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Inspectro

Nextflow run with conda run with docker

Introduction

Diagram of Inspectro pipeline

Inspectro is a bioinformatics pipeline that characterizes long-range interaction profiles in Hi-C maps by spectral decomposition. The pipeline is built using Nextflow, a workflow tool that enables easy installation and reproducibility of the pipeline through Docker containers.

This pipeline (v1.2) supports S3 files for bigwig/bigbed tracks.

Note: Local files are not currently supported.

Future update: Adding iGenomes, which will provide the option to use your own fasta file or one provided by iGenomes for the genome of your choice.

Quick Start and Usage

  1. Install Nextflow (>=21.10.3)

  2. Install Docker to implement environment for pipeline reproducibility. Build the Docker image: docker build -t open2c/inspectrov1.2:latest ./docker

  3. Place supplementary tab-delimited bigwig file with a header in data/track_metadata.tsv to include in graphical outputs. The following columns must be included:

  • Name: a display name
  • ID: a unique identifier to use in the database (can be the same as Name)
  • FileFormat: must be the string bigWig
  • Path: a local absolute path to the file
  1. Add supplementary bigwig track names, sample name and genome assembly name to the params.yml file.

  2. Finally, you can run the pipeline using:

nextflow run main.nf \
    --config params.yml \
    --genome <FASTA_file> \
    --meta track_metadata.tsv \
    --outdir <OUTDIR> \
    --blacklist <blacklist_file> \
    --mcool <mcool_file> \
    -profile docker

Credits

Tarishi Pathak, Aleksandra Galitsyna, George Spracklin, Nezar Abdennur

Citation

@article{spracklin2022diverse,
  title={Diverse silent chromatin states modulate genome compartmentalization and loop extrusion barriers},
  author={Spracklin, George and Abdennur, Nezar and Imakaev, Maxim and Chowdhury, Neil and Pradhan, Sriharsa and Mirny, Leonid A and Dekker, Job},
  journal={Nature Structural \& Molecular Biology},
  pages={1--14},
  year={2022},
  publisher={Nature Publishing Group}
}

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  • Python 83.9%
  • Nextflow 15.8%
  • Dockerfile 0.3%