metavannier / metatranscriptomics-bulkrnaseq_workflow

This workflow performs a metatranscriptomics and bulk RNAseq analysis from the sequencing output data to the differential expression analyses.

Differential Expression Workflow: Metatranscriptomic and bulk RNAseq analysis

Author

Thomas Vannier (@metavannier), https://centuri-livingsystems.org/t-vannier/

About

This workflow performs a metatranscriptomic analysis from the sequencing output data to the differential expression analyses.

You need to install Singularity on your computer. This workflow also work in a slurm environment.

Each snakemake rules call a specific conda environment. In this way you can easily change/add tools for each step if necessary.

3 steps for the analysis:

• clean.smk: The quality of the raw reads are assessed using FastQC v0.11.9 toolkit. Adapters and low quality reads are trimmed using Trimmomatic v0.39. SortMeRNA v4.3.4 is used to filter rRNA fragments from the reads.
• count.smk: HiSat2 v2.2.1 is used for mapping the raw reads to the reference genome. The expression for each gene is evaluated using featureCounts from the Subread v2.0.1 package.
• differential_exp.smk: The low expressed genes are removed from further analysis. The raw counts are normalized and used for differential expression testing using DESeq2 v1.28.0.

Usage

Step 1: Install workflow

You can use this workflow by downloading and extracting the latest release. If you intend to modify and further extend this workflow or want to work under version control, you can fork this repository.

We would be pleased if you use this workflow and participate in its improvement. If you use it in a paper, don't forget to give credits to the author by citing the URL of this repository and, if available, its DOI (see above).

Step 2: Configure workflow

Configure the workflow according to your needs via editing the files and repositories:

• 00_RawData need the single or pair-end fastq file of each run to analyse
• 01_Reference the fasta file and gff/gtf of your reference genome for the mapping step
• sample.tsv, coldata.tsv, condition.tsv and ko_list.tsv to indicate the samples, run, condition, list of ko etc. for the analyse.
• config.yaml indicating the parameters to use.

Step 3: Execute workflow

In local

• You need Singularity v3.5.3 installed on your computer or cluster.

• Load snakemake from a docker container and run the workflow from the root by using these commands:

singularity run docker://snakemake/snakemake:v6.3.0

• Then execute the workflow locally via

snakemake --use-conda --use-singularity --cores 10

On a cluster

• Adapt the batch scripts run_slurm.sh and cluster_config.json file to run your snakemake from the working directory

It will install snakemake with pip and run the workflow in the HPC:

sbatch run_slurm.sh

Step 4: Investigate results

After successful execution, you can create a self-contained interactive HTML report with all results via:

snakemake --report report.html