zhxiaokang / RASflow

RNA-Seq analysis workflow

RASflow: RNA-Seq Analysis Snakemake Workflow

RASflow is a modular, flexible and user-friendly RNA-Seq analysis workflow.

RASflow can be applied to both model and non-model organisms. It supports mapping RNA-Seq raw reads to both genome and transcriptome (can be downloaded from public database or can be homemade by users) and it can do both transcript- and gene-level Differential Expression Analysis (DEA) when transcriptome is used as mapping reference. It requires little programming skill for basic use. If you're good at programming, you can do more magic with RASflow!

You can help support RASflow by citing our publication:

Zhang, X., Jonassen, I. RASflow: an RNA-Seq analysis workflow with Snakemake. BMC Bioinformatics 21, 110 (2020). https://doi.org/10.1186/s12859-020-3433-x

Workflow

Quick start

Installation

Manual mode

Clone the repository:

git clone https://github.com/zhxiaokang/RASflow.git

Create the environment:

conda env create -n rasflow -f env.yaml

Activate the environment:

conda activate rasflow

Lazy mode

Firstly, you need to have Docker installed on your machine.

Create the container from RASflow image:

docker container run --name=rasflow -it zhxiaokang/rasflow

Activate the environment:

conda activate rasflow

Set up configuration

Modify the metafile describing your data configs/metadata.tsv.

Customize the workflow based on your need in configs/config_main.yaml.

Run RASflow

python main.py

Tutorial

A more detailed tutorial of how to use this workflow can be found here: Tutorial

Evaluation

RASflow has been evaluated on 4 datasets including two model organisms (human and mouse) and a non-model organism (Atlantic cod). To keep this repository as light as possible, the evaluation of RASflow on real datasets is deposited here: RASflow_realData