A tool for integrated analysis of the liquid biopsy sequencing data
- Summary
- Installation
- Download References
- Change Log
- Details
- System Requirements
- Copyright and License Information
- Citation
RNA-seq realted analysis
DNA-methylation realted analysis
DNA-seq realted analysis
Run exVariance --help
to get the usage:
usage: exVariance [-h] --user_config_file USER_CONFIG_FILE
[--cluster]
[--cluster-config CLUSTER_CONFIG]
[--cluster-command CLUSTER_COMMAND]
[--singularity SINGULARITY]
[--singularity-wrapper-dir SINGULARITY_WRAPPER_DIR]
{ RNA_seq_pre_process,RNA_seq_exp_matrix,
RNA_seq_fusion_transcripts,RNA_seq_RNA_editing,
RNA_seq_SNP,RNA_seq_APA,RNA_seq_AS,
DNA_seq_ctDNA_mutation,DNA_seq_NP,
DNA_meth_WGBS,DNA_meth_RRBS,
DNA_meth_Seal_seq,DNA_meth_Methyl-cap_seq,
DNA_meth_MeDIP_seq,DNA_meth_MCTA_seq
}
exVariance is a tool for integrated analysis of the liquid biopsy sequencing data.
positional arguments:
{ RNA_seq_pre_process,RNA_seq_exp_matrix,
RNA_seq_fusion_transcripts,RNA_seq_RNA_editing,
RNA_seq_SNP,RNA_seq_APA,RNA_seq_AS,
DNA_seq_ctDNA_mutation,DNA_seq_NP,
DNA_meth_WGBS,DNA_meth_RRBS,
DNA_meth_Seal_seq,DNA_meth_Methyl-cap_seq,
DNA_meth_MeDIP_seq,DNA_meth_MCTA_seq
}
optional arguments:
-h, --help show this help message and exit
--user_config_file USER_CONFIG_FILE, -u USER_CONFIG_FILE
the user config file
--cluster submit to cluster
--cluster-config CLUSTER_CONFIG
cluster configuration file
--cluster-command CLUSTER_COMMAND
command for submitting job to cluster (default read
from {config_dir}/cluster_command.txt
--singularity SINGULARITY
singularity image file
--singularity-wrapper-dir SINGULARITY_WRAPPER_DIR
directory for singularity wrappers
For additional help or support, please visit https://github.com/ShangZhang/exVariance
Install the github source code and ependencies below listed:
git clone https://github.com/ShangZhang/exVariance.git
Dependencies:
- Anaconda3/Miniconda3 conda version latter than 4.8.4
- Python version latter than 3.8.3
- Snakemake version=5.14.0
- R version=3.6.3
- R packages
How to install all the dependencies:
- Install Anaconda3/Minicodna3 and Python
wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh bash Miniconda3-latest-Linux-x86_64.sh
- Whilst running the installation script, follow the commands listed on screen, and press the enter key to scroll.
- Make sure to answer yes when asked if you want to prepend Miniconda3 to PATH.
- Close your terminal, open a new one and you should now have Conda working! Test by entering:
conda update conda
- Press y to confirm the conda updates
- Install Mamba
The default conda solver is a bit slow and sometimes has issues with selecting the special version packages. Therefore, we recommend to install Mamba as a drop-in replacement via
conda install -c conda-forge mamba
- Install Snakemake 5.14.0 and R 3.6.3
mamba create -n exvariance -c conda-forge -c bioconda snakemake=5.14.0 r-base=3.6.3 -y
- Install related R packages
Best Practice
conda activate exvariance mamba install -c r -c conda-forge -c bioconda -c eugene_t r-argparse r-clustersim r-ggpubr bioconductor-scater bioconductor-scran bioconductor-singlecellexperiment bioconductor-sva bioconductor-edger bioconductor-ruvseq r-kbet r-devtools -y
OR## continue to install other tools in R conda activate exvariance R > library(usethis) > library(devtools) > devtools::install_github(c("hemberg-lab/scRNA.seq.funcs","theislab/kBET"),host="https://api.github.com")
conda activate exvariance R > library(usethis) > library(devtools) > install.packages(c("argparse","clusterSim","ggpubr","BiocManager","devtools")) > BiocManager::install(c("scater","scran","SingleCellExperiment","sva","edgeR","RUVSeq")) > devtools::install_github(c("hemberg-lab/scRNA.seq.funcs"),host="https://api.github.com") > devtools::install_github(c("theislab/kBET"),host="https://api.github.com")
For easy installation, you can use the exVariance image of docker with all dependencies installed:
docker pull <exVariance_image>
- dependencies
- docker version>=19.03.4
Alternatively, you can use use singularity or udocker to run the container for Linux kernel < 3 or if you don't have permission to use docker.
exVariance is dependent on reference files which can be found for the supported species listed below: hg38
To unzip these files: tar -xzf hg19.tar.gz OR tar -xzf mm9.tar.gz
- Release exVariance
- Fix some bugs
- For paire end analysis, the fastq files should end with
_1.fastq.gz
and_2.fastq.gz
, and in thesample_ids.txt
file, the suffix should not write in the file. - You need to create the following 3 directories:
summary
,output
andtemp
.
Some of the tools that exVariance uses, e.g. STAR is very memory intensive programs. Therefore we recommend the following system requirements for exVariance:
Minimal system requirements: We recommend that you run exVariance on a server that has at least 48GB of ram. This will allow for a single-threaded exVariance run (on human samples).
Recommended system requirements: We recommend that you have at least 64GB of ram and at least a 4-core CPU if you want to run exVariance in multi-threaded mode (which will speedup the workflow significantly).
Our own servers have 64GB of ram and 16 cores.
Copyright (C) Lu Lab @ Tsinghua University, Beijing, China 2020 All rights reserved