/MNase-seq-workflow

A standard workflow on preprocessing MNase-seq data

Primary LanguagePythonMIT LicenseMIT

Snakemake pipeline for MNase-seq data

@author Jianyu Yang, Pennsylvania State University

Dependency

  • miniconda
  • snakemake(>=5.1.2)

Quick Start

  • Put the gzipped fastq data into the data folder
  • Modify the samples.tsv and units.tsv file corresponding to the data
  • excute the following command in the root directory of the project, you'll see an output folder with all generated files! 
    snakemake --use-conda --cores

Introduction

This pipeline aims for standard MNase-seq fastq files handling, which consists of

  • Standard Procedure:
    • reads trimming
    • bowtie2 mapping
    • mark duplicates
    • reads filtering by samtools and python script
  • QC:
    • multiqc report
    • fragment size frequency report

Written in Snakemake, which is a very powerful workflow management system