exVariance

A tool for integrated analysis of the liquid biopsy sequencing data

Summary

Content

Manual

RNA-seq realted analysis

RNA-seq pre process

DNA-methylation realted analysis

DNA-seq realted analysis

Help message

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

Installation

Best Practice

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

## 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")

Docker image

For easy installation, you can use the exVariance image of docker with all dependencies installed:

  docker pull <exVariance_image>

dependencies
1. docker version>=19.03.4

Singularity image

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.

Download References

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

Change Log

v1.0.0

Release exVariance

v1.0.1

Fix some bugs

Details

For paire end analysis, the fastq files should end with _1.fastq.gz and _2.fastq.gz, and in the sample_ids.txt file, the suffix should not write in the file.
You need to create the following 3 directories: summary, output and temp.

System Requirements:

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.

522845911 / exvariance Goto Github PK

exvariance's Introduction