RNA-Seq-analysis

A workflow made with snakemake to analyse a specific gene expression.

Contents

• Description
• Requirements
• Installation and application
• References

Description

RNA-Seq-analysis is an open software workflow using snakmeake to analyze clinical data following the procedure of, the STAR Protocol, Analysis workflow of publicly available RNA-sequencing datasets. The two graphics below show the different rules in a graph and their respective files that will be in- and output.

Requirements

Ensure you have the required dependencies:

• mamba >= 1.5 (alternatively conda >= 23.11)
• an environment that has snakemake >= 8.5 installed as specified here
• a stable internet connection

Installation and application

1. Clone this repository via:

   git clone https://github.com/ToLeWeiss/RNA-Seq-analysis.git --depth 1
   

2. Change the config if necessary (i.e. if you want to use a different GEO dataset or analyze a different set of genes)

3. Activate the base environment and your snakemake environment
   If you use mamba:

   mamba activate base && mamba activate <your snakemake environment>
   

   If you use conda:

   conda activate base && conda activate <your snakemake environment>
   

4. Run the workflow: you can specify your preferred number of cores yourself in the below code it is set to 1, which should be ok for all systems.

   snakemake --use-conda --cores 1
   

OPTIONAL

5. Generate a report from the executed workflow if you want to generate a web report, after the workflow has run, you can generate one as follows:

   snakemake --report report.html
   

   If you want to change the text from the report you can go to the report directory and change the respective *.rst file.

References

STARS Protocol

• Sanchis, P., Lavignolle, R., Abbate, M., Lage-Vickers, S., Vazquez, E., Cotignola, J., … Gueron, G. (2021). Analysis workflow of publicly available RNA-sequencing datasets. STAR Protocols, 2(2), 100478.

Original Paper

• Bizzotto, J., Sanchis, P., Abbate, M., Lage-Vickers, S., Lavignolle, R., Toro, A., … Gueron, G. (2020). SARS-CoV-2 Infection Boosts MX1 Antiviral Effector in COVID-19 Patients. iScience, 23(10), 101585.

Snakemake

• Mölder, F., Jablonski, K.P., Letcher, B., Hall, M.B., Tomkins-Tinch, C.H., Sochat, V., Forster, J., Lee, S., Twardziok, S.O., Kanitz, A., Wilm, A., Holtgrewe, M., Rahmann, S., Nahnsen, S., Köster, J., 2021. Sustainable data analysis with Snakemake. F1000Res 10, 33.

• Snakemake on GitHub

• The snakemake website

• Documentation of Snakemake

R Packages

• DESeq2

  • Love MI, Huber W, Anders S (2014). “Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.” Genome Biology, 15, 550

  • The DESeq2 repository

• ggplot2

  • H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.

  • The ggplot2 website

  • ggplot2 on GitHub

• GGally

  • Schloerke B, Cook D, Larmarange J, Briatte F, Marbach M, Thoen E, Elberg A, Crowley J (2024). GGally: Extension to 'ggplot2'.

  • The GGally website

  • GGally on GitHub

• canvasXpress

  • The canvasXpress website

  • canvasXpress on GitHub

• factoextra

  • The factoextra website

  • factoextra on GitHub

• clinfun

  • Seshan V, Whiting K (2023). clinfun: Clinical Trial Design and Data Analysis Functions. R package version 1.1.1.

  • The clinfun website

  • clinfun on GitHub

• GEOquery

  • Davis S, Meltzer P (2007). “GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor.” Bioinformatics, 14, 1846–1847.

  • The GEOquery repository

Pandoc

• MacFarlane, J., Krewinkel, A., & Rosenthal, J. Pandoc [Computer software]

• The Pandoc website