Skip to content

Re-analysis of public data to compare to COVID RNA-seq results (Chalmers)

Notifications You must be signed in to change notification settings

bartongroup/MG_sulfRNAseq

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

10 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Re-analysis of L-SFN-treated PBMC mRNAseq data

Software to accompany the manuscript of Long et al., "SFX-01 in hospitalised patients with community-acquired pneumonia during the COVID19 pandemic: a double-blind, randomised, placebo-controlled trial", ERJ Open Research (2024).

Usage

On a Linux cluster

Create and activate a conda environment

cd rna_seq
conda env create -f env.yaml
conda activate sulfrnaseq

Download FASTQ files

./get_fastq.sh

Run snakemake

./run_snake.sh

This will trim adapters, perform quality control, download genome files, map reads to the reference and count reads per gene.

In RStudio

Once snakemake is finished, we suggest using RStudio. If this is done on a different machinge (I run RStudio on a laptop), some data need to be copied over (see ./scripts/get_data.sh and ./scripts/rsync_include.txt). Once in RStudio, start in the top project directory. The first step is to create environment using renv:

install.packages("renv")
renv::restore()

This will install all necessary packages. Run the targets pipeline.

targets::tar_make()

This will carry out all the calculations, create figures (some as targets, some in ./fig directory) and output TSV files in directory ./tab.

About

Re-analysis of public data to compare to COVID RNA-seq results (Chalmers)

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published