ChiaV2 is a packaged version of the ChIA-PIPE workflow, which eliminates the hassle of software installation.
DockerHub Link: https://hub.docker.com/r/fengchuiguo1994/chiav2/tags
chiav2 processes the sequencing data (ChIATAC/ChIA-PET/Bridge-linker Hi-C) to generate chromatin loops, coverage signal and contact matrix. chiaV2 includes linker calling, mapping, duplicate removing, loop calling. It also contain the software for Hi-C analysis.
ChiaV2 should be run on a Linux system that meets the following requirements:
- 4-core Intel or AMD processor (>12 cores recommended)
- 64GB RAM (>256GB recommended)
- 64-bit CentOS/RedHat 7.8 or Ubuntu 20.04
- Singularity: a container platform, >=3.8
## On Red Hat Enterprise Linux or CentOS install the following dependencies:
$ sudo yum update -y && \
sudo yum groupinstall -y 'Development Tools' && \
sudo yum install -y \
openssl-devel \
libuuid-devel \
libseccomp-devel \
wget \
squashfs-tools \
cryptsetup
## On Ubuntu or Debian install the following dependencies:
$ sudo apt-get update && sudo apt-get install -y \
build-essential \
uuid-dev \
libgpgme-dev \
squashfs-tools \
libseccomp-dev \
wget \
pkg-config \
git \
cryptsetup-bin
## Install Go
$ export VERSION=1.14.12 OS=linux ARCH=amd64 && \
wget https://dl.google.com/go/go$VERSION.$OS-$ARCH.tar.gz && \
sudo tar -C /usr/local -xzvf go$VERSION.$OS-$ARCH.tar.gz && \
rm go$VERSION.$OS-$ARCH.tar.gz
$ echo 'export GOPATH=${HOME}/go' >> ~/.bashrc && \
echo 'export PATH=/usr/local/go/bin:${PATH}:${GOPATH}/bin' >> ~/.bashrc && \
source ~/.bashrc
## Install singularity on CentOS without compile
$ yum install -y singularity
You can download chiav2 by running the following command:
singularity build chiav2.sif docker://fengchuiguo1994/chiav2:1.0
singularity build chiav2.sif docker://docker.awsl9527.cn/fengchuiguo1994/chiav2:1.0
A ChIATAC demo data from mice was provided.
# 1. Prepare genome
wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M35/GRCm39.genome.fa.gz
gunzip GRCm39.genome.fa.gz
singularity run chiav2.sif bwa index GRCm39.genome.fa
# 2. Modify the information in the config file.
# 3. Execute program.
bash run.chiaV2.sh chiaV2.config.sh ${PWD}/demoData/ChIATACdata/SCG0192 result # abspath
chiaV2.config.sh: config file
${PWD}/demoData/ChIATACdata/SCG0192: the prefix of the input fastq data, must be absolute path
result: output dir
| file name | description |
|---|---|
| *.narrowPeak | DNA binding region (like ChIPSeq) |
| *.treat_pileup.NDP.bw | coverage signal (like ChIPSeq) |
| *.hic | contact matrix (Visualization in juicer-box) |
| *.BE2.sigf.interactions | chromatin loops |
| *.final_stats.tsv | result report |
Hi-C pipeline is a packaged workflow that works for chromatin interaction data, which eliminates the hassle of software installation.
DockerHub Link: https://hub.docker.com/r/fengchuiguo1994/chiav2/tags
Hi-C pipeline processes the sequencing data of chromation interactions to generate chromatin loops and contact matrix. Hi-C pipeline includes mapping, duplicate removing, pairing. chiaV2.sif also contain the software for Hi-C analysis.
A Hi-C demo data from mice was provided.
# 1. Prepare genome
wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M35/GRCm39.genome.fa.gz
gunzip GRCm39.genome.fa.gz
singularity run chiav2.sif bwa index GRCm39.genome.fa
# 2. Modify the information in the config file.
# 3. Execute program.
bash hic_pipeline.sh ${PWD}/demoData_hic/Bcell_hic ${PWD}/result # abspath
${PWD}/demoData_hic/Bcell_hic: the prefix of the input fastq data, must be absolute path
${PWD}/result: output dir, must be absolute path
| file name | description |
|---|---|
| *.hic | contact matrix (Visualization in juicer-box) |
| *.flt1000.hic | contact matrix filtering cis contacts < 1kb (Visualization in juicer-box) |
| *.mapped.pairs.cut.gz | contact txt |
| *.flt1000.mapped.pairs.cut.gz | contact txt filtering cis contacts < 1kb |
| *.out.qc | result report |