From Cancer Sequencing Data to Neoantigen Prediction: A Reusable Pipeline using Snakemake

Neoantigens are newly formed peptides formed by somatic mutations that are capable of inducing tumor-specific T-cell recognition. Because neoantigens are expressed specifically in tumor cells, prediction of these neoantigens can lead to personalized immunotherapies for the treatment of cancers. This process involves many steps, the most crucial of which is identification of expressed somatic mutations (or variants) using next generation sequencing data. After evaluating multiple bioinformatics tools for somatic mutation calling, we selected GATK (Genome Analysis ToolKit) for its ability to accurately call expected mutations. There are other steps that need to be performed before and after identification of somatic mutations as well and these include mapping, duplicate marking, annotation of mutation calls, and filtering of mutation calls. We developed a pipeline using the workflow management system Snakemake to perform these steps in order to identify somatic mutations from whole exome and RNA-Seq data. By making this into a snakemake workflow, we are able to easily extend upon it and add more steps as was done for neoantigen prediction. Furthermore, Snakemake submits slurm jobs for each individual step and can intelligently adjust the runtime and processing load for those jobs. This makes it simple to run even very large samples through the pipeline. We have evaluated this pipeline using RNA sequencing and whole exome sequencing data from 46 Multiple Myeloma cell lines and have identified hundreds of expressed mutations per cell line. This reusable and expandable pipeline can serve as a useful resource for other researchers looking to identify expressed mutations and make neoantigen predictions from cancer sequencing data