Ribofilio

Snakemake Workflow for Variant Calling for RNASeq

This is a GATK variant calling for RNASeq snakemake pipeline written by Sherine Awad.

To run the pipeline, edit the config file to match your samples file name and reference genome. Your files should be by default in samples.tsv. Change this file name in config file if needed.

snakemake -jn 

where n is the number of cores for example for 10 cores use:

snakemake -j10 

Use conda

For less froodiness, use conda:

snakemake -jn --use-conda 

For example, for 10 cores use:

snakemake -j10 --use-conda 

This will pull automatically the same versiosn of tools we used. Conda has to be installed in the system, in addition to snakemake.

Dry Run

For a dry run use:

snakemake -j1 -n 

and to print command in dry run use:

snakemake -j1 -n -p 

Resources

1. Brouard, Jean-Simon, Flavio Schenkel, Andrew Marete, and Nathalie Bissonnette. "The GATK joint genotyping workflow is appropriate for calling variants in RNA-seq experiments." Journal of animal science and biotechnology 10, no. 1 (2019): 1-6.

2. Van der Auwera, Geraldine A., Mauricio O. Carneiro, Christopher Hartl, Ryan Poplin, Guillermo Del Angel, Ami Levy‐Moonshine, Tadeusz Jordan et al. "From FastQ data to high‐confidence variant calls: the genome analysis toolkit best practices pipeline." Current protocols in bioinformatics 43, no. 1 (2013): 11-10.

3. Poplin, R., Ruano-Rubio, V., DePristo, M. A., Fennell, T. J., Carneiro, M. O., Van der Auwera, G. A., ... & Banks, E. (2018). Scaling accurate genetic variant discovery to tens of thousands of samples. BioRxiv, 201178.

4. Rausch, T., Zichner, T., Schlattl, A., Stütz, A. M., Benes, V., & Korbel, J. O. (2012). DELLY: structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics, 28(18), i333-i339.

5. Li, H. (2011). A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics, 27(21), 2987-2993.

6. Eisfeldt, J., Vezzi, F., Olason, P., Nilsson, D., & Lindstrand, A. (2017). TIDDIT, an efficient and comprehensive structural variant caller for massive parallel sequencing data. F1000Research, 6.