8. Integrating genomic and transcriptomic data to identify splice altering mutations across 35 cancer types

      The interpretation of variants in cancer is often focused on genomic alterations that have a known coding consequence. This analysis strategy excludes somatic mutations in non-coding regions of the genome and even exonic mutations may have unidentified non-coding consequences. To address this issue, we created RegTools, an open-source software package that integrates variant calls from genomic data with evidence of expressed splice junctions from transcriptomic data to identify variants associated with aberrant splicing. To date, we have applied RegTools to over 9,000 tumors from TCGA and Washington University School of Medicine in an effort to identify somatic variants that are associated with alternative splicing patterns. We identified over 220,000 putative splicing-relevant mutations by identifying novel splice isoforms whose expression was significantly elevated in the presence of the associated mutation. To further characterize these somatic variants and their associated splice isoforms, we annotated them with the Variant Effect Predictor (VEP), SpliceAI, and Genotype-Tissue Expression (GTEx) junction counts. While approximately 25 percent of these mutations would have been predicted by VEP or SpliceAI to be splicing relevant, our analysis provides additional detail by specifically associating a mutation with an altered junction or junctions, including ones not predicted by VEP/SpliceAI or observed within GTEx normal tissue data. We have also compared our results to other methods that have been published and found that RegTools captures variant junction associations previously missed by these approaches. RegTools is freely available (www.regtools.org) and analysis scripts are provided within the project's GitHub repository (https://github.com/griffithlab/regtools).
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