2-D) wavelet denoising algorithm is used to remove the redundant information from two extracted feature vectors. The two feature vectors based on parallel 2-D wavelet denoising are fused, which is known as PWD-FU-PseAAC. The project includes three original datasets, source code for two-dimensional wavelet denoising and source code for feature vector prediction",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "adacgh2": {
        "Name": "ADaCGH2",
        "Description": "Analysis and plotting of array CGH data. Allows usage of Circular Binary Segementation, wavelet-based smoothing (both as in Liu et al., and HaarSeg as in Ben-Yaacov and Eldar), HMM, BioHMM, GLAD, CGHseg. Most computations are parallelized (either via forking or with clusters, including MPI and sockets clusters) and use ff for storing data.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "affypara": {
        "Name": "affyPara",
        "Description": "The package contains parallelized functions for exploratory oligonucleotide array analysis. The package is designed for large numbers of microarray data.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "affyparaebi": {
        "Name": "affyParaEBI",
        "Description": "Aan R based pipeline for parallel pre-processing of Affymetrix TM chips. The pipeline starts from a directory containing raw CEL files files and produces Bioconductor R objects containing gene expression measurements suitable for further analysis.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "ahoj": {
        "Name": "AHoJ: Apo Holo Protein Search",
        "Description": "Webserver & command-line tool for search and alignment of APO (unbound) protein structures from HOLO (bound) forms and vice versa. Features: customizable search of Apo-Holo pairs in the PDB, alignment to the query structure, batch mode for fast parallel dataset processing, visualization via Molstar and PyMol, public documented REST-API.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "alexa-seq": {
        "Name": "ALEXA-Seq",
        "Description": "Alternative Expression Analysis by massively parallel RNA sequencing",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "ambergpumdsimulation": {
        "Name": "AmberGPUMDSimulation",
        "Description": "An automated workflow tool for Kepler to perform AMBER GPU molecular dynamics simulations.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "anexvis": {
        "Name": "anexVis",
        "Description": "anexVis is a transcriptome tool to visualize organ/tissue-specific glycosaminoglycan biosynthetic and catabolic pathways in human health and diseases. anexVis  allows one to analyze a large number of genes that are related to biosynthetic and catabolic pathways of all glycosaminoglycans, such as heparan sulfate, chondroitin sulfate, keratan sulfate, and hyaluronic acid, in parallel across various human tissues organs. Such visual analyses have not been accessible to the broad research community despite the accumulation of a large amount of RNA-seq data.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "apache_trino": {
        "Name": "Apache Trino",
        "Description": "Trino is a distributed SQL query engine designed to query large data sets distributed over one or more heterogeneous data sources.\n\nTrino is a tool designed to efficiently query vast amounts of data using distributed queries. If you work with terabytes or petabytes of data, you are likely using tools that interact with Hadoop and HDFS. Trino was designed as an alternative to tools that query HDFS using pipelines of MapReduce jobs, such as Hive or Pig, but Trino is not limited to accessing HDFS. Trino can be and has been extended to operate over different kinds of data sources, including traditional relational databases and other data sources such as Cassandra.\n\nTrino was designed to handle data warehousing and analytics: data analysis, aggregating large amounts of data and producing reports. These workloads are often classified as Online Analytical Processing (OLAP).",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "aqmm": {
        "Name": "aqmm",
        "Description": "The AQMM was developed to perform absolute quantification of multiple metagenomes and it's parallel metatranscriptome. In order to use this algorithm, the experiment should be designed with both metagenome and metatranscriptome data. From the initial stage, the molecular experimental data should be recorded to help on estimating the overall DNA or RNA of a unit (ml/gram) of the sample. The AQMM was demonstrated to obtain better results of differential expression genes identification in comparative metatranscriptomic studies.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "aradeepopsis": {
        "Name": "ARADEEPOPSIS",
        "Description": "ARADEEPOPSIS is a software tool that enables plant researchers to non-invasively score plant growth, biomass accumulation and senescence from image data in a highly parallelized, high throughput, yet easy to use manner.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "arion_4_omics": {
        "Name": "Arion 4 Omics",
        "Description": "High performance, \u2018end-to-end\u2019 analysis pipeline for the classification of omics profiles. Incorporating highly parallel architecture and sophisticated database technologies to overcome inherent technology based bottlenecks, currently faced in the Life Science research path. Arion is a scalable platform providing rapid exploratory analysis via machine learning, visualization and statistical modules with topology planned for a future release.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "asltbx": {
        "Name": "ASLtbx",
        "Description": "Imaging Clinically Relevant Pain States Using Arterial Spin Labeling | The Center for Functional Neuroimaging at the University of Pennsylvania provides unification for currently distributed medical center efforts in physiological and clinical brain imaging and advance the general interests of the brain imaging community through targeted methods development, symposia and colloquia, handling of regulatory issues, and fund-raising efforts | Updated manual, ASL MoCo code, and asl_perf_subtract.m. Major new features include: | asl_perf_subtract.m has been updated with new parameters (T1b, T2wm, Rwm-blood) from the latest literature. CBF quantification for PASL using voxelwise M0 value has been updated. Please see the document therein for the details | Made a new version for SPM12. Major new features include: | 1) wraper functors for most of the processing steps,",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "asterias": {
        "Name": "Asterias",
        "Description": "Set of tools for the analyses of high throughput genomic data that includes applications for microarray data normalization, filtering, detection of differential gene expression, class and survival prediction model building, and analysis of array CGH data. Most applications use parallel computing resulting in significant increases in speed.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "autodeconj": {
        "Name": "AutoDeconJ",
        "Description": "A GPU-accelerated ImageJ plugin for 3D light-field deconvolution with optimal iteration numbers predicting.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "autorelacs": {
        "Name": "AutoRELACS",
        "Description": "AutoRELACS is a Python script for automated generation and analysis of ultra-parallel ChIP-seq.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "basenumber": {
        "Name": "BaseNumber",
        "Description": "High performance of a GPU-accelerated variant calling tool in genome data analysis",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "batchmap": {
        "Name": "BatchMap",
        "Description": "Parallel implementation of the OneMap R package for fast computation of F1 linkage maps in outcrossing species.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "bbrowser": {
        "Name": "BBrowser",
        "Description": "BBrowser allows biologists to handle scRNA-seq data without programming knowledge. Intuitive operation. Functions are updated. It has a new CITE-seq dashboard, a complete package for interactively exploring single-cell gene expression data in parallel with surface protein information.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "bets": {
        "Name": "BETS",
        "Description": "Bootstrap Elastic net regression from Time Series (BETS) is a statistical framework based on Granger causality for the recovery of a directed gene network from transcriptional time-series data. BETS uses elastic net regression and stability selection from bootstrapped samples to infer causal relationships among genes. BETS is highly parallelized, enabling efficient analysis of large transcriptional data sets.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "bigPint": {
        "Name": "bigPint",
        "Description": "Visualization methods for differential expression analysis | Methods for visualizing large multivariate datasets using static and interactive scatterplot matrices, parallel coordinate plots, volcano plots, and litre plots. Includes examples for visualizing RNA-sequencing datasets and differentially expressed genes | Big multivariate data plotted interactively | bigPint: Make BIG data pint-sized | Welcome to the bigPint package website! If you are a new user, please begin by reading from the Get Started tab at the top of this website. There are ten short vignette articles in that tab, and we recommend reading them in order. These short vignette articles consist of reproducible code that provide: | Alternatives to data metrics object | Clustering, DataImport, DifferentialExpression, GeneExpression, MultipleComparison, Normalization, Preprocessing, QualityControl, RNASeq, Sequencing, Software, Transcription, Visualization",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "bigseqkit": {
        "Name": "BigSeqKit",
        "Description": "The Next Generation Sequencing (NGS) raw data are stored in FASTA and FASTQ text-based file formats. Common operations on FASTA/Q files include searching, filtering, sampling, deduplication and sorting, among others. We can find several tools in the literature for FASTA/Q file manipulation but none of them are well fitted for large files of tens of GB (likely TBs in the near future) since mostly they are based on sequential processing. The exception is seqkit that allows some routines to use a few threads but, in any case, the scalability is very limited. To deal with this issue, we introduce BigSeqKit, a parallel toolkit to manipulate FASTA/Q files at scale with speed and scalability at its core. BigSeqKit takes advantage of an HPC-Big Data framework (IgnisHPC) to parallelize and optimize the commands included in seqkit. In this way, in most cases it is from tens to hundreds of times faster than other state-of-the-art tools such as seqkit, samtools and pyfastx.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "biochemddi": {
        "Name": "BioChemDDI",
        "Description": "BioChemDDI web server is constructed to predict drug-drug interactions, which is compatible with most major browsers, and the parallel speed-up is implemented.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "biocparallel": {
        "Name": "BiocParallel",
        "Description": "This package provides modified versions and novel implementation of functions for parallel evaluation, tailored to use with BioConductor objects.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "biogridrunner": {
        "Name": "BioGridRunner",
        "Description": "BioGridRunner is a distributed computing application for bioinformatics, incorporating directory services (data and software), grid computing methods (security, authentication, data transport and remote jobs), and gene sequence and genomic data processing methods.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "biosigner": {
        "Name": "biosigner",
        "Description": "This package implements a new method to assess the relevance of the variables for the prediction performances of the classifier. The approach can be run in parallel with the PLS-DA, Random Forest, and SVM binary classifiers. The signatures and the corresponding 'restricted' models are returned, enabling future predictions on new datasets.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "bis-snp": {
        "Name": "Bis-SNP",
        "Description": "A package based on the Genome Analysis Toolkit map-reduce framework for genotyping in bisulfite treated massively parallel sequencing on Illumina platform. It uses bayesian inference with either manually specified or automatically estimated methylation probabilities of different cytosine context(not only CpG, CHH, CHG in Bisulfite-seq, but also GCH et.al. in other bisulfite treated sequencing) to determine genotypes and methylation levels simultaneously.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "bitpai": {
        "Name": "BitPAI",
        "Description": "A bit-parallel algorithm for general, integer-scoring global alignment. Integer-scoring schemes assign integer weights for match, mismatch and insertion/deletion. This method uses structural properties in the relationship between adjacent scores in the scoring matrix to construct classes of efficient algorithms, each designed for a particular set of weights.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "brian2genn": {
        "Name": "Brian2GeNN",
        "Description": "Brian2GeNN is a software package that enables the users to make use of GeNN GPU acceleration when developing their models in Brian, without requiring any technical knowledge about GPUs, C++ or GeNN. The new Brian2GeNN software uses a pipeline of code generation to translate Brian scripts into C++ code that can be used as input to GeNN, and subsequently can be run on suitable NVIDIA GPU accelerators.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "bsf-skeleton": {
        "Name": "BSF-skeleton",
        "Description": "A template for parallelization of iterative numerical algorithms on cluster computing systems.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "cate": {
        "Name": "CATE",
        "Description": "A fast and scalable CUDA implementation to conduct highly parallelized evolutionary tests on large scale genomic data.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "catsnap": {
        "Name": "Catsnap",
        "Description": "A user-friendly algorithm for determining the conservation of protein variants reveals extensive parallelisms in the evolution of alternative splicing.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "cbmos": {
        "Name": "CBMOS",
        "Description": "CBMOS is a GPU-enabled Python framework for the numerical study of center-based models. Cell-based models are becoming increasingly popular for applications in developmental biology.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "cbptools": {
        "Name": "CBPtools",
        "Description": "CBPtools is a Python package that allows users to run an extensively evaluated rCBP analysis workflow on a given ROI. It currently supports two modalities: resting-state functional connectivity and structural connectivity based on diffusion-weighted imaging, along with support for custom connectivity matrices. Analysis parameters are customizable and the workflow can be scaled to a large number of subjects using a parallel processing environment. Parcellation results with corresponding validity metrics are provided as textual and graphical output. Regional connectivity-based parcellation (rCBP) is a widely used procedure for investigating the structural and functional differentiation within a region of interest (ROI) based on its long-range connectivity.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "cbs_das_protein_viewer": {
        "Name": "CBS DAS protein viewer",
        "Description": "Protein viewer which uses the distributed annotation system (DAS) to integrate and present annotation data from multiple sources for a protein sequence.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "cellosaurus": {
        "Name": "Cellosaurus",
        "Description": "A knowledge resource on cell lines. It attempts to describe all cell lines used in biomedical research, including immortalized cell lines, naturally immortal cell lines (ie stem cell lines), finite life cell lines when those are distributed and widely used, vertebrate cell lines with an emphasis on human, mouse and rat cell lines, and invertebrate (insect and tick) cell lines.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "chemos": {
        "Name": "ChemOS",
        "Description": "ChemOS is a portable, modular and versatile software package, which supplies the structured layers indis-pensable for operating autonomous laboratories.  Additionally, it enables remote control oflaboratories, provides access to distributed computing resources, and comprises state-of-the-art machine learning methods.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "chinook": {
        "Name": "Chinook",
        "Description": "Chinook is a peer-to-peer (P2P) service for the discovery, use and assessment of bioinformatics programs. Chinook Online allows researchers to connect and run distributed bioinformatics programs using a web application.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "chromscape": {
        "Name": "ChromSCape",
        "Description": "ChromSCape is a user-friendly interactive Shiny/R application distributed as a Bioconductor package, that processes single-cell epigenomic data to assist the biological interpretation of chromatin landscapes within cell populations. ChromSCape analyses the distribution of repressive and active histone modifications as well as chromatin accessibility landscapes from single-cell datasets.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "circvar-db": {
        "Name": "circVAR database",
        "Description": "circVAR database is genome-wide archive of genetic variants for human circular RNAs\nCircular RNAs (circRNAs), the 3' and 5' ends of which are covalently linked, are a kind of widely distributed and abundant RNAs found in eukaryotic organisms in recent years. They could play as sponges for regulating microRNAs and RNA binding proteins. Our circVAR database aims to provide resources for circRNA-related genetic variants in healthy and diseased populations.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "clij-assistant": {
        "Name": "CLIJ-assistant",
        "Description": "CLIJx-Assistant is an intuitive user interface for building custom GPU-accelerated image processing workflows using CLIJ2 in Fiji. It visualizes workflows as image date flow graphs while building them. It suggests what to do next and generates scripts and human readable protocols to facilitate reproducible bio-image analysis.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "cloudburst": {
        "Name": "CloudBurst",
        "Description": "CloudBurst is a parallel read-mapping algorithm optimized for mapping next-generation sequence data to the human genome and other reference genomes.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "clustersheep": {
        "Name": "ClusterSheep",
        "Description": "ClusterSheep is a GPU/CUDA-accelerated software tool for large-scale clustering of tandem mass spectra from shotgun proteomics.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "cmfsm": {
        "Name": "cmFSM",
        "Description": "A parallel acceleration tool for classical frequent subgraph mining algorithm. The core idea is to employ parallel techniques to parallelize extension tasks, so as to reduce computation time. On the other hand, multi-node strategy is employed to solve the problem of memory constraints.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "cmsa": {
        "Name": "CMSA",
        "Description": "A heterogeneous CPU/GPU computing system for multiple similar RNA/DNA sequence alignment.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "cogstack": {
        "Name": "CogStack",
        "Description": "Experiences of deploying integrated information retrieval and extraction services in a large National Health Service Foundation Trust hospital.\n\nCogStack is a lightweight distributed, fault tolerant database processing architecture and ecosystem, intended to make NLP processing and preprocessing easier in resource constrained environments.\n\nCogStack is a lightweight distributed, fault tolerant database processing architecture, intended to make NLP processing and preprocessing easier in resource constained environments.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "coils": {
        "Name": "COILS",
        "Description": "Program that compares a sequence to a database of known parallel two-stranded coiled-coils and derives a similarity score. By comparing this score to the distribution of scores in globular and coiled-coil proteins, the program then calculates the probability that the sequence will adopt a coiled-coil conformation.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "comer2": {
        "Name": "COMER2",
        "Description": "GPU-accelerated sensitive and specific homology searches.\n\nCOMER2, cross-platform software for protein remote homology search.\n\nThe COMER method based on sequence profile-profile comparison is one of the most sensitive and accurate computational tools developed for protein alignment and homology search. COMER version 2.1 (COMER2) represents one of the fastest implementations of calculations for sensitive protein homology search. High COMER2 performance is achieved by harnessing the power of the Graphics processing unit (GPU). Hence, a GPU is expected to be installed on the system.\n\nCOMER2, a cross-platform software package for protein remote homology search and alignment.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "compact": {
        "Name": "CompaCt",
        "Description": "CompaCt performs automated integrative comparative analysis of large-scale (protein) interaction datasets, identifying groups of interactors (e.g., protein complexes) in parallel in multiple species, allowing systematic identification and comparison of conserved as well as taxon-specific components of protein complexes and other interactions.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "compss": {
        "Name": "COMPSs",
        "Description": "Sequential programming model\nGeneral purpose programming languages + annotations/hints\nExploitation of implicit parallelism\nAutomatic on-the-fly creation of a \ntask dependency graph\nCOMPSs workflows portable to HPC and Cloud without change",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "consort-nlp": {
        "Name": "CONSORT-NLP",
        "Description": "CONSORT-NLP is an Artificial Intelligence Tool with Automated Reporting Checklist Generation for Parallel Group Randomized Trials.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "coolpuppy": {
        "Name": "coolpup.py",
        "Description": "coolpup.py is a versatile Python tool to perform pile-up analysis on Hi-C data in .cool format. coolpup.py is particularly well suited performance-wise for analysing huge numbers of potential interactions, since it loads whole chromosomes into memory one by one (or in parallel to speed it up) to extract small submatrices quickly. Having to read everything into memory makes it relatively slow for small numbers of loops, but performance doesn't decrease until you reach a huge number of interactions.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "copywriter": {
        "Name": "CopywriteR",
        "Description": "This tool extracts DNA copy number information from targeted sequencing by utilizing off-target reads. It allows extracting uniformly distributed copy number information, and it can be applied to sequencing data obtained from various techniques including chromatin immunoprecipitation and target enrichment on small gene panels. Thereby, this tool constitutes a widely applicable alternative to available copy number detection tools.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "corenup": {
        "Name": "CORENup",
        "Description": "CORENup is a deep learning model for nucleosome identification. CORENup processes a DNA sequence as input using one-hot representation and combines in a parallel fashion a fully convolutional neural network and a recurrent layer. These two parallel levels are devoted to catching both non periodic and periodic DNA string features.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "cosinor": {
        "Name": "cosinor",
        "Description": "Time distributed data analysis by Cosinor.Online application.\n\nTools for estimating and predicting the cosinor model version 1.1 from CRAN.\n\ncosinor is a set of simple functions that transforms longitudinal data to estimate the cosinor linear model as described in Tong (1976). Methods are given to summarize the mean, amplitude and acrophase, to predict the mean annual outcome value, and to test the coefficients.\n\ncosinor: Tools for estimating and predicting the cosinor model.\n\nMolcan, L. (2019). Time distributed data analysis by Cosinor. Online application. bioRxiv, 805960.\n\nPreprint available at BioRxiv, doi: https://doi.org/10.1101/805960.\n\nCosinor Online, cosinor on the web, is a simple web-based application evaluating the presence of 24-h periodicity. Loading data is easy, just copy and paste columns from an Excel file to the web form",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "coverageview": {
        "Name": "CoverageView",
        "Description": "This package provides a framework for the visualization of genome coverage profiles. It can be used for ChIP-seq experiments, but it can be also used for genome-wide nucleosome positioning experiments or other experiment types where it is important to have a framework in order to inspect how the coverage distributed across the genome.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "covidmulti-net": {
        "Name": "CovidMulti-Net",
        "Description": "CovidMulti-Net is a parallel-dilated multi-scale feature fusion architecture for the identification of COVID-19 cases from chest X-ray Images.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "cpmd": {
        "Name": "CPMD",
        "Description": "Parallelized plane wave/pseudopotential implementation of Density Functional Theory, particularly designed for ab-initio molecular dynamics.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "crimson": {
        "Name": "CRIMSON",
        "Description": "Crimson (cardiovascular integrated modelling & simulation) is an advanced simulation environment for subject-specific hemodynamic analysis. Crimson is integrating best-in-class open source-solutions for parallel flow solvers, fluid-structure interactions, GUI-based boundary condition specification, data assimilation, medical image processing, mesh generation, transitional hemodynamics, and much more.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "croco-xlink": {
        "Name": "CroCo",
        "Description": "A a user-centred tool to convert results from crosslinking mass spectrometry experiments | Convert data formats from chemical cross-linking mass spectrometry (XL-MS) | The CroCo cross-link converter \u2014 CroCo documentation | CroCo converts multiple data format from cross-linking mass spectrometry software tools to xTable format (in csv format) | The CroCo cross-link converter \u00b6 | CroCo converts multiple data format from cross-linking mass spectrometry software tools to xTable format (in csv format). It is distributed as graphical programme to be run from an executable and as a Python module to be integrated into workflows",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "crtpowerdist": {
        "Name": "CRTpowerdist",
        "Description": "An R package to calculate attained power and construct the power distribution for cross-sectional stepped-wedge and parallel cluster randomized trials.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "ct-projection-simulator": {
        "Name": "CT Projection Simulator",
        "Description": "CT Projection Simulator computes the parallel beam 2D projections of an object specified as a set of geometric shapes.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "cuda-ec": {
        "Name": "CUDA-EC",
        "Description": "A scalable parallel algorithm for correcting sequencing errors in high-throughput short-read data so that error-free reads can be available before DNA fragment assembly.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "cuda-meme": {
        "Name": "CUDA-MEME",
        "Description": "Motif discovery software based on MEME algorithm for a single GPU device using CUDA programming model. At present, it only supports the OOPS and ZOOPS models.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "cudammc": {
        "Name": "cudaMMC",
        "Description": "GPU-enhanced multiscale Monte Carlo chromatin 3d modelling.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "cudasw": {
        "Name": "CUDASW++",
        "Description": "CUDASW++ (compute unified device architecture) is a bioinformatics software for Smith-Waterman protein database searches that takes advantage of the massively parallel CUDA architecture of NVIDIA Tesla GPUs to perform fast sequence searches.",
        "parallel": true,
        "distributed": false,
        "GPU": true
    },
    "cufluxsampler.jl": {
        "Name": "CuFluxSampler.jl",
        "Description": "GPU-accelerated algorithms for flux sampling in CUDA.jl, working with COBREXA.jl",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "cuneiform": {
        "Name": "Cuneiform",
        "Description": "A functional scientific workflow language for large scale scientific data analysis. It enables integration of external tools written in any language and automatic parallelization of execution.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "curc": {
        "Name": "CURC",
        "Description": "A GPU-accelerated reference-free compressor for high-throughput sequencing reads of FASTQ files.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "cuttlefish": {
        "Name": "Cuttlefish",
        "Description": "Cuttlefish is a fast, parallel, and very lightweight memory tool to construct the compacted de Bruijn graph from genome reference(s). Cuttlefish is a tool for constructing the (colored) compacted de Bruijn graph from a collection of one or more genome references. Cuttlefish introduces a novel modeling scheme of the de Bruijn graph vertices as finite-state automata, and constrains the state-space for the automata to enable tracking of their transitioning states with very low memory usage. Cuttlefish is also fast and highly parallelizable. Experimental results demonstrate that the algorithm scales much better than existing approaches, especially as the number and scale of the input references grow.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "d-ee": {
        "Name": "D-EE",
        "Description": "D-EE is a distributed dimensionality reduction and visualization tool. Its distributed storage and distributed computation technique allow us to efficiently analyze large-scale single-cell data at the cost of constant time speedup.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "dasmiweb": {
        "Name": "DASMIweb",
        "Description": "Server that allows integration, analysis and quantitative assessment of distributed sources of protein and domain interactions. Users can query numerous sources simultaneously, which can then be configured and can support incorporation of user data.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "datma": {
        "Name": "DATMA",
        "Description": "DATMA (Distributed AuTomatic Metagenomic Assembly and annotation framework) is a distributed automatic pipeline for fast metagenomic analysis that includes: sequencing quality control, 16S-identification, reads binning, de novo assembly, ORF detection and taxonomic annotation.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "dax": {
        "Name": "DAX",
        "Description": "Distributed automation for XNAT toolkit (DAX) provides large-scale image storage and analysis pipelines with an optimized job management tool for neuroimaging data",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "dbgenvoc": {
        "Name": "dbGENVOC",
        "Description": "dbGENVOC, a comprehensive, flexible database framework, developed with an aim to allow potential users to access, query, browse and download clinically relevant somatic and germline variation data from Indian oral cancer patients. This database will store variant calls from various studies that uses massively parallel sequencing to generate genome-scale data.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "ddcmd": {
        "Name": "ddcMD",
        "Description": "ddcMD is a fully GPU-accelerated molecular dynamics program for the Martini force field.\nddcMDconverter should be used to convert GROMACS inputs to ddcMD inputs",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "ddecon": {
        "Name": "DDecon",
        "Description": "DDecon is an ImageJ plug-in that provides a GUI for super-resolution measurement of thin filament lengths by applying distributed deconvolution analysis to periodic line scans collected from fluorescence images.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "ddrp": {
        "Name": "DDRP",
        "Description": "DDRP is a platform for real-time phenology and climatic suitability modeling of invasive insects.\n\nA final production version of the DDRP platform that includes cohorts, parallel processing, and improving mapping routines. The objective of the Degree-Day, establishment Risk, and Pest event mapping system (DDRP) is to predict phenology and climate suitability of invasive, biocontrol, and IPM species for the conterminous United States. DDRP is \u2026.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "decgpu": {
        "Name": "DecGPU",
        "Description": "Parallel and distributed error correction algorithm for high-throughput short reads.",
        "parallel": true,
        "distributed": true,
        "GPU": false
    },
    "deep-framework": {
        "Name": "Deep-Framework",
        "Description": "A Distributed, Scalable, and Edge-Oriented Framework for Real-Time Analysis of Video Streams.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "deepac4c": {
        "Name": "DeepAc4C",
        "Description": "A convolutional neural network model with hybrid features composed of physicochemical patterns and distributed representation information for identification of N4-acetylcytidine in mRNA.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "deepnog": {
        "Name": "DeepNOG",
        "Description": "DeepNOG is a tool for protein orthologous groups assignment. Assign proteins to orthologous groups (eggNOG 5) on CPUs or GPUs with deep networks. DeepNOG is much faster than alignment-based methods, providing accuracy similar to HMMER.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "deeptrio": {
        "Name": "DeepTrio",
        "Description": "A ternary prediction system for protein-protein interaction using mask multiple parallel convolutional neural networks.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "delly2": {
        "Name": "Delly2",
        "Description": "Integrated structural variant prediction method that can discover, genotype and visualize deletions, tandem duplications, inversions and translocations at single-nucleotide resolution in short-read massively parallel sequencing data. It uses paired-ends and split-reads to sensitively and accurately delineate genomic rearrangements throughout the genome. Structural variants can be visualized using Delly-maze and Delly-suave.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "denodo": {
        "Name": "Denodo",
        "Description": "For every organization data and its related infrastructure is constantly evolving. As a result, enterprise data will always remain distributed. The Denodo Platform gives IT organizations the flexibility to evolve their data strategies, migrating to the cloud, or logically unifying data warehouses and data lakes, without affecting business. The Denodo Platform also accelerates data provisioning through reduced data replication, it enables consistent security and governance across multiple systems, and it gives your business users the flexibility to choose their preferred applications. The only way you can accomplish this is through a logical data fabric powered by data virtualization. The Denodo Platform is the only solution that can meet this need. Read about the benefits of the Denodo Platform in this Forrester TEI Repo",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "denoisem": {
        "Name": "DenoisEM",
        "Description": "An interactive ImageJ plugin for semi-automated image denoising in electron microscopy.\n\nDenoisEM offers several state-of-the-art denoising and deconvolution algorithms such as non-local means, BLS-GSM, Tikhonov deconvolution, etc.\n\nFast and advanced image denoising of large-scale 3D electron microscopy data.\n\nDenoisEM is developed by the TELIN department at Ghent University and the Bio Informatics Core at VIB . The GPU backbone is driven by Quasar , an in-house programming language of the TELIN department.\n\nApache/2.4.7 (Ubuntu) Server at bioimagingcore.be Port 443.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "desmond": {
        "Name": "Desmond",
        "Description": "Desmond is a software package developed at D. E. Shaw Research to perform high-speed molecular dynamics simulations of biological systems on conventional commodity clusters. The code uses novel parallel algorithms and numerical techniques to achieve high performance and accuracy on platforms containing a large number of processors, but may also be executed on a single computer.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "diamin": {
        "Name": "DIAMIN",
        "Description": "DIAMIN is a high-level software library to facilitate the development of distributed applications for the efficient analysis of large-scale molecular interaction networks.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "diapasef": {
        "Name": "diaPASEF",
        "Description": "diaPASEF is an appproch for parallel accumulation-serial fragmentation combined with data-independent acquisition.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "distmap": {
        "Name": "distmap",
        "Description": "A Toolkit for Distributed Short Read Mapping on a Hadoop Cluster.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "distributeddata.jl": {
        "Name": "DistributedData.jl",
        "Description": "Utility functions library for manipulating massive datasets within a distributed environment, using Julia.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "dlagm": {
        "Name": "dLagM",
        "Description": "dLagM is a R package for distributed lag models and autoregressive distributed lag (ARDL) bounds testing to explore the short and long-run relationships between dependent and independent time series. Distributed lag models constitute a large class of time series regression models including the ARDL models used for cointegration analysis.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "dpn-sa": {
        "Name": "DPN-SA",
        "Description": "The deep propensity network using a sparse autoencoder (DPN-SA) is a deep learning architecture for propensity score matching and counterfactual prediction to tackle the problems of high dimensionality, nonlinear/nonparallel treatment assignment, and residual confounding when estimating treatment effects.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "easyamber": {
        "Name": "easyAmber",
        "Description": "EasyAmber is a comprehensive toolbox to automate the molecular dynamics simulation of proteins. EasyAmber is a set of wrapper scripts to automate the molecular dynamics routines implemented in the Amber package. The toolbox can address a wide set of tasks in computational biology struggling to account for protein flexibility, and supports the full-atom model building, optimization/equilibration of the molecular system, classical/conventional and accelerated molecular dynamics simulations. The easyAmber software takes the molecular dynamics to the next level in terms of usability for complex processing of large volumes of data. It implements advanced MD protocols, but is highly automated and easy-to-operate to attract a broad audience. The toolbox can be used on a personal desktop station equipped with a gaming GPU-accelerator, as well as help to manage huge workloads on a powerful supercomputer.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "easyparallel": {
        "Name": "EasyParallel",
        "Description": "EasyParallel is a free GUI cross-platform tool that utilizes a multi-thread parallel algorithm for processing multiple iterations of STRUCTURE and NEWHYBRIDS analyses.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "ecccl": {
        "Name": "eccCL",
        "Description": "Parallelized GPU implementation of Ensemble Classifier Chains.",
        "parallel": true,
        "distributed": false,
        "GPU": true
    },
    "eccparacorp": {
        "Name": "ECCParaCorp",
        "Description": "English-Chinese Cancer Parallel Corpus (ECParaCorp) is a database involving information of 6 cancers in three different themes: cancer prevention, cancer screening and cancer treatment.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "ego_viii": {
        "Name": "EGO VIII",
        "Description": "EGO is a program to perform molecular dynamics simulations on parallel as well as on sequential computers. Supported parallel machines include the Hitachi SR8000, CRAY-T3E, IBM-SP2, Fujitsu VPP700, Parsytec-CC under PARIX and inhomogeneous clusters of UNIX workstations under PVM or MPI.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "elssi": {
        "Name": "ELSSI",
        "Description": "Ensemble learning-based approach (ELSSI): parallel SNP-SNP interactions detection by ensemble multi-type detectors.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "enly": {
        "Name": "Enly",
        "Description": "Simple tool based on the iterative mapping of sequence reads at contig edges, capable to extend the genomic contigs deriving from high-throughput sequencing, especially those deriving by Newbler-like assemblies. Testing it on a set of de novo draft genomes led to the closure of up to 20% of the gaps originally present. It is cross-platform and most of the steps of its pipeline are parallelizable, making easy and fast to improve a draft genome resulting from a de novo assembly.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "epacts": {
        "Name": "EPACTS",
        "Description": "EPACTS (Efficient and Parallelizable Association Container Toolbox) is a versatile software pipeline to perform various statistical tests for identifying genome-wide association from sequence data through a user-friendly interface, both to scientific analysts and to method developers.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "f5c": {
        "Name": "f5c",
        "Description": "GPU Accelerated Adaptive Banded Event Alignment for Rapid Comparative Nanopore Signal Analysis | Re-engineered and optimised Nanopolish call-methylation module (supports CUDA acceleration) | An optimised re-implementation of the call-methylation module in Nanopolish. Given a set of basecalled Nanopore reads and the raw signals, f5c detects the methylated cytosine bases. f5c can optionally utilise NVIDIA graphics cards for acceleration",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "fastbma": {
        "Name": "fastBMA",
        "Description": "Fast, scalable, parallel and distributed inference of very large networks by Bayesian Model Averaging.",
        "parallel": true,
        "distributed": true,
        "GPU": false
    },
    "fastme": {
        "Name": "FastME",
        "Description": "Distance algorithms to infer phylogenies. It's based on balanced minimum evolution, which is the very principle of NJ. It includes Nearest Neighbor Interchange (NNI) and also Subtree Pruning and Regrafting (SPR), while remaining as fast as NJ and providing a number of facilities: distance estimation for DNA and proteins with various models and options, bootstrapping, and parallel computations.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "fits": {
        "Name": "FITs",
        "Description": "Forest of imputation trees for recovering true signals in single-cell open chromatin profiles.\n\nForest of Imputation Trees (FITs) is a method to impute highly sparse and noisy data-sets from single cell epigenome profiling.\n\nFITs work in two phases. It has been made so to handle very large read-count matrixes and to get better imputation. In phase-1 it builds multiple imputation trees and from every tree it extracts 1 or 2 imputed version of original read-count matrix. One can run phase-1 of FITs in parallel processing mode also, where multiple trees can be build using several processors.After phase-1, the phase-2 part of FITs is used to accumulate the most relevant imputed version for every cell.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "flexbar": {
        "Name": "Flexbar",
        "Description": "Flexible barcode and adapter removal. It demultiplexes barcoded runs and removes adapter sequences. Several adapter removal presets for Illumina libraries are included. Computes exact overlap alignments using SIMD and multicore parallelism. Moreover, trimming and filtering features are provided. It increases read mapping rates and improves genome as well as transcriptome assemblies.  The software supports data in fasta and fastq format from multiple sequencing platforms.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "flexgsea": {
        "Name": "flexgsea",
        "Description": "Flexgsea is a R package to do gene set enrichment analysis. Main advantages:\n  - Allows user defined functions. So you can use you favorite package for differential expression.\n  - Calculates significance by sample permutation.\n  - Parallelization using the foreach package.\n  - Can calculate normalized enrichment scores (NES) and significance based on that.\n  - Implements the weighted KS-like statistic.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "forecast_mpra": {
        "Name": "FORECAST",
        "Description": "Effective design and inference for cell sorting and sequencing based massively parallel reporter assays.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "fret-ibra": {
        "Name": "FRET-IBRA",
        "Description": "FRET - IBRA is a fully parallelized toolkit to process fluorescence resonance energy transfer (FRET) intensity data to produce ratiometric images with low measurement bias.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "g-blastn": {
        "Name": "G-BLASTN",
        "Description": "GPU-accelerated nucleotide alignment tool based on the widely used NCBI-BLAST.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "gads": {
        "Name": "GADS",
        "Description": "Software for Genetic Analyses of quantitative traits Distributed with Spike on the base of large pedigrees without loops.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "gage": {
        "Name": "gage",
        "Description": "Published method for gene set (enrichment or GSEA) or pathway analysis. It is generally applicable independent of microarray or RNA-Seq data attributes and consistently achieves superior performance over other frequently used methods. We provide functions for basic analysis, result processing and presentation. We have also built pipeline routines for of multiple analyses in a batch, comparison between parallel analyses, and combined analysis of heterogeneous data from different sources/studies.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "galaxytrakr": {
        "Name": "GalaxyTrakr",
        "Description": "A distributed analysis tool for public health whole genome sequence data accessible to non-bioinformaticians.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "game": {
        "Name": "G.A.M.E",
        "Description": "GPU-Accelerated Mixture Elucidator. GPU acceleration is useful in solving complex chemical information problems. Identifying unknown structures from the mass spectra of natural product mixtures has been a desirable yet unresolved issue in metabolomics.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "gbc": {
        "Name": "GBC",
        "Description": "Parallel toolkit based on highly addressable byte-encoding blocks for extremely large-scale genotypes of species.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "gcup": {
        "Name": "gCUP",
        "Description": "Rapid GPU-based HIV-1 coreceptor usage prediction for next-generation sequencing.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "gdasc": {
        "Name": "GDASC",
        "Description": "A GPU parallel-based web server for detecting hidden batch factors.\n\nIt provides a user-friendly web interface for detecting batch factors. Based on the good performance of DASC algorithm, it is able to give the most accurate results. For two steps of DASC, data-adaptive shrinkage and semi-non-negative matrix factorization, we designed parallelization strategies facing convex clustering solution and decomposition process. It runs more than 50 times faster than the original version on the representative RNA sequencing quality control dataset. With its accuracy and high speed, this server will be a useful tool for batch effects analysis.",
        "parallel": true,
        "distributed": false,
        "GPU": true
    },
    "genogam": {
        "Name": "GenoGAM",
        "Description": "This package allows statistical analysis of genome-wide data with smooth functions using generalized additive models based on the implementation from the R-package 'mgcv'. It provides methods for the statistical analysis of ChIP-Seq data including inference of protein occupancy, and pointwise and region-wise differential analysis. Scaling of generalized additive model fitting to whole chromosomes is achieved by parallelization over overlapping genomic intervals.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "genomicfiles": {
        "Name": "GenomicFiles",
        "Description": "This package provides infrastructure for parallel computations distributed 'by file' or 'by range'. User defined MAPPER and REDUCER functions provide added flexibility for data combination and manipulation.",
        "parallel": true,
        "distributed": true,
        "GPU": false
    },
    "genomix": {
        "Name": "Genomix",
        "Description": "Parallel genome assembly system built from the ground up with scalability in mind. It can assemble large and high-coverage genomes from fastq files in a short time and produces assemblies similar to Velvet or Ray in quality.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "gpgpufragfold": {
        "Name": "GPGPUFRAGFOLD",
        "Description": "GPUFRAGFOLD is a CUDA ACELERATED Protein Structure Prediction Tool.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "gpmeta": {
        "Name": "GPMeta",
        "Description": "GPU-accelerated method for ultrarapid pathogen identification from metagenomic sequences.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "gprimer": {
        "Name": "GPrimer",
        "Description": "GPrimer is a fast GPU-based pipeline for primerdesign for qPCR experiments.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "gpu-bsm": {
        "Name": "GPU-BSM",
        "Description": "GPU-BSM (GPU-BiSulfite reads Mapping) is a GPU-based tool devised to map bisulfite-treated reads.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "gpu-cassert": {
        "Name": "GPU-CASSERT",
        "Description": "The GPU-based implementation of the CASSERT algorithm for protein 3D structure similarity searching. The algorithm is based on the two-phase alignment of protein structures when matching fragments of compared proteins.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "gpu-darwin": {
        "Name": "GPU-Darwin",
        "Description": "GPU acceleration of Darwin read overlapper for de novo assembly of long DNA reads.\n\nThis repository contains a GPU implementation of Darwin [1][2], a hardware-friendly DNA aligner.\n\nIt consists of two parts: D-SOFT and GACT, which represent typical seed-and-extend methods. D-SOFT (Diagonal-band based Seed Overlapping based Filtration Technique) filters the search space by counting non-overlapping bases in matching Kmers in a band of diagonals. GACT (Genomic Alignment using Constant Tracebackmemory) can align reads of arbitrary length using constant memory for the compute-intensive step.\n\nThis implementation can be used to run on CPU only, or use the GPU-accelerated version. For more choices between individual optimizations, go back to commit e472745e.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "gpu-i-tasser": {
        "Name": "GPU-I-TASSER",
        "Description": "A GPU accelerated I-TASSER protein structure prediction tool.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "gpu3snp": {
        "Name": "GPU3SNP",
        "Description": "GPU3SNP is a multi-GPU tool that exhaustively analyzes case-control datasets looking for 3-SNP combinations that present epistatic interaction.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "gpu_mrbayes": {
        "Name": "GPU MrBayes",
        "Description": "Modified version of MrBayes that enables running Metropolis coupled \u00a0Markov \u00a0chain Monte Carlo (MC)^3 sampling on a Graphics Processing Unit (GPU).",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "gpufit": {
        "Name": "Gpufit",
        "Description": "A GPU-accelerated CUDA implementation of the Levenberg-Marquardt algorithm. It was developed to meet the need for a high performance, general- purpose nonlinear curve fitting software library which is publicly available and open source.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "gpumotif": {
        "Name": "GPUmotif",
        "Description": "GPU-accelerated ultra-fast and energy-efficient motif analysis program.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "grasviq": {
        "Name": "grasviq",
        "Description": "Grass Vein Image Quantification (GrasVIQ) is an image analysis framework for automatic quantification of veins in grass leaves. Designed specifically for parallel venation, GrasVIQ automatically segments veins from images of cleared grass leaves using thresholding and edge detection techniques. Veins are quantified and classified into orders, and spatial parameters, such as vein width and interveinal distance, are calculated automatically. For more details, please see.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "grenits": {
        "Name": "GRENITS",
        "Description": "The package offers four network inference statistical models using Dynamic Bayesian Networks and Gibbs Variable Selection: a linear interaction model, two linear interaction models with added experimental noise (Gaussian and Student distributed) for the case where replicates are available and a non-linear interaction model.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "gseabenchmarker": {
        "Name": "GSEABenchmarkeR",
        "Description": "It implements an extendable framework for reproducible evaluation of set- and network-based methods for enrichment analysis of gene expression data. This includes support for the efficient execution of these methods on comprehensive real data compendia using parallel computation on standard workstations and institutional computer grids.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "h-blast": {
        "Name": "H-BLAST",
        "Description": "Fast protein sequence alignment toolkit on heterogeneous computers with GPUs .",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "hadoop": {
        "Name": "Apache Hadoop",
        "Description": "The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "hermes": {
        "Name": "Hermes",
        "Description": "Hermes introduces a new \u201cdescribe once, run anywhere\u201d paradigm for the execution of bioinformatics workflows in hybrid cloud environments. It combines the traditional features of parallelization-enabled workflow management systems and of distributed computing platforms in a container-based approach.",
        "parallel": true,
        "distributed": true,
        "GPU": false
    },
    "hisif": {
        "Name": "HiSIF",
        "Description": "Modeling and analysis of Hi-C data by HiSIF identifies characteristic promoter-distal loops.\n\nHiSIF - HiC Significant Interacting Fragments.\n\nHiSIF was implemented by using C++/C with parallel processing being written in C. It has been compiled and run exclusively on Linux operating systems. This tool only requires the g++ compiler and a reference genome for HG19. Standalone CERN ROOT C++ framework is used to extract fit parameters of the CTS interactions. A small C tool is provided to process the initial data from NCBI. HiSIF supports all of the available three Hi-C protocols (Hi-C, TCC and in situ Hi-C). The tool is named from the fact that it is designed to find the significant interactions from a given sample of Hi-C reads.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "hpc-reditools": {
        "Name": "HPC-REDItools",
        "Description": "A Novel HPC-aware Tool for Improved Large Scale RNA-editing Analysis.\n\nREDItools2 is the optimized, parallel multi-node version of REDItools.\n\nREDItools takes in input a RNA-Seq (or DNA-Seq BAM) file and outputs a table of RNA-Seq editing events. Here is an example of REDItools's output:.\n\nThe following image explains the high-level architecture.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "hpg-dhunter": {
        "Name": "HPG-DHunter",
        "Description": "An ultrafast, friendly tool for DMR detection and visualization.\n\nIf you want to use this tool just now, there is an executable file for Linux x86_64 systems. This compressed file is available at releases page. But, previously, CUDA and Nvidia drivers must be installed in your system. For that, you can go to System requirements section.\n\nHPG-Dhunter is an interactive tool for detecting Differentially Methylathed Regions (DMRs) and visualizing DNA methylation signals. It is based on building a methylation signal from the information yielded by HPG-HMapper, and using a NVidia GPU and the CUDA programming model to compute the Discrete Wavelet Transform (DWT) of this methylation signal. The transformation of the signals in turn allows the comparison of different signals at low resolution levels, easily identifying DMRs with a low workload, when compared to other strategies. HPG-Dhunter is part of the HPG-MSuite.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "hpg_pore": {
        "Name": "HPG pore",
        "Description": "Toolkit to explore and analyze nanopore sequencing data that can run both on a single computer and on the Hadoop distributed computing framework.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "hpul": {
        "Name": "Hpul",
        "Description": "Usage of the Sea Urchin Hemicentrotus pulcherrimus Database, HpBase.\n\nHemicentrotus pulcherrimus Genome Resources.\n\nHemicentrotus pulcherrimus Genome and Transcriptome database.\n\nHemicentrotus pulcherrimus (A. Agassiz, 1863) (Animaria: Echinodermata: Echinoidea: Echinoida: Strongylocentrotiae: Hemicentrotus) is the most widely distributed sea urchin in Japan and important marine food resources in eastern Asia. This species has a long history as a model organism in the field of developmental and cell biology since the mid 1900s. The web site provides information on H. pulcherrimus genome and transcriptome for a wide range of biologists.\n\n||| COMMON LINK WITH (PUB. & NAME DIFFERENT) bio.tools/insdc (NIG.AC.JP), bio.tools/dfast (NIG.AC.JP), bio.tools/ddbj (NIG.AC.JP), bio.tools/cibex (NIG.AC.JP)\n\n||| CORRECT NAME OF TOOL COULD ALSO BE 'H pulcherrimus'",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "hypercluster": {
        "Name": "hypercluster",
        "Description": "A python package and SnakeMake pipeline for flexible, parallelized unsupervised clustering optimization.\n\nA package for clustering optimization with sklearn.\n\nExample jupyter notebooks for hypercluster.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "iblp": {
        "Name": "iBLP",
        "Description": "An XGBoost-Based Predictor for Identifying Bioluminescent Proteins.\n\nThe role of this page is a brief introduction to iBLP.\n\n\nBioluminescent proteins are a class of proteins that widely distributed in many living organisms with various mechanisms of light emission including bioluminescence and chemiluminescence from luminous organisms.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "igemrna": {
        "Name": "IgemRNA",
        "Description": "IgemRNA is an open access toolbox for transcriptome data statistical and biochemical network topology-based analysis. IgemRNA was developed in the MATLAB environment in order to take advantage of the up-to-date and most commonly distributed GSM modelling tool Cobra Toolbox 3.0 and spreadsheet file capabilities.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "igneous": {
        "Name": "Igneous",
        "Description": "Distributed dense 3D segmentation meshing, neuron skeletonization, and hierarchical downsampling.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "imagehts": {
        "Name": "imageHTS",
        "Description": "R package dedicated to the analysis of high-throughput microscopy-based screens. The package provides a modular and extensible framework to segment cells, extract quantitative cell features, predict cell types and browse screen data through web interfaces. Designed to operate in distributed environments, it provides a standardized access to remote data and facilitates the dissemination of high-throughput microscopy-based datasets.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "irma": {
        "Name": "IRMa",
        "Description": "IRMa ( Interpr\u00e9tation des R\u00e9sultats Mascot)\u00a0\u00a0toolbox provides an interactive application to assist in the validation of Mascot\u00ae search results. IRMa reads MASCOT\u00ae result (using Matrix Science\u00ae Parser distributed free of charge) and automatically filters identified peptides. All relevant information is displayed in a structured manner, showing \u201cproteins hits\u201d details. User can then manually or automatically confirm or reject individual peptide spectrum matches.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "isac": {
        "Name": "ISAC",
        "Description": "Accelerated 2D Classification With ISAC Using GPUs.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "isgp-drlf": {
        "Name": "isGP-DRLF",
        "Description": "Identification of Sub-Golgi protein localization by use of deep representation learning features.\n\nif computing on a GPU, it would be fasster.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "j-modl": {
        "Name": "J-MoDL",
        "Description": "Joint Model-Based Deep Learning for Optimized Sampling and Reconstruction.\n\nJoint model-based deep learning for parallel imaging.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "jfuzzymachine": {
        "Name": "JFuzzyMachine",
        "Description": "A Multistaged Hyperparallel Optimization of the Fuzzy-Logic Mechanistic Model of Molecular Regulation.\n\nElucidating mechanistic relationships among intracellular macromolecules is fundamental to understanding the molecular basis of normal and diseased processes. The classical fuzzy logic approach employs varying degrees of truth to describe relationships between interacting molecules. jFuzzyMachine implements the fuzzy logic approach to elucidate mechanistic relationships among biological molecules and makes more readily available the fuzzy logic inference approach in a freely available tool.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "jind": {
        "Name": "JIND",
        "Description": "Joint Integration and Discrimination for Automated Single-Cell Annotation.\n\nJIND is a framework for automated cell-type identification based on neural networks. It directly learns a low-dimensional representation (latent code) inwhich cell-types can be reliably determined. To account for batch effects, JIND performs a novel asymmetric alignment in which the transcriptomic profileof unseen cells is mapped onto the previously learned latent space, hence avoiding the need of retraining the model whenever a new dataset becomes available. JIND also learns cell-type-specific confidence thresholds to identify and reject cells that cannot be reliably classified. We show on datasets with and without batch effects that JIND classifies cells more accurately than previously proposed methods while rejecting only a small proportion of cells. Moreover, JIND batch alignment is parallelizable, being more than five or six times faster than Seurat integration.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "kmerkeys": {
        "Name": "KmerKeys",
        "Description": "KmerKeys is a web resource for searching indexed genome assemblies and variants. It provides performant, rapid query speeds for cloud computation on genome assemblies. It enable fuzzy as well as exact k-mer-based searches of assemblies. To enable robust and speedy performance, the website implements cache-friendly hash tables, memory mapping and massive parallel processing. Our method employs a scalable and efficient data structure that can be used to jointly index and search a large collection of human genome assembly information. One can include variant databases and their associated metadata such as the gnomAD population variant catalog. This feature enables the incorporation of future genomic information into sequencing analysis.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "kmx": {
        "Name": "kmx",
        "Description": "Count the occurrences of k-mers in biological sequences (Fastq or Fasta formatted). It can then perform various analyses on the generated indices. The counting is resource intensive, but this tool can divide the computation up, so that it can be run as sequential parts (to fit in memory), in parallel (to exploit multiple CPUs), or distributed (to use a cluster of computers).",
        "parallel": true,
        "distributed": true,
        "GPU": false
    },
    "lara_2": {
        "Name": "LaRA 2",
        "Description": "Parallel and vectorized program for sequence-structure alignment of RNA sequences.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "ldkit": {
        "Name": "LDkit",
        "Description": "A parallel computing toolkit for linkage disequilibrium analysis.\n\nGUI package is under the GUI folder, please double-click the LDkit_GUI.jar to start.\n\nRun using Graphic User Interface (GUI).",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "liteqtl": {
        "Name": "LiteQTL",
        "Description": "LiteQTL is a package that runs whole genome QTL scans near real-time, utilizing the computation power of GPU.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "malacoda": {
        "Name": "malacoda",
        "Description": "Bayesian modelling of high-throughput sequencing assays with malacoda.\n\nThe goal of malacoda is to enable Bayesian analysis of high-throughput genomic assays like massively parallel reporter assays (MPRA) and CRISPR screens.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "malvirus": {
        "Name": "MALVIRUS",
        "Description": "MALVIRUS is a fast and accurate tool for genotyping haploid individuals that does not require to assemble the read nor mapping them to a reference genome. It is tailored to work with virological data and can genotype an individual directly from sequencing data in minutes.\n\nMALVIRUS is divided into two logically distinct steps: the creation of a variant catalog from a set of assemblies and the genotype calling. The first step is based on mafft and snp-sites, whereas the second step is based on KMC and MALVA.\n\nThe variant catalog can be built once and reused for genotyping multiple individuals.\n\nPlease see the website for additional details.\n\nMALVIRUS is distributed as a Docker image and is publicly available on GitHub and Docker Hub under the terms of the GNU General Public License version 3 or later. MALVIRUS was mainly developed and tested under Ubuntu GNU/Linux version 18.04 but works wherever Docker is available.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "mappertrac": {
        "Name": "MaPPeRTrac",
        "Description": "A Massively Parallel, Portable, and Reproducible Tractography Pipeline.\n\nMassively Parallel, Portable, and Reproducible Tractography (MaPPeRTrac) is a brain tractography workflow for high performance computing. It incorporates novel technologies to simplify and accelerate neuroimaging research.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "mare": {
        "Name": "MaRe",
        "Description": "Processing Big Data with application containers on Apache Spark.\n\nItalian, pronounced: /\u02c8mare/. Noun: Sea.\n\nMaRe has been developed with scientific application in mind. High-throughput methods produced massive datasets in the past decades, and using frameworks like Spark and Hadoop is a natural choice to enable high-throughput analysis. In scientific applications, many tools are highly optimized to resemble, or detect some phenomena that occur in a certain system. Hence, sometimes the effort of reimplementing scientific tools in Spark or Hadoop can't be sustained by research groups. MaRe aims to provide the means to run existing serial tools in MapReduce fashion. Since many of the available scientific tools are trivially parallelizable, MapReduce is an excellent paradigm that can be used to parallelize the computation.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "masserstein": {
        "Name": "Masserstein",
        "Description": "Robust linear deconvolution by optimal transport.\n\nThis repository contains software tools which allow to compare spectra using the Wasserstein distance and estimate relative abundances of molecules from the spectrum by minimizing the Wasserstein distance.\n\nThe tools are distributed as a Python3 package called masserstein. Basic functionality is also available as a set of commandline applications: WSDistance to compute the Wasserstein distance and WSDeconv to estimate proportions.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "matapax": {
        "Name": "Matapax",
        "Description": "Platform for high throughput collaborative genome wide association studies (GWAS). Computational time is reduced by processing user-supplied trait data in parallel. Result analysis is facilitated by displaying annotated candidate markers in tabular format and in a genome browser.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "maude": {
        "Name": "MAUDE",
        "Description": "Inferring expression changes in sorting-based CRISPR screens.\n\nMAUDE: Mean Alterations Using Discrete Expression.\n\nMAUDE is an R package for finding differences in means of normally distributed (or nearly so) data, via measuring abundances in discrete bins.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "mcpnet": {
        "Name": "MCPNet",
        "Description": "Parallel maximum capacity-based genome-scale gene network construction framework.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "mcr_toolkit": {
        "Name": "MCR Toolkit",
        "Description": "A GPU-based toolkit for multi-channel reconstruction of preclinical and clinical x-ray CT data.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "medicc2": {
        "Name": "MEDICC2",
        "Description": "Whole-genome doubling-aware copy number phylogenies for cancer evolution with MEDICC2. Chromosomal instability (CIN) and somatic copy number alterations (SCNA) play a key role in the evolutionary process that shapes cancer genomes. SCNAs comprise many classes of clinically relevant events, such as localised amplifications, gains, losses, loss-of-heterozygosity (LOH) events, and recently discovered parallel evolutionary events revealed by multi-sample phasing. These events frequently appear jointly with whole genome doubling (WGD), a transformative event in tumour evolution, which generates tetraploid or near-tetraploid cells. WGD events are often clonal, occuring before the emergence of the most recent common ancestor, and have been associated with increased CIN, poor patient outcome and are currently being investigated as potential therapeutic targets",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "mendeliht.jl": {
        "Name": "MendelIHT.jl",
        "Description": "Implements iterative hard thresholding as a multiple regression model for GWAS. Built-in support for handling PLINK and VCF files, parallel computing, fits a variety of GLM models, and handles group/weighting SNPs.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "meta-prism": {
        "Name": "Meta-Prism",
        "Description": "Ultra-fast and highly accurate microbial community structure search utilizing dual indexing and parallel computation.\n\nMeta-Prism aims to conduct quick query in large-scale metagenomics database. The method is based on three computational techniques: dual-indexing approach for sample sub-grouping, refined scoring function that could scrutinize the minute differences among samples, and parallel computation based on GPU computing.",
        "parallel": true,
        "distributed": false,
        "GPU": true
    },
    "metacram": {
        "Name": "MetaCRAM",
        "Description": "Pipeline for taxonomy identification and lossless compression of FASTA-format metagenomic reads. \u00a0It integrates algorithms for taxonomy identification, read alignment, assembly, and finally, a reference-based compression method in a parallel manner.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "metalaffa": {
        "Name": "MetaLAFFA",
        "Description": "A flexible, end-to-end, distributed computing-compatible metagenomic functional annotation pipeline.\n\nMetaLAFFA is a flexible, end-to-end, and compute cluster-compatible metagenomic functional annotation pipeline.\nMetaLAFFA is a pipeline for annotating shotgun metagenomic data with abundances of functional orthology groups. This process consists of several steps to go from raw FASTQs (with sequencing adapters removed) to functional profiles:.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "methpanel": {
        "Name": "MethPanel",
        "Description": "A parallel pipeline and interactive analysis tool for multiplex bisulphite PCR sequencing to assess DNA methylation biomarker panels for disease detection.\n\nMethPanel is a computational pipeline in Linux operating system with an interactive graphical interface for rapid analysis of multiplex bisulphite PCR sequencing data. The tool covers a complete analysis workflow from genomic alignment to DNA methylation calling and supports an unlimited number of PCR amplicons and input samples. Moreover MethPanel offers important and unique features, such as a epipolymorphism score and a bisulphite PCR bias correction. MethPanel can be run in parallel by samples on either a personal computer or a high performance computer. The outputs are automatically forwarded to a shinyApp for convenient display, visualisation and sharing of data with collaborators and clinicians.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "mfassignr": {
        "Name": "MFAssignR",
        "Description": "Molecular formula assignment software for ultrahigh resolution mass spectrometry analysis of environmental complex mixtures.\n\nUltrahigh resolution mass spectrometry is widely used for nontargeted analysis of complex environmental and biological mixtures, such as dissolved organic matter, due to its unparalleled ability to provide accurate mass measurements.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "mica-aligner": {
        "Name": "MICA-aligner",
        "Description": "New short-read aligner that is optimized in view of MIC\u2019s limitation and the extra parallelism inside each MIC core.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "microbench": {
        "Name": "Microbench",
        "Description": "Microbench is a small Python package for benchmarking Python functions, and optionally capturing extra runtime/environment information. It is most useful in clustered/distributed environments, where the same function runs under different environments, and is designed to be extensible with new functionality. In addition to benchmarking, this can help reproducibility by e.g. logging the versions of key Python packages, or even all packages loaded into the global environment. Other captured metadata can include CPU and RAM usage, environment variables, and hardware specifications.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "mipgen": {
        "Name": "MIPgen",
        "Description": "Fast, simple way to generate designs for MIP (Molecular Inversion Probes) assays targeting hundreds or thousands of genomic loci in parallel.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "mmcp-counter": {
        "Name": "mMCP-counter",
        "Description": "The murine Microenvironment Cell Population counter method to estimate abundance of tissue-infiltrating immune and stromal cell populations in murine samples using gene expression.\n\nMurine version of MCP-counter, a tool to estimate the immune and stromal composition of heterogeneous tissue, from transcriptomic data. It is distributed as a R package.\n\n.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "mp-nerf": {
        "Name": "MP-NeRF",
        "Description": "A Massively Parallel Method for Accelerating Protein Structure Reconstruction from Internal Coordinates.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "mpgafold": {
        "Name": "MPGAfold",
        "Description": "Massively parallel genetic algorithm that predicts RNA secondary structure.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "mpiutils": {
        "Name": "mpiutils",
        "Description": "Do you have thousands of files that you need to run the same program on? Are xargs, GNU parallel, and any of the million workflow management systems not working out for you? This package may be of assistance. It provides a command line tool for parallel execution of many command line tasks on any architecture that supports MPI. It also includes parallel implementations of the Python :func:`map` and :func:`imap` functions and a few handy bits and pieces for logging.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "mpra": {
        "Name": "mpra",
        "Description": "Tools for data management, count preprocessing, and differential analysis in massively parallel report assays (MPRA).",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "mr-mspolygraph": {
        "Name": "MR-MSPOLYGRAPH",
        "Description": "MR-MSPOLYGRAPH is a MapReduce based implementation for parallelizing peptide identification from mass spectrometry data",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "mri-image-snr-computer": {
        "Name": "MRI parallel image SNR Computer",
        "Description": "Compute SNR for MRI parallel images using different reconstruction methods. There are three methods we used here to compute the SNR of MR images: ACR, SoS, and OPT.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "ms-hbm": {
        "Name": "MS-HBM",
        "Description": "MS-HBM is script for individual-specific areal-level parcellations improve functional connectivity prediction of behavior. A multi-session hierarchical Bayesian model (MS-HBM) for estimating high-quality individual-specific network-level parcellations. Here, we extend the model to estimate individual-specific areal-level parcellations. While network-level parcellations comprise spatially distributed networks spanning the cortex, the consensus is that areal-level parcels should be spatially localized, i.e., should not span multiple lobes. There is disagreement about whether areal-level parcels should be strictly contiguous or comprise multiple non-contiguous components, therefore we considered three areal-level MS-HBM variants spanning these range of possibilities: distributed MS-HBM (dMSHBM), contiguous MS-HBM (cMSHBM) and gradient-infused MS-HBM (gMSHBM).",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "msaprobs-mpi": {
        "Name": "MSAProbs-MPI",
        "Description": "Fast and Accurate Multiple Sequence Alignment with MSAProbs-MPI.\n\nParallel and accurate multiple sequence alignment.\n\nMSAProbs is a well-established state-of-the-art multiple sequence alignment algorithm for protein sequences.\n\nMSAProbs-MPI is a parallelization of MSAProbs (v0.9.7) using MPI for distributed-memory systems.",
        "parallel": true,
        "distributed": true,
        "GPU": false
    },
    "mucnv": {
        "Name": "muCNV",
        "Description": "muCNV is genotyping structural variants for population-level sequencingMulti-sample SV genotyper for large-scale WGS data. muCNV uses multiple steps for multi-sample SV genotyping, to handle large number of samples and to enable efficient parallelization:.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "multi-branch-cnn": {
        "Name": "Multi-Branch-CNN",
        "Description": "Classification of ion channel interacting peptides using parallel convolutional neural networks.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "multicrispr": {
        "Name": "multicrispr",
        "Description": "gRNA design for prime editing and parallel targeting of thousands of targets.\n\nMulti-locus multi-purpose Crispr/Cas design.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "multinanopolish": {
        "Name": "MultiNanopolish",
        "Description": "Refined grouping method for reducing redundant calculations in nanopolish.\n\nNanopolish is a software package for signal-level analysis of Oxford Nanopore sequencing data. Nanopolish can calculate an improved consensus sequence for a draft genome assembly, detect base modifications, call SNPs and indels with respect to a reference genome and more (see Nanopolish for more details).\n\nWe present an efficient implementation of Nanopolish, called MultiNanopolish. MultiNanopolish use a different iterative calculation strategy to reduce redundant calculations. We propose an abstract concept, namely independent computing unit(GroupTask) which can be distributed to the thread pool for multi-thread concurrent computing.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "mummergpu": {
        "Name": "MUMmerGPU",
        "Description": "MUMmerGPU is a low cost, ultra-fast sequence alignment program designed to handle the increasing volume of data produced by HTS.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "musket": {
        "Name": "Musket",
        "Description": "It is an efficient multistage k-mer based corrector for Illumina short read data. This corrector employs the k-mer spectrum approach and introduces three correction techniques in a multistage workflow. It is multi-threaded using a master-slave model and demonstrates superior parallel scalability compared to all other evaluated correctors as well as a highly competitive overall execution time.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "myrialign": {
        "Name": "Myrialign",
        "Description": "Software to align short reads produced by a short read genome sequencer to a reference genome. It performs brute force alignment using a variant on the 'bitap' algorithm that aligns several thousand reads to a reference in parallel. It uses bit-parallelism, multiple processors, and Cell SPUs if available.\nIt will find alignments with any number of errors up to a user specified cutoff. The emphasis is on doing a 100% accurate search as fast as is possible.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "nanoparticle-pbpk-model": {
        "Name": "Nanoparticle PBPK model",
        "Description": "SimBiology model and code to perform CI and NCA analyses, and optimization using parallel computing.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "navise": {
        "Name": "NaviSE",
        "Description": "User-friendly streamlined tool which performs a fully-automated parallel processing of genome-wide epigenomics data from sequencing files into a final report, built with a comprehensive set of annotated files that are navigated through a graphic user interface.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "nccmp": {
        "Name": "nccmp",
        "Description": "nccmp compares two NetCDF files bitwise, semantically or with a user defined tolerance (absolute or relative percentage).  Parallel comparisons are done in local memory without requiring temporary files.  Highly recommended for regression testing scientific models or datasets in a test-driven development environment.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "netmix": {
        "Name": "NetMix",
        "Description": "A network-structured mixture model for reduced-bias estimation of altered subnetworks.\n\nNetMix is an algorithm for identifying altered subnetworks with node scores that are distributed differently from other nodes in the network. NetMix improves upon current methods by using a Gaussian Mixture Model to find a less biased estimate of the size of an altered subnetwork. This README is under construction.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "neurodevsim": {
        "Name": "NeuroDevSim",
        "Description": "NeuroDevSim is a Neural Development Simulator for Linux of MacOS environments. It is a parallel and phenomenological computational framework to grow large numbers of neuronal morphologies (and resultant microcircuits) simultaneously according to growth-rules expressed in terms of interactions with the environment and internal variables:.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "ngscopy": {
        "Name": "NGScopy",
        "Description": "Provides a quantitative caller for detecting copy number variations in next generation sequencing (NGS), including whole genome sequencing (WGS), whole exome sequencing (WES) and targeted panel sequencing (TPS). The caller can be parallelized by chromosomes to use multiple processors/cores on one computer.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "nmf-mgpu": {
        "Name": "NMF-mGPU",
        "Description": "Implements the Non-negative Matrix Factorization (NMF) algorithm by making use of Graphics Processing Units (GPUs).",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "node2loc": {
        "Name": "node2loc",
        "Description": "Predicting protein subcellular location using learned distributed representations from a protein-protein network | Predicting protein subcellular location using node embedding | we present a deep learning based method, node2loc, to predict protein subcellular location. node2loc first learns distributed representations of proteins in a protein-protein network, which acquires representations from unlabeled data for downstream tasks. Then the learned representations are further fed into a recurrent neural network (RNN) to predict subcellular locations | To identify the functions of a protein, we first need know where this protein is located. Interacting proteins tend to locate in the same subcellular location. Thus, it is imperative to take the protein-protein interactions into account for computational identification of protein subcellular locations",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "normirazor": {
        "Name": "NormiRazor",
        "Description": "Tool applying GPU-accelerated computing for determination of internal references in microRNA transcription studies.\n\nGitLab is a single application for the entire software development lifecycle. From project planning and source code management to CI/CD, monitoring, and security.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "nthits": {
        "Name": "ntHits",
        "Description": "De novo repeat identification of genomics data using a streaming approach.\n\nntHits is a method for identifying repeats in high-throughput DNA sequencing data.\n\nntHits uses OpenMP for parallelization, which requires a modern compiler such as GCC 4.2 or greater. If you have an older compiler, it is best to upgrade your compiler if possible. If you have multiple versions of GCC installed, you can specify a different compiler:.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "ogda": {
        "Name": "OGDA",
        "Description": "A comprehensive organelle genomes database for algae cpDNA and mtDNA.\n\nAlgae is the earliest evolutionary biological group on the earth, are thought to have appeared at least 2.6 billion years ago. As the most complex biological group, algae have shown the high diversity during the long evolutionary history. Algae are widely distributed in 4 kingdoms of the Eukaryota, including the Plantae, Protozoa, Acritarcha, Chromista and Fungi. Up to now, there are 46,177 species of eukaryotic algae have been recognized and classified (AlgaeBase, 2020), but in fact, more species need the further confirmation.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "omicsdi": {
        "Name": "omicsDI",
        "Description": "Provides dataset discovery across a heterogeneous, distributed group of Transcriptomics, Genomics, Proteomics and Metabolomics data resources.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "onesense": {
        "Name": "oneSENSE",
        "Description": "A graphical user interface that facilitates the dimensional reduction method based on the t-distributed stochastic neighbor embedding (t-SNE) algorithm, for categorical analysis of mass cytometry data.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "openTSNE": {
        "Name": "openTSNE",
        "Description": "A modular Python library for t-SNE dimensionality reduction and embedding | Extensible, parallel implementations of t-SNE | openTSNE is a modular Python implementation of t-Distributed Stochasitc Neighbor Embedding (t-SNE), a popular dimensionality-reduction algorithm for visualizing high-dimensional data sets. openTSNE incorporates the latest improvements to the t-SNE algorithm, including the ability to add new data points to existing embeddings, massive speed improvements, enabling t-SNE to scale to millions of data points and various tricks to improve global alignment of the resulting visualizations",
        "parallel": true,
        "distributed": true,
        "GPU": false
    },
    "openalea": {
        "Name": "OpenAlea",
        "Description": "Open source project primarily aimed at the plant research community.It is a distributed collaborative effort to develop Python libraries and tools which address the needs of current and future work in Plant Architecture modeling. Includes modules to represent, analyse, and model the functioning and growth of plant architecture.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "openmdlr": {
        "Name": "OpenMDlr",
        "Description": "Parallel, open-source tools for general protein structure modeling and refinement from pairwise distances.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "opt-SNE": {
        "Name": "opt-SNE",
        "Description": "Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets.\n\nParallel opt-SNE implementation with Python wrapper.\n\nopt-SNE is a modified version of t-SNE that enables high-quality embeddings in the optimal amount of compute time without having to empirically tune algorithm parameters. For thorough background and discussion on this work, please read the paper.\n\nopt-SNE is a modified version of t-SNE that enables high-quality embeddings in the optimal amount of compute time without having to tune algorithm parameters.\n\nopt-SNE was developed to solve two fundamental problems with t-SNE. The first is the tendency of large datasets to fail to produce useful embeddings. The second is the need to empirically search the algorithm parameter space to find optimal settings",
        "parallel": true,
        "distributed": true,
        "GPU": false
    },
    "pSpatiocyte": {
        "Name": "pSpatiocyte",
        "Description": "a high-performance simulator for intracellular reaction-diffusion systems.\n\na lattice-based particle simulator.\n\nLattice-based stochastic particle simulator.\n\nSpatiocyte \u2014 A lattice-based particle simulator.\n\nSimulate with mass action, Gillespie Next-Reaction and lattice-based particle diffusion and reaction algorithms simultaneously.\n\nThree-dimensional diffusion and reaction in a cubic volume.\n\nParallel version of Spatiocyte (pSpatiocyte): Github",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "pageman": {
        "Name": "PageMan",
        "Description": "PageMan is a tool to get a quick overview of multiparallel experiments. PageMan also helps comparing experiments from different organisms.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "parallel": {
        "Name": "GNU Parallel",
        "Description": "GNU parallel is a shell tool for executing jobs in parallel using one or more computers. A job can be a single command or a small script that has to be run for each of the lines in the input. The typical input is a list of files, a list of hosts, a list of users, a list of URLs, or a list of tables. A job can also be a command that reads from a pipe. GNU parallel can then split the input and pipe it into commands in parallel.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "parallelgwas": {
        "Name": "parallelGWAS",
        "Description": "Developing parallel computing tools for genome-wide association studies.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "parallelnewhybrid": {
        "Name": "parallelnewhybrid",
        "Description": "An R package for the parallelization of hybrid detection using newhybrid.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "paramed": {
        "Name": "ParaMed",
        "Description": "A parallel corpus for English-Chinese translation in the biomedical domain.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "parasam": {
        "Name": "ParaSAM",
        "Description": "ParaSAM is a high performance parallel processing implementation of the SAM (Significance Analysis of Microarrays) algorithm. The permutations are divided across the multiple nodes. The computational workload is divided among multiple CPUs and the main memory of all participating computers is utilized to avoid caching operations to the disk, which significantly decrease algorithm execution time.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "parbibit": {
        "Name": "ParBiBit",
        "Description": "Parallel tool to accelerate the search of biclusters on binary datasets, especially useful for gene expression data. This tool receives as input the expression values of n genes and m samples in a file with ARFF extension and returns a file with the biclustering information.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "pardre": {
        "Name": "ParDRe",
        "Description": "De novo parallel tool to remove duplicated and near-duplicated reads through the clustering of Single-End or Paired-End sequences from fasta or fastq files.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "parfr": {
        "Name": "PaRFR",
        "Description": "Parallel version of RF for regression tasks with both univariate and multivariate responses \u00a0to support multivariate quantitative trait loci mapping in unrelated subjects.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "parglms": {
        "Name": "parglms",
        "Description": "support for parallelized estimation of GLMs/GEEs, catering for dispersed data",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "parkvfinder": {
        "Name": "parKVFinder",
        "Description": "Parallel KVFinder (parKVFinder) is an open-source software designed for the detection and spatial characterization (shape, volume, area and surrounding residues) of any type of biomolecular cavity. parKVFinder is available alongside an easy-to-use PyMOL plugin (PyMOL2 parKVFinder Tools) with an intuitive graphical user interface that allows users to explore customizable parameters for cavity detection and characterization.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "parmatt": {
        "Name": "parMatt",
        "Description": "parMatt: parallel multiple alignment of protein 3D-structures with translations and twists for distributed-memory systems. parMatt is based on a popular Matt software.",
        "parallel": true,
        "distributed": true,
        "GPU": false
    },
    "parmigene": {
        "Name": "parmigene",
        "Description": "The package provides a parallel estimation of the mutual information based on entropy estimates from k-nearest neighbours distances and algorithms for the reconstruction of gene regulatory networks.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "pasha": {
        "Name": "PASHA",
        "Description": "It is a parallel short read assembler for large genomes using de Bruijn graphs. Taking advantage of both shared-memory multi-core CPUs and distributed-memory compute clusters, it has demonstrated its potential to perform high-quality de-novo assembly of large genomes in reasonable time with modest computing resources.",
        "parallel": true,
        "distributed": true,
        "GPU": false
    },
    "pathoqc": {
        "Name": "PATHOQC",
        "Description": "PATHOQC is a quality control and preprocessing standalone program for a high-throughput dataset. PATHOQC combines three commonly-used quality control programs (FastQC, cutadapt, and prinseq-lite) into one package that can handle a variety type of NGS dataset in a parallel computation.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "pboost": {
        "Name": "PBOOST",
        "Description": "GPU based tool for parallel permutation tests in genome-wide association studies.",
        "parallel": true,
        "distributed": false,
        "GPU": true
    },
    "pe-assembler": {
        "Name": "PE-Assembler",
        "Description": "A simple 3' extension approach to assembling paired-end reads and capable of parallelization.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "pecanpy": {
        "Name": "PecanPy",
        "Description": "PecanPy: A parallelized, efficient, and accelerated node2vec in Python.\n\nLearning low-dimensional representations (embeddings) of nodes in large graphs is key to applying machine learning on massive biological networks. Node2vec is the most widely used method for node embedding. PecanPy is a fast, parallelized, memory efficient, and cache optimized Python implementation of node2vec. It uses cache-optimized compact graph data structures and precomputing/parallelization to result in fast, high-quality node embeddings for biological networks of all sizes and densities.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "pepinfo": {
        "Name": "pepinfo",
        "Description": "Plot amino acid properties of a protein sequence in parallel.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "pepinfo-ebi": {
        "Name": "pepinfo (EBI)",
        "Description": "Plot amino acid properties of a protein sequence in parallel.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "perceptron": {
        "Name": "PERCEPTRON",
        "Description": "PERCEPTRON is an open-source GPU-accelerated proteoform identification pipeline for top-down proteomics. PERCEPTRON is a freely available web-based proteoform identification pipeline for Top-Down Proteomics (TDP).",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "permgwas": {
        "Name": "permGWAS",
        "Description": "permGWAS is an open source software tool written in python to efficiently perform genome-wide association studies (GWAS) with permutation-based thresholds. permGWAS provides support for multiple CPUs as well as for GPUs.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "petal": {
        "Name": "PETAL",
        "Description": "PETAL (ParallEl paThways AnaLyzer): a python tool for deep analysis of biological pathways.\n\n\nPETAL software is written in the Python 3 programming language. It contains a set of tools for pathway analysis and discovery of novel therapeutic targets. The approach allows you to scan and perform a in-depth search of the biological pathway to analyze less recurrent pathways, detect nodes that are far from the initial target nodes and showing the pathway of origin from which it was taken the gene.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "pfsearch": {
        "Name": "pfsearch",
        "Description": "Replaces the original pfsearch program distributed with the pftools. It uses modern CPU instructions to exploit the capabilities of multicore processors and a new heuristic filter to rapidly score and select possible candidate matches.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "pgcloser": {
        "Name": "PGcloser",
        "Description": "Fast Parallel Gap-Closing Tool Using Long-Reads or Contigs to Fill Gaps in Genomes.\n\nThis tool is for gap-closing in the genome using long-reads or contigs. PGcloser contains 7 modules: SplitFa, ExtrGap, BwtBuilt, CompGap, ClsGap, MergFa and GetCls.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "pladipus": {
        "Name": "Pladipus",
        "Description": "Platform for distributed proteomics software.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "plast": {
        "Name": "Plast",
        "Description": "Parallel Local Alignment Search Tool for Database Comparison.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "plexdb": {
        "Name": "PLEXdb",
        "Description": "Unified gene expression resource for plants and plant pathogens. It is a genotype to phenotype, hypothesis building information warehouse, leveraging highly parallel expression data with seamless portals to related genetic, physical, and pathway data.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "pmm": {
        "Name": "pmm",
        "Description": "The Parallel Mixed Model approach is suitable for hit selection and cross-comparison of RNAi screens generated in experiments that are performed in parallel under several conditions. For example, we could think of the measurements or readouts from cells under RNAi knock-down, which are infected with several pathogens or which are grown from different cell lines.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "pnetcdf": {
        "Name": "PnetCDF",
        "Description": "PnetCDF: A Parallel I/O Library for NetCDF File Access",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "porthomcl": {
        "Name": "PorthoMCL",
        "Description": "Parallel orthology prediction using MCL for the realm of massive genome availability.\n\nParallel implementation of OrthoMCL.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "primordia": {
        "Name": "PRIMoRDiA",
        "Description": "A Software to Explore Reactivity and Electronic Structure in Large Biomolecules.\n\nPRIMoRDiA ( PRIMoRDiA Macromolecular Reactivity Descriptors Access ) is a shared memory parallel software written in C++ for post electronic structure calculations, that efficiently reads output files from most used quantum mechanics packages, storing molecular information and processing it to generate several descriptors to evaluate the global and local reactivity of molecular systems. PRIMoRDiA supports the main reactivity descriptors of the Conceptual Density Functional Theory, the most famous and used reactivity theory, which works from response variables of the electronic structure of the molecules, as also other electrostatics properties.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "prismatic": {
        "Name": "Prismatic",
        "Description": "CPU/GPU software for fast simulation of Scanning Transmission Electron Microscopy (STEM) experiments.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "probis": {
        "Name": "ProBiS",
        "Description": "Web server which detects protein binding sites based on local structural alignments. Algorithms have been parallelized to allow for faster computing against the PDB. Pre-calculated protein similarity profiles for over 29,000 non-redundant proteins are also available.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "profeatx": {
        "Name": "ProFeatX",
        "Description": "A parallelized protein feature extraction suite for machine learning.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "promocell": {
        "Name": "ProMoCell",
        "Description": "ProMoCell (Protein interaction-based functional Modules of the Cell of an organism) is a network-based zoning approach that can determine the functional modules of a cell of an organism and can potentially be utilized for parallel whole-cell simulation. ProMoCell is a single-click web service and it is very simple, user-friendly and easy to use. Presumably, no other web services like ProMoCell exists till date.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "prottrans": {
        "Name": "ProtTrans",
        "Description": "ProtTrans is providing state of the art pre-trained models for proteins. ProtTrans was trained on thousands of GPUs from Summit and hundreds of Google TPUs using various Transformers Models.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "pteros": {
        "Name": "Pteros",
        "Description": "Fast parallel molecular analysis library.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "pts": {
        "Name": "PTS",
        "Description": "Small collection of programs concerned with parallel sequencing of multiple samples on the 454 platform. untag supports Parallel Tagged Sequencing by sorting sequences according to their tags and by removing those tags when producing output files (in FASTA or SFF format) for downstream processing with standard 454 or other assembly software.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "pygbe": {
        "Name": "PyGBe",
        "Description": "Computational nanoplasmonics in the quasistatic limit for biosensing applications.\n\nPyGBe\u2014pronounced pigb\u0113\u2014is a Python library that applies the boundary integral method for biomolecular electrostatics and nanoparticle plasmonics.\n\nPyGBe achieves both algorithmic and hardware acceleration. The solution algorithm uses a Barnes-Hut treecode to accelerate each iteration of a GMRES solver to O(N logN), for N unknowns. It exploits NVIDIA GPU hardware on the most computationally intensive parts of the code using CUDA kernels in the treecode, interfacing with PyCUDA. Some parts of the code are written in C++, wrapped using SWIG.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "pygenn": {
        "Name": "PyGeNN",
        "Description": "PyGeNN is a Python library for GPU-enhanced neural networks. PyGeNN is a Python package which exposes all of GeNN's functionality to Python with minimal overhead. This provides an alternative, arguably more user-friendly, way of using GeNN and allows modelers to use GeNN within the growing Python-based machine learning and computational neuroscience ecosystems.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "pyir": {
        "Name": "PyIR",
        "Description": "PyIR is a scalable wrapper for processing billions of immunoglobulin and T cell receptor sequences using IgBLAST.\n\nPyIR is a minimally-dependent high-speed wrapper for the IgBLAST immunoglobulin and T-cell analyzer. This is achieved through chunking the input data set and running IgBLAST single-core in parallel to better utilize modern multi-core and hyperthreaded processors.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "pykvfinder": {
        "Name": "pyKVFinder",
        "Description": "Python-C Parallel KVFinder (pyKVFinder) is an efficient and integrable Python package for biomolecular cavity detection and characterization in data science. Besides conventional cavity properties such as volume and area, which are stored as Python dictionaries, pyKVFinder computes cavity depth and hydropathy. Similar to cavity points, these spatial and physicochemical properties are stored as Python ndarrays and can be visualized using Python molecular visualization widgets. In general, pyKVFinder is designed for efficient scripting routines built on easy-to-handle data structures, such as Python dictionaries and NumPy N-dimensional arrays (ndarrays), and can be building blocks for data science and drug design applications.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "pymethylprocess": {
        "Name": "pymethylprocess",
        "Description": "convenient high-throughput preprocessing workflow for DNA methylation data | Preprocessing methylation pipeline, written in python. Easy to use and highly parallelized | https://github.com/Christensen-Lab-Dartmouth/PyMethylProcess | pip install pymethylprocess && pymethyl-install_r_dependencies (Note: May need to prefix pip install with MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ for Mac OS install)",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "pyspawn": {
        "Name": "PySpawn",
        "Description": "Software for Nonadiabatic Quantum Molecular Dynamics.\n\nThe ab initio multiple spawning (AIMS) method enables nonadiabatic quantum molecular dynamics simulations in an arbitrary number of dimensions, with potential energy surfaces provided by electronic structure calculations performed on-the-fly. However, the intricacy of the AIMS algorithm complicates software development, deployment on modern shared computer resources, and postsimulation data analysis. PySpawn is a nonadiabatic molecular dynamics software package that addresses these issues. The program is designed to be easily interfaced with electronic structure software, and an interface to the TeraChem software package is described here. PySpawn introduces a task-based reorganization of the AIMS algorithm, allowing fine-grained restart capability and setting the stage for efficient parallelization in a future release",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "qbed": {
        "Name": "qBED",
        "Description": "a novel genome browser visualization for point processes.\n\nTransposon calling cards is a genomic assay for identifying transcription factor binding sites in both bulk and single cell experiments. Here we describe the qBED format, an open, text-based standard for encoding and analyzing calling card data. In parallel, we introduce the qBED track on the WashU Epigenome Browser, a novel visualization that enables researchers to inspect calling card data in their genomic context. Finally, through examples, we demonstrate that qBED files can be used to visualize non-calling card datasets, such as CADD scores and GWAS eQTL hits, and may have broad utility to the genomics community",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "qpms9": {
        "Name": "qPMS9",
        "Description": "Parallel exact qPMS (quorum Planted Motif Search) algorithm that offers significant runtime improvements on DNA and protein datasets.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "qsne": {
        "Name": "qSNE",
        "Description": "quadratic rate t-SNE optimizer with automatic parameter tuning for large datasets.\n\nNon-parametric dimensionality reduction techniques, such as t-distributed stochastic neighbor embedding (t-SNE), are the most frequently used methods in the exploratory analysis of single-cell datasets. Current implementations scale poorly to massive datasets and often require downsampling or interpolative approximations, which can leave less-frequent populations undiscovered and much information unexploited.<h4>Results< h4>We implemented a fast t-SNE package, qSNE, which uses a quasi-Newton optimizer, allowing quadratic convergence rate and automatic perplexity (level of detail) optimizer",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "quest": {
        "Name": "QuEST",
        "Description": "Kernel Density Estimator-based package for analysis of massively parallel sequencing data from chromatin immunoprecipitations (ChIP-seq or ChIPseq).",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "quickbam": {
        "Name": "quickBAM",
        "Description": "Parallelized BAM file access API for high-throughput sequence analysis informatics.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "quickld": {
        "Name": "quickLD",
        "Description": "quickLD (qLD) is a tool to calculate Linkage disequilibrium (the non-random association between alleles at different loci), with highly efficient CPU and GPU kernels that utilize dense linear algebra (DLA) operations.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "rKIN": {
        "Name": "rKIN",
        "Description": "Kernel-based method for estimating isotopic niche size and overlap.\n\nThe isotopic niche of consumers represents biologically relevant information on resource and habitat use. Several tools have been developed to quantify niche size and overlap. Nonetheless, methods adapted by spatial ecologists to quantify animal home ranges can be modified for use in stable isotope ecology when data are not normally distributed in bivariate space. We offer a tool that draws on existing spatial metrics, such as Minimum Convex Polygon (MCP) and Standard Ellipse Area (SEA), and add novel metrics using Kernel Utilization Density (KUD) estimators to measure isotopic niche size and overlap. We present examples using empirical and simulated data to demonstrate the performance of the package Kernel Isotopic Niches in R (rKIN) under various scenarios.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "raagr2-net": {
        "Name": "RAAGR2-Net",
        "Description": "A brain tumor segmentation network using parallel processing of multiple spatial frames.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "rapidgsea": {
        "Name": "rapidGSEA",
        "Description": "We present rapidGSEA \u2013 a software suite consisting of two tools for facilitating permutation-based gene set enrichment analysis(GSEA): cudaGSEA and ompGSEA. cudaGSEA is a CUDA-accelerated tool using fine-grained parallelization schemes on massively parallel architectures while ompGSEA is a coarse-grained multi-threaded tool for multi-core CPUs.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "rariant": {
        "Name": "Rariant",
        "Description": "This package identifies single nucleotide variants from sequencing data based on the difference of binomially distributed mismatch rates between matched samples.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "rck": {
        "Name": "RCK",
        "Description": "Reconstruction of clone- and haplotype-specific cancer genome karyotypes from bulk tumor samples.\n\nReconstruction of clone- and haplotype-specific Cancer Karyotypes.\n\nNOTE: this repository contains only the initial version of RCK at the time of its publication.\n\nRCK - is a method for Reconstruction of clone- and haplotype-specific Cancer Karyotypes from tumor mixtures, distributed both as a standalone software package and as a Python library under the MIT licence.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "readscan": {
        "Name": "Readscan",
        "Description": "This is a highly scalable parallel program to identify non-host sequences (of potential pathogen origin) and estimate their genome relative abundance in high-throughput sequence datasets.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "recoittv": {
        "Name": "RecoItTV",
        "Description": "It's an accelerated implementation of an iterative method for CBCT following the Split Bregman formulation, which reduces computational time through GPU-accelerated kernels. The implementation enables the reconstruction of large volumes (>10243 pixels) using partitioning strategies in forward- and back-projection operations.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "refka": {
        "Name": "RefKA",
        "Description": "A fast and efficient long-read genome assembly approach for large and complex genomes.\n\nRecent advances in long-read sequencing have the potential to produce more complete genome assemblies using sequence reads which can span repetitive regions. However, overlap based assembly methods routinely used for this data require significant computing time and resources. We have developed RefKA, a reference-based approach for long read genome assembly. This approach relies on breaking up a closely related reference genome into bins, aligning k-mers unique to each bin with PacBio reads, and then assembling each bin in parallel followed by a final bin-stitching step.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "retrieve_and_relate": {
        "Name": "Retrieve and Relate",
        "Description": "Retrieve similar sequences beginning from DNA, RNA or Proteins as well as free text - meaning there is no need to set any preliminary search parameters or filters which restrict the search space. Out of the box your search in parallel 11 of the most popular databases. Average alignment takes less then 3 seconds. Adding your own database is as simple as a click of the button.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "rfcoil": {
        "Name": "RFCoil",
        "Description": "A predictor that could distinguish parallel dimeric and trimeric coiled coils. It uses the most efficient and non-redundant amino acid indices combined with the state-of-the-art machine learning method Random Forest to make the prediction.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "rhinella": {
        "Name": "Rhinella arenarum transcriptome",
        "Description": "The common toad Rhinella arenarum is widely distributed in Argentina, where it is utilised as an autochthonous model in ecotoxicological research and environmental toxicology. However, the lack of a reference genome makes molecular assays and gene expression studies difficult to carry out on this non-model species. To address this issue, we performed a genome-wide transcriptome analysis on R. arenarum larvae through massive RNA sequencing, followed by de novo assembly, annotation, and gene prediction. We obtained 57,407 well-annotated transcripts representing 99.4% of transcriptome completeness (available at http: rhinella.uncoma.edu.ar). We also defined a set of 52,800 high-confidence lncRNA transcripts and demonstrated the reliability of the transcriptome data to perform phylogenetic analysis",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "ribodetector": {
        "Name": "RiboDetector",
        "Description": "RiboDetector is a software developed to accurately yet rapidly detect and remove rRNA sequences from metagenomeic, metatranscriptomic, and ncRNA sequencing data. It was developed based on LSTMs and optimized for both GPU and CPU.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "rmvp": {
        "Name": "rMVP",
        "Description": "A Memory-efficient, Visualization-enhanced, and Parallel-accelerated tool for Genome-Wide Association Study.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "rna-stability": {
        "Name": "rna-stability",
        "Description": "Parallel processing framework for large-scale generation of secondary RNA structures and folding statistics for the transcriptome of any species.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "roptimus": {
        "Name": "ROptimus",
        "Description": "Parallel general-purpose adaptive optimization engine.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "rpmcmc": {
        "Name": "RPMCMC",
        "Description": "A parallel MCMC algorithm for discovering diverse motifs from large sequence sets.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "rptR": {
        "Name": "rptR",
        "Description": "The rptR package offers easy-to-use functions for estimating R and its uncertainty. The package includes estimates of the repeatability (and also raw variances and marginal R2) for Gaussian traits, binomial and Poisson-distributed traits. The theory for the quantification of the repeatability for non-Gaussian traits.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "rqc": {
        "Name": "Rqc",
        "Description": "Optimised tool designed for quality control and assessment of high-throughput sequencing data that performs parallel processing of entire files and produces a report which contains a set of high-resolution graphics.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "rubioseq": {
        "Name": "RUbioSeq",
        "Description": "Primary and secondary analysis of resequencing projects by an integrated software suite of parallelized pipelines to detect exome variants (SNVs and CNVs) and to perform Bisulfite-seq analyses automatically.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "rvpedigree": {
        "Name": "RVPedigree",
        "Description": "Family-based rare variant association tests for normally and non-normally distributed quantitative traits. Calculation of kinship matrices, various options for coping with non-normality, three different ways of estimating statistical significance incorporating triaging to enable efficient use of the most computationally-intensive calculations, and a parallelization option for genome-wide analysis.",
        "parallel": true,
        "distributed": true,
        "GPU": false
    },
    "sbwt": {
        "Name": "sBWT",
        "Description": "A Burrows\u2013Wheeler transformation BWT based fast indexer/aligner specialized in parallelized indexing and searching for Next Generation Sequencing data.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "scamace": {
        "Name": "scAMACE",
        "Description": "scAMACE (integrative Analysis of single-cell Methylation, chromatin ACcessibility, and gene Expression). Python implementation (both CPU and GPU version) to a model-based approach to the joint analysis of single-cell data on chromatin accessibility, gene expression and methylation.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "scawmv": {
        "Name": "scAWMV",
        "Description": "An adaptively weighted multi-view learning framework for the integrative analysis of parallel scRNA-seq and scATAC-seq data.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "scdrake": {
        "Name": "scdrake",
        "Description": "Scdrake is a highly scalable, reproducible and configurable pipeline for scRNA-seq data prepared by a popular 10x Genomics droplet-based technology. Scdrake is implemented as a package for the R language and is built on top of the drake package, a Make-like pipeline toolkit. Scdrake currently provides common steps of scRNA-seq data analysis: quality control and filtering of cells and genes, normalization, dimensionality reduction, clustering, finding of cluster markers and differentially expressed genes between clusters, and integration of multiple datasets. All pipeline steps are accompanied by rich graphical outputs and reports in HTML format.\n\nThanks to the drake package, all intermediate results can be reused, and the pipeline can be easily extended by users to incorporate custom analyses. Also, drake analyzes which parts of the pipeline are already done or haven't changed since the last run, and which can be run in parallel, resulting in great execution speed.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "sceqtl": {
        "Name": "SCeQTL",
        "Description": "an R package for identifying eQTL from single-cell parallel sequencing data.\n\nSCeQTL is an R package that uses zero-inflated negative binomial regression to do eQTL analysis on single-cell data. It can distinguish two type of gene-expression differences among different genotype groups. It\u2019s more suitable to use SCeQTL to identify eQTLs from single-cell data. It can also be used for finding gene expression variations associated with other grouping factors like cell lineages. Following is the detail information and usage of this program. You can also found them in READ.ME. R Packages is a book based around this workflow.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "scg": {
        "Name": "Single Cell Genotyper (SCG)",
        "Description": "A new statistical model and machine learning inference algorithm designed to determine the pattern of how DNA mutations are distributed in individual cells of a tumour. The software provides implementation of several probabilistic models for clustering single cell (nucleus) data and inferring clonal genotypes.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "schema-health": {
        "Name": "schema",
        "Description": "an open-source, distributed mobile platform for deploying mHealth research tools and interventions.\n\nschema is a cross-platform mobile application for deploying mHealth monitoring and intervention studies.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "scigans": {
        "Name": "scIGANs",
        "Description": "single-cell RNA-seq imputation using generative adversarial networks.\n\nGenerative adversarial networks for single-cell RNA-seq imputation.\n\nThe data and codes for reproducing all Figures and Tables in the manuscript of scIGANs.\n\nscIGANs is a computational tool for single-cell RNA-seq imputation and denoise using Generative Adversarial Networks (GANs). Build on PyTorch, scIGNAs enables GPU acceleration inaddition to CPU computing as long as the GPUs are available.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "scikit-learn": {
        "Name": "scikit-learn",
        "Description": "scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "scitrack": {
        "Name": "SciTrack",
        "Description": "Library aimed at application developers writing scientific software to support this tracking of scientific computation. The library provides elementary functionality to support logging. The primary capabilities concern generating checksums on input and output files and facilitating logging of the computational environment. If the optional dependency mpiutils is installed, then logging can be done safely in a parallel computation environment.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "scrooge": {
        "Name": "Scrooge",
        "Description": "A fast and memory-frugal genomic sequence aligner for CPUs, GPUs and ASICs.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "sdams": {
        "Name": "SDAMS",
        "Description": "This Package utilizes a Semi-parametric Differential Abundance analysis (SDA) method for metabolomics and proteomics data from mass spectrometry. SDA is able to robustly handle non-normally distributed data and provides a clear quantification of the effect size.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "seal": {
        "Name": "SEAL",
        "Description": "Seal is a suite of distributed applications for aligning short DNA reads, manipulating and analyzing short read alignments. It is generally run on the Hadoop framework, which makes it particularly well suited for processing large data sets.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "segment_hpc": {
        "Name": "SEGMEnT_HPC",
        "Description": "A novel, massively parallel computational model of the gut, the Spatially Explicitly General-purpose Model of Enteric Tissue_HPC (SEGMEnT_HPC), which extends an existing model of the gut epithelium, SEGMEnT, in order to create cell-for-cell anatomic scale simulations",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "segseq": {
        "Name": "SegSeq",
        "Description": "An algorithm to identify chromosomal breakpoints using massively parallel sequence data.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "sepca": {
        "Name": "sePCA",
        "Description": "Rotationally Invariant Exponential Family PCA.\n\nIn photon-limited imaging, the pixel intensities are affected by photon count noise. Many applications require an accurate estimation of the covariance of the underlying 2-D clean images. For example, in X-ray free electron laser (XFEL) single molecule imaging, the covariance matrix of 2-D diffraction images is used to reconstruct the 3-D molecular structure. Accurate estimation of the covariance from low-photon-count images must take into account that pixel intensities are Poisson distributed, hence the classical sample covariance estimator is highly biased. Moreover, in single molecule imaging, including in-plane rotated copies of all images could further improve the accuracy of covariance estimation. In this paper we introduce an efficient and accurate algorithm for covariance matrix estimation of count noise 2-D images, including their uniform planar rotations and possibly reflections",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "seqseg": {
        "Name": "SeqSeg",
        "Description": "An algorithm to detect and localize copy-number alterations from massively parallel sequence data.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "seqsphere": {
        "Name": "SeqSphere+",
        "Description": "This software is designed for distributed work-groups (client/server model) and allows automatic processing, assembling and analyzing NGS (e.g., Illumina, Ion Torrent or PacBio) and Sanger capillary-electrophoresis sequence data. It provides with an easy and automated microbial analysis; enabling your lab to employ whole genome microbial typing (cgMLST or MLST+), traditional MLST or eMLST/rMLST sequencing projects.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "shinemas": {
        "Name": "SHiNeMaS",
        "Description": "A web tool dedicated to seed lots history, phenotyping and cultural practices.\n\nIn 2005, a collaboration started between INRA and the french farmers' seed network R\u00e9seau Semences Paysannes (RSP) on bread wheat. In this project, researchers and farmers' organizations needed to map the history and life cycle of the population-varieties using the network formalism. All this information needed to be centralized and stored in a database. Thus, we developed SHiNeMaS (Seeds History and Network Management System) a database with its web interface, dedicated to the management of the history of seed lots and related data like phenotyping, environment and cultural practices. SHiNeMaS is freely available and distributed under GNU Affero GPL Licence.\n\nLa forge Enseignement sup\u00e9rieur et Recherche.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "simcov": {
        "Name": "SIMCoV",
        "Description": "Spatially distributed infection increases viral load in a computational model of SARS-CoV-2 lung infection.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "simpli": {
        "Name": "SIMPLI",
        "Description": "A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially resolved tissue phenotyping at single-cell resolution. SIMPLI is a platform agnostic pipeline for the analysis of highly multiplexed histological imaging data.\n\nThis will run SIMPLI on minimal example dataset distributed in this repository. For more details on the example dataset and the associated analysis workflow see the example-workflow page.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "sitepath": {
        "Name": "sitePath",
        "Description": "A visual tool to identify polymorphism clades and help find fixed and parallel mutations.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "skeleton3d": {
        "Name": "Skeleton3D",
        "Description": "Calculates the 3D skeleton of an arbitrary binary volume using parallel medial axis thinning.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "sledgehmmer": {
        "Name": "SledgeHMMER",
        "Description": "Tool for searching the Pfam database using a parallelized version of the program hmmpfam. The user can perform queries with one or more sequences at a time and then receive the results by e-mail.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "sleepwalk": {
        "Name": "Sleepwalk",
        "Description": "Exploring dimension-reduced embeddings with Sleepwalk.\n\nInteractively Explore Dimension-Reduced Embeddings.\n\nA tool to interactively explore the embeddings created by dimension reduction methods such as Principal Components Analysis (PCA), Multidimensional Scaling (MDS), T-distributed Stochastic Neighbour Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP) or any other.\n\nSleepwalk displays a 2D embedding,i.e., a 2D representation of higher-dimensional data points, and whenever the user hovers with the mouse over a data point, all points are coloured according to their distance to the focus point under the mouse cursor.\n\nWhen working with single-cell data, e.g., from single-cell RNA-Seq, we need an intuitive visual representation of our data.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "snapMRF": {
        "Name": "snapMRF",
        "Description": "GPU-accelerated magnetic resonance fingerprinting dictionary generation and matching using extended phase graphs.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "sneakysnake": {
        "Name": "SneakySnake",
        "Description": "A Fast and Accurate Universal Genome Pre-Alignment Filter for CPUs, GPUs, and FPGAs.\n\nThe first and the only pre-alignment filtering algorithm that works efficiently and fast on modern CPU, FPGA, and GPU architectures. SneakySnake greatly (by more than two orders of magnitude) expedites sequence alignment calculation for both short (Illumina) and long (ONT and PacBio) reads.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "snpint-gpu": {
        "Name": "SNPInt-GPU",
        "Description": "SNPInt-GPU is a software providing several methods for statistical epistasis testing. SNPInt-GPU supports GPU acceleration, but can also be used without GPU hardware. The software implements logistic regression (as in PLINK epistasis testing), BOOST, log-linear regression, mutual information (MI) and information gain (IG) for pairwise testing as well as mutual information and information gain for third-order tests.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "snppar": {
        "Name": "SNPPar",
        "Description": "identifying convergent evolution and other homoplasies from microbial whole-genome alignments.\n\nData and scripts for testing SNPPar with either simulated or empirical datasets.\n\nSNPPar is designed to find homoplasic SNPs based on a user-defined phylogenetic tree - more specifically, it searches for those SNPs that are: parallel - same mutation (eg. A ~> T) @ same position in two (or more) unrelated groups/isolates; convergent - different mutation in resulting in same base (eg. A ~> T, C ~> T) @ same position in two (or more) unrelated groups/isolates; and/or revertant - mutation back to ancestral state (eg. A ~> T ~> A).\n\nThese are self-contained datasets, including reference(s), tree and snp_table(s) required to run SNPPar. The instructions for each are below in 10. Published Datasets.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "soap3-gpu": {
        "Name": "SOAP3",
        "Description": "SOAP3 is a GPU-based software for aligning short reads to a reference sequence.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "soapmetas": {
        "Name": "SOAPMetaS",
        "Description": "Profiling large metagenome datasets efficiently on distributed clusters.\n\nAn Apache SparkTM based tool for profiling large metagenome datasets accurately on distributed cluster.\n\n'meph' mode reference (recommended for new microbe communities where MetaPhlAn2 is preferable).\n\n'comg' mode reference (recommended for new samples of known microbe communities which has known gene set).\n\nSOAPMetaS has been tested in the environments of local, Spark standalone cluster as well as YARN cluster. Users should download Spark and use spark-submit file to launch SOAPMetaS.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "sonoviewer": {
        "Name": "sonoviewer",
        "Description": "Web-Based GPU-Accelerated Application for Multiplanar Reconstructions from Conventional 2D Ultrasound | ImagingAnatomy.com is an interactive atlas of normal imaging anatomy for the radiologist as well as a learning device for health professionals in general studying anatomy for any reason | Sonoviewer is an open access web application developed as an extension of the Mulrecon web-based viewer",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "spang": {
        "Name": "SPANG",
        "Description": "A command-line SPARQL client supporting query generation for distributed RDF databases.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "spark-vs": {
        "Name": "Spark-VS",
        "Description": "Spark-based library for setting up massively parallel Structure-Based Virtual Screening (SBVS) pipelines in Spark.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "sparsedossa": {
        "Name": "sparseDOSSA",
        "Description": "Sparse Data Observations for Simulating Synthetic Abundance. Provides a model based Bayesian method to characterize and simulate microbiome data. This model captures the marginal distribution of each microbial feature as a truncated, zero-inflated log-normal distribution, with parameters distributed as a parent log-normal distribution. The model can be effectively fit to reference microbial datasets in order to parameterize their microbes and communities, or to simulate synthetic datasets of similar population structure.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "spgpcw": {
        "Name": "SpGPCW",
        "Description": "SpGPCW: Spatially Varying Gaussian Process Model for Critical Window Estimation.\nA spatially varying distributed lag model with application to an air pollution and term low birth weight study.\n\nThis package implements a hierarchical Bayesian logistic regression analysis to estimate spatially varying critical windows of susceptibility corresponding to exposure from a single time-varying covariate. The method relies on a spatiotemporally structured Gaussian process and is fit using Markov chain Monte Carlo sampling techniques. Please see the 'SpGPCW_Example' folder for more specific information regarding package use details.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "splink": {
        "Name": "sPLINK",
        "Description": "A Federated, Privacy-Preserving Tool as a Robust Alternative to Meta-Analysis in Genome-Wide Association Studies.\n\nGenome-wide association studies (GWAS) have been widely used to unravel connections between genetic variants and diseases. Larger sample sizes in GWAS can lead to discovering more associations and more accurate genetic predictors. However, sharing and combining distributed genomic data to increase the sample size is often challenging or even impossible due to privacy concerns and privacy protection laws such as the GDPR. While meta-analysis has been established as an effective approach to combine summary statistics of several GWAS, its accuracy can be attenuated in the presence of cross-study heterogeneity. Here, we present sPLINK ( safe PLINK ), a user-friendly tool, which performs federated GWAS on distributed datasets while preserving the privacy of data and the accuracy of the results. sPLINK neither exchanges raw data nor does it rely on summary statistics",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "sprint": {
        "Name": "SPRINT",
        "Description": "A parallel framework for R. It provides an easy access to high performance computing for the analysis of high throughput post genomic data using the statistical programming language R.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "starbeast3": {
        "Name": "StarBeast3",
        "Description": "BEAST 2 based package for Bayesian multispecies coalescent (MSC) analyses using efficient and parallelised MCMC operators.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "stpeter": {
        "Name": "StPeter",
        "Description": "A tool for analyzing proteomics data acquired by mass spectrometry. Quantifies proteins from shotgun MS/MS data using normalized spectral count or normalized spectral index techniques. Incorporates distributed techniques to account for peptide sequence degeneracy.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "structure_threader": {
        "Name": "Structure_threader",
        "Description": "A wrapper program to parallelize and automate runs of 'Structure', 'fastStructure' and 'MavericK'.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "sv-callers": {
        "Name": "sv-callers",
        "Description": "Highly portable parallel workflow to detect structural variants in cancer genomes.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "sv-gen": {
        "Name": "sv-gen",
        "Description": "Highly portable parallel workflow to generate artificial genomes with structural variants.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "swap-assembler": {
        "Name": "SWAP-Assembler",
        "Description": "A scalable and fully parallelized genome assembler designed for massive sequencing data. Intend of using traditional de Bruijn Graph, it adopts multi-step bi-directed graph (MSG). With MSG, the standard genome assembly (SGA) is equivalent to the edge merging operations in a semi-group.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "sweed": {
        "Name": "SweeD",
        "Description": "Parallel and checkpointable tool implementing a composite ratio test for detecting selective sweeps. SweeD is based on the SweepFinder algorithm. SweeD can calculate the theoretical SFS of a give demographic model (stepwise changes or with an exponential growth phase + stepwise changes) using the method of Zickovic and Stephan (2011).",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "swipe": {
        "Name": "SWIPE",
        "Description": "Tool for performing rapid local alignment searches in amino acid or nucleotide sequence databases. It is a highly optimized implementation of the Smith-Waterman algoritm using SIMD parallel computing technology available on common CPUs.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "tensorpac": {
        "Name": "Tensorpac",
        "Description": "Tensorpac is an Python open-source toolbox for computing Phase-Amplitude Coupling (PAC) using tensors and parallel computing for an efficient, and highly flexible modular implementation of PAC metrics both known and novel. Check out our documentation for details.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "tiddit": {
        "Name": "TIDDIT",
        "Description": "Efficient and comprehensive structural variant caller for massive parallel sequencing data. Identify chromosomal rearrangements using Mate Pair or Paired End sequencing data. It allows identification of intra and inter-chromosomal translocations, deletions, tandem-duplications and inversions, using supplementary alignments as well as discordant pairs.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "timothy": {
        "Name": "Timothy",
        "Description": "Timothy is a novel large scale parallel computational model allowing 3-D simulations of cell colonies growing and interacting with variable environment in previously unavailable tissue scale.\nThe cells are modeled as individuals located in the lattice-free 3-D space. The model incorporates cellular environment modelled in a continuous manner, mathematical description based on partial differential equations is formulated for selected important components of the environment. Discrete and continuous formulations are efficiently coupled in one model and allow considerations on different scales: sub-cellular, cellular and tissue scale.\nHigh parallel scalability achieved allows simulation of up to 109 individual cells. This large scale computational approach allows for simulations to be carried out over realistic spatial scales up to 1cm in size i.e. the tissue scale.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "tinker-hp": {
        "Name": "Tinker-HP",
        "Description": "Tinker-HP is a tool for high-performance massively parallel evolution of tinker on CPUs & GPUs. It is used to accelerating molecular dynamics simulations of large complex systems with advanced point dipole polarizable force fields using GPUs and multi-GPU systems.",
        "parallel": true,
        "distributed": false,
        "GPU": true
    },
    "tipr": {
        "Name": "TIPR",
        "Description": "A sequence-based machine learning model that identifies TSSs with high accuracy and resolution for multiple spatial distribution patterns along the genome, including broadly distributed TSS patterns that have previously been difficult to characterize.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "tis": {
        "Name": "TIS",
        "Description": "Assessment of methods for Transposon Insertion Sequencing(TIS) analyses.\n\nThe TA are distributed relatively evenly along genome. The Mariner-based transposons can be inserted to impact statistically every gene, with in average more than 30 insertions site per kb. With the low insertion bias, it is easy to build saturated libraries. But local variations means less loci and less statistical power.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "trance": {
        "Name": "TraNCE",
        "Description": "TRAnsforming Nested Collections Efficiently (TraNCE) is a compilation framework that auto- mates the difficulties of designing distributed analyses with complex, biomedical data types.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "trap-seq": {
        "Name": "TRAP-seq",
        "Description": "Massively parallel quantification of CRISPR editing in cells by TRAP-seq enables better design of Cas9, ABE, CBE gRNAs of high efficiency and accuracy.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "ugahash": {
        "Name": "UGAHash",
        "Description": "An algorithm designed to provide a distributed method for assigning accession numbers to genomic features. The website is designed to introduce the utility of the tool by providing resources for mapping human genomic features among various bioinformatic databases and versions of databases with a focus on lncRNAs.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "uni_dock": {
        "Name": "Uni-Dock",
        "Description": "GPU-accelerated docking.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "variant-calling-at-scale": {
        "Name": "VariantCalling-at-Scale",
        "Description": "A scalable, parallel and efficient implementation of pre-processing and variant calling workflows on cluster environments for next generation sequencing data.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "varsifter": {
        "Name": "VarSifter",
        "Description": "Graphical java program designed to display, sort, filter, and generally sift variation data from massively parallel sequencing experiments.It is designed to read exome-scale variation data in either a tab-delimited text file with header, or an uncompressed VCF file (see User Guide for details.) These files should be pre-generated with desired annotation information one would like to view.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "vcascale": {
        "Name": "VCaScale",
        "Description": "Variant Calling at Scale is a scalable, parallel and efficient implementation of next generation sequencing data pre-processing and variant calling workflows. Our design tightly integrates most pre-processing workflow stages, using Spark built-in functions to sort reads by coordinates, and mark duplicates efficiently. A cluster scaled DeepVariant for both CPU-only and CPU+GPU clusters is also integrated in this workflow.",
        "parallel": true,
        "distributed": false,
        "GPU": true
    },
    "veryfasttree": {
        "Name": "VeryFastTree",
        "Description": "VeryFastTree is a highly-tuned implementation of the FastTree-2 tool that takes advantage of parallelization and vectorization strategies to speed up the inference of phylogenies for huge alignments. It is important to highlight that VeryFastTree keeps unchanged the phases, methods and heuristics used by FastTree-2 to estimate the phylogenetic tree. In this way, it produces trees with the same topological accuracy than FastTree-2. In addition, unlike the parallel version of FastTree-2, VeryFastTree is deterministic.\n\nTo facilitate the adoption from the research community, VeryFastTree keeps exactly the same command line arguments than FastTree-2. In this way, it is only necessary to replace the call to FastTree-2 by a call to VeryFastTree using the same options to increase the overall performance.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "virtual_imaging_platform": {
        "Name": "Virtual Imaging Platform",
        "Description": "VIP is a web portal for medical imaging applications. It allows users to access scientific applications as a service (directly through the web browser with no installation required), as well as distributed computing resources in a transparent manner. It exploits the resources available in the biomed virtual organization of the EGI e-infrastructure to offer an open service to researchers worldwide.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "vkcdb": {
        "Name": "VKCDB",
        "Description": "The Voltage-gated K(+) Channel DataBase contains full-length or nearly full-length unique channel sequences from Bacteria, Archaea and Eukaryotes. Corresponding nucleotide sequences of the open reading frames corresponding to the amino acid sequences are now available and can be extracted in parallel with sets of protein sequences. Channels are categorized into subfamilies by phylogenetic analysis and by using hidden Markov model analyses.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "watchdog": {
        "Name": "Watchdog",
        "Description": "Workflow management system for the automated and distributed analysis of large-scale experimental data",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "webapollo": {
        "Name": "WebApollo",
        "Description": "Browser-based tool for distributed community annotation of sequences.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "what_if": {
        "Name": "WHAT IF",
        "Description": "PDB Related Datbases present a series of databases that run parallel to the PDB. DSSP holds the secondary structure of the proteins. PDBREPORT holds reports on the structure quality and lists errors. HSSP holds a multiple sequence alignment for all proteins. The PDBFINDER holds easy to parse summaries of the PDB file content. PDB_REDO holds re-refined, and often improved, copies of all structures solved by X-ray.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "winbioinftools": {
        "Name": "WinBioinfTools",
        "Description": "Bioinformatics Tools for Windows Cluster provides: 1) CoCoNUT for pairwise genome comparison, 2) parallel BLAST for biological database search, and 3) parallel global pairwise sequence alignment  running over Windows Cluster running Windows HPC server 2008.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "wish-r": {
        "Name": "WISH-R",
        "Description": "WISH-R package (WISH-R) can calculate epistatic interactions using a linear or generalized linear model on a genome-wide level using genomic data and phenotype/disease data in a fully parallelized environment, and visualize genome-wide epistasis in many ways.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "x-entropy": {
        "Name": "X-Entropy",
        "Description": "X-Entropy is a parallelized kernel density estimator with automated bandwidth selection to calculate entropy. This library is primarily meant to calculate dihedral entropies from MD simulation data. For this, we use a KDE with automatic bandwidth selection as suggested by Z. Botev et al. We tried to keep the package as generalized as possible, therefore, the package can be used to calculate the entropy of any data, or also to simply calculate the KDE.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "xansons": {
        "Name": "XaNSoNS",
        "Description": "XaNSoNS is an open source software with GPU support, which simulates X-ray and neutron 1D (or 2D) diffraction patterns and pair-distribution functions (PDF) for amorphous or crystalline nanoparticles (up to \u223c atoms) of heterogeneous structural content.",
        "parallel": false,
        "distributed": false,
        "GPU": true
    },
    "xchemexplorer": {
        "Name": "XChemExplorer",
        "Description": "Data management and workflow tool for the parallel determination of protein-ligand structures. It was initially written to support crystallographic fragment screening at beamline I04-1 at the Diamond Light Source, but it is a generic program that can be used to facilitate any structure-based drug design project.",
        "parallel": true,
        "distributed": false,
        "GPU": false
    },
    "xip": {
        "Name": "XiP",
        "Description": "A flexible, editable and modular environment with a user-friendly interface that does not require previous programming skills to run,construct and edit workflows. It allows the construction of workflows by linking components written in both R and Java, the analysis of high-throughput data in grid engine systems and also the development of customized pipelines that can be encapsulated in a package and distributed.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "yabi": {
        "Name": "Yabi",
        "Description": "It is an open source software system which provides an analysis workflow environment that can create and reuse workflows as well as manage large amounts of both raw and processed data in a secure and flexible way across geographically distributed compute resources. It can be used via a web-based environment to drag-and-drop tools to create sophisticated workflows.",
        "parallel": false,
        "distributed": true,
        "GPU": false
    },
    "yalla": {
        "Name": "yalla",
        "Description": "GPU-Powered Spheroid Models for Mesenchyme and Epithelium",
        "parallel": false,
        "distributed": false,
        "GPU": true
    }
}