Ramu, A. | bioRxiv Science

bioRxiv2018

RegTools: Integrated analysis of genomic and transcriptomic data for discovery of splicing variants in cancer

Somatic mutations within non-coding regions and even exons may have unidentified regulatory consequences that are often overlooked in analysis workflows. Here we present RegTools (www.regtools.org), a computationally efficient, free, and open-source software package designed to integrate somatic variants from genomic data with splice junctions from bulk or single cell transcriptomic data to identify variants that may cause aberrant splicing. RegTools was applied to over 9,000 tumor samples with both tumor DNA and RNA sequence data. We discovered 235,778 events where a splice-associated variant significantly increased the splicing of a particular junction, across 158,200 unique variants and 131,212 unique junctions. To 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 and compared our results to other tools that integrate genomic and transcriptomic data. While many events were corroborated by the aforementioned tools, the flexibility of RegTools also allowed us to identify novel splice-associated variants and previously unreported patterns of splicing disruption in known cancer drivers, such as TP53, CDKN2A, and B2M, as well as in genes not previously considered cancer-relevant.

bioinformatics↗

bioRxiv2018

Arnav: Site specific error models to identify variants in RNA

SummaryWe present Arnav (Analysis of RNA variants) a lightweight and easy-to-use statistical method for detecting mutations from RNA sequencing data. Site-specific error models allow Arnav to call variants with high specificity when the true variant allele fraction is unknown. We show the utility of Arnav by identifying variants using RNA sequencing data from the GTEx project.\n\nAvailability and ImplementationArnav is implemented in C++ and is distributed under the GPL license at https://github.com/gatoravi/arnav.\n\nContactaramu@wustl.edu

genomics↗

bioRxiv2018

Somatically Mutated Genes Under Positive and Negative Selection Found by Transcriptome Sequence Analysis Include Oncogene and Tumor Suppressor Candidates

IntroductionOncogenic somatic mutations confer proliferative advantage and undergo positive clonal selection. We developed software and applied new analytical approaches to identify: (1) somatic mutations in diverse tissues, (2) somatically mutated genes under positive and negative selection, (3) post-transcriptional modifications in the mitochondrial transcriptome, and (4) inherited germline alleles predisposing people to higher somatic mutation burden or higher levels of post-transcriptional modification.\n\nMethodsTranscriptome sequence data (Genotype Tissue Expression project) for 7051 tissue samples from 549 postmortem donors and representing 44 tissue types were used. Germline mutations were inferred from whole-exome DNA sequencing and SNP arrays. DNA somatic mutations were inferred from variant allele frequencies (VAF) in RNA-seq data. Post-transcriptional modifications were inferred from Polymorphism Information Content (PIC) at the p9 sites of mitochondrial tRNA sequences. Positive and negative clonal selection was evaluated using a nonsynonomous/synonomous mutation rate (dN/dS) model. Genome-wide association studies (GWAS) were assessed with mitochondrial PIC for post-transcriptional modification level, or using the total number of somatic mutations observed per donor for somatic mutation burden.\n\nResultsOur dN/dS model identified 78 genes under negative selection for somatic mutations (dN/dS < 1, padj < 0.05) and 14 under positive selection (dN/dS > 1, padj <0.05). Our GWAS identified 2 sites associated with post-transcriptional modification (1 approaching significance with p=5.99x10-8, 1 with p<5x10-8) and [~]20 sites associated with somatic mutation burden (p<5x10- 8).\n\nConclusionsTo our knowledge these are the first genome-wide association studies on normal somatic mutation burden. These studies were an attempt to increase understanding of the somatic mutation process. Our work identified somatic mutations at the global organismal level that may promote cell proliferation in a tissue-specific manner. By identifying tissue-specific mutations in actively expressed genes that appear before cancer phenotype is detected, this work also identifies gene candidates that might initiate tumorigenesis.

genomics↗