Finding Expressed Mutations in Multiple Myeloma Cell Lines

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Richardson, Jensen
Pritha, Jafrin
Jiang, Wenxuan
Prasad, Rohit
Arasappan, Dhivya
Kowalski-Muegge, Jeanne

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Neoantigens are newly formed peptides created from somatic mutations that are capable of inducing tumor-specific T-cell recognition. Prediction of these neoantigens can lead to personalized immunotherapies for the treatment of cancers. Identification of expressed somatic mutations using next generation sequencing data is a crucial first step in neoantigen prediction. Because of the expansion of next generation sequencing data, there exist a plethora of tools designed to sift through this data and return high quality Single Nucleotide Variants (SNVs) and small insertions and deletions (indels), however, it is essential to select tools that are flexible, efficient, and above all, accurate at detecting these mutations. Using RNA sequencing combined with whole exome sequencing data from 71 Human Multiple Myeloma cell lines (HMCLs), we compared different variant calling tools to develop a workflow for identifying expressed mutations. The use of well characterized HMCL’s with known SNVs and indels enables us to compare the accuracy of each variant calling tool. Thus far, we have compared the accuracy and efficiency of VarScan’s simple variant calling pipeline to GATK’s fully encompassing pipelines for exome and RNA-Seq data and have incorporated post-filtering, annotation and visualization of found variants to our workflow. Because of the large number of HMCLs and the several steps required and specific to each pipeline, we used Lonestar5 to parallelize our processing of the data. Our completed workflow will provide a standardized means for identifying expressed mutations in tumors.




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