CARTMAN: Co-occurrence Analysis of Repeating Transcription-factor Motifs And Networks
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Updated
Oct 9, 2024 - Jupyter Notebook
CARTMAN: Co-occurrence Analysis of Repeating Transcription-factor Motifs And Networks
A powerful abstraction of gene databases
Scanning sample-specific miRNA regulation from bulk and single-cell RNA-sequencing data
Python visualization of single locus SMF data
An R package for multi-dimensional pathway enrichment analysis
All code generated for Loupe et al. 2023
ChIP-seq analysis pipeline encompassing data processing, quality control, alignment, peak calling, annotation and motif analysis.
Depicting pseudotime-lagged causality for accurate gene-regulatory inference
A scalable python-based framework for gene regulatory network inference using tree-based ensemble regressors.
pyJASPAR: A Pythonic interface to JASPAR transcription factor motifs
Code and data used by the JASPAR profile inference tool
All code generated from Rogers et al. 2024
Code for simulations and analysis used in Menon et al., 2024, "Proximal termination generates a transcriptional state that determines the rate of establishment of Polycomb silencing "
Detecting Regulatory Elements using GRO-seq and PRO-seq
Fast Inference of Networks from Directed Regulations
Inferring ncRNA synergistic competition
Gene regulation exploring with OOP
De novo discovery of traits co-occurring with chronic obstructive pulmonary disease.
Code for the research article "Exploring functional protein covariation across single cells using nPOP". It is distributed under MIT license.
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