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
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
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
Modify the metafile describing your data configs/metadata.tsv.
Customize the workflow based on your need in configs/config_main.yaml.
python main.py
A more detailed tutorial of how to use this workflow can be found here: Tutorial
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