schic 0.2.4

single cell HiC analysis package

schic

schic is a Python package for analyzing sc-hic data.

Table of Contents

• schic
• Table of Contents
• Overview
• Requirements
• schic modules
  • schic whitelist
  • Usage
  • schic extract
  • Usage
  • schic cutadapt
  • Usage
  • schic contacts
  • Usage
• Docker
• Conda Environment
• Cite schic

Overview

schic

Requirements

1. Python 3.10+
2. cutadapt 2.10+
3. pairtools 2.2.3+

schic modules

schic provides a set of modules for analyzing sc-hic data. The modules are:

• whitelist
• extract
• cutadapt
• contacts
• report

schic whitelist

Usage

cd 01_whitelist

bc_pattern='(?P<discard_1>ACATGGCTACGATCCGACTTTCTGCG)(?P<cell_1>.{10})(?P<discard_2>CCTTCC)(?P<cell_2>.{10})(?P<discard_3>TCGTCGGCAGCGTCAGATGTGTATA)(?P<umi_1>.{1}).*'

NUM=$(cat ../config/sample|wc -l)
cat ../config/sample|xargs -n 1 -P ${NUM} -i -t bash -e -c \
    """
        schic whitelist \
            --bc-pattern='${bc_pattern}' \
            --stdin ../input/{}/{}_R1.fastq.gz \
            --set-cell-number=5000 \
            --plot-prefix={}_whitelist \
            --stdout={}_whitelist.txt
    """

schic extract

Usage

cd 02_extract
bc_pattern='(?P<discard_1>ACATGGCTACGATCCGACTTTCTGCG)(?P<cell_1>.{10})(?P<discard_2>CCTTCC)(?P<cell_2>.{10})(?P<discard_3>TCGTCGGCAGCGTCAGATGTGTATA)(?P<umi_1>.{8}).*'

NUM=$(cat ../config/sample|wc -l)
cat ../config/sample|xargs -n 1 -P ${NUM} -i -t bash -e -c \
    """
    schic extract \
        --bc-pattern='${bc_pattern}' \
        --stdin ../input/{}/{}_R1.fastq.gz \
        --stdout {}_R1.extracted.fastq.gz \
        --read2-in ../input/{}/{}_R2.fastq.gz \
        --read2-out {}_R2.extracted.fastq.gz \
        --whitelist=../reference/barcodes/whitelist.txt
    """

schic cutadapt

Usage

cd 03_cutadapt

NUM=$(cat ../config/sample|wc -l)
cat ../config/sample|xargs -n 1 -P ${NUM} -i -t bash -e -c \
    """
    schic cutadapt \
        --read1 ../02_extract/{}_R1.extracted.fastq.gz \
        --read2 ../02_extract/{}_R2.extracted.fastq.gz \
        --read1-out {}_R1.trimmed.fastq.gz \
        --read2-out {}_R2.trimmed.fastq.gz \
        --params='-a CTGTCTCTTATACACATCT -A AGATGTGTATAAGAGACAG'
    """

schic contacts

Usage

cd 03_contacts

NUM=$(cat ../config/sample|wc -l)
cat ../config/sample|xargs -n 1 -P ${NUM} -i -t bash -e -c \
    """
    schic contacts \
        --read1 ../03_cutadapt/{}_R1.trimmed.fastq.gz \
        --read2 ../03_cutadapt/{}_R2.trimmed.fastq.gz \
        --reference ../reference/index/bwa/genome \
        --genome-size ../reference/index/bwa/genome.size \
        --prefix {} \
        --threads 8
    
    """
# test
schic contacts \
    --read1 ../03_cutadapt/T4-Bioth-long_R1.trimmed.fastq.gz \
    --read2 ../03_cutadapt/T4-Bioth-long_R2.trimmed.fastq.gz \
    --reference ../reference/index/bwa/genome \
    --genome-size ../reference/index/bwa/genome.size \
    --prefix T4-Bioth-long \
    --filter 3 \
    --threads 16

Docker

If the user has docker installed, the following command can be used to run the pipeline in a docker container:

docker run -v /path/to/data:/data -it schic/schic:latest /bin/bash

Conda Environment

If the user has conda installed, the following command can be used to create a conda environment for schic:

1. Install conda
2. Create a new conda environment: conda create -n schic python=3.10
3. Activate the environment: conda activate schic
4. Install the required packages: conda install -c bioconda samtools bedtools
5. Install the required python packages: pip install pandas numpy scipy sklearn matplotlib seaborn pysam

Cite schic

If you use schic in your research, please cite the following paper: