Integrated analysis of genomic and transcriptomic data for the discovery of splice-associated variants in cancer

Abstract

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. We apply RegTools to over 9000 tumor samples with both tumor DNA and RNA sequence data. RegTools discovers 235,778 events where a splice-associated variant significantly increases 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 annotate them with the Variant Effect Predictor, SpliceAI, and Genotype-Tissue Expression junction counts and compare our results to other tools that integrate genomic and transcriptomic data. While many events are corroborated by the aforementioned tools, the flexibility of RegTools also allows us to identify splice-associated variants in known cancer drivers, such as TP53, CDKN2A, and B2M, and other genes.

Prediction of tumor-specific splicing from somatic mutations as a source of neoantigen candidates

“…IRIS [24] is a modular neoantigen prediction pipeline based on RNA-seq and supports customized pipelines. Regtools [28] provides functionalities to integrate DNA sequencing and RNA-seq data to identify potential splice-associated variants. DICAST [44] integrates several RNA-seq splicing tools for unified junction analysis.…”

Cas13d-mediated isoform-specific RNA knockdown with a unified computational and experimental toolbox

“…I. Li et al 2017): reads were aligned to hg38 using STAR v2.7.1 with parameters ‘--twopassMode’ and ‘--outSAMstrandField intronMotif’ (Dobin et al 2013). Sequencing reads that overlap exon-exon junctions were counted using ‘regtools junction extract’ v0.5.2 with parameters ‘-a 8 -m 50 -M 500000 -s 1’ (Cotto et al 2023). Next, to define intron clusters using LeafCutter, we ran the ‘leafcutter_cluster_regtools.py’ python script with ‘-m 30 -p 0.01 -l 500000 parameters’ (Y.…”

“…A375 gene expression, percent gene nuclear, and percent junction nuclear were calculated from A375 RNA-seq data generated in this paper (see ‘Methods: Illumina short read sequencing analysis’ and Supplemental Table S3 for additional information). Briefly, gene counts were obtained using featureCounts (Liao, Smyth, and Shi 2013) and were normalized to RPKM (Reads Per Kilobase Million) and junction counts were obtained using RegTools (Cotto et al 2023). Percent nuclear values were obtained by dividing the nuclear read counts by the sum of the nuclear and cytoplasmic read counts for a gene or junction.…”

“…HEK293, hESC, and A375 junction counts (provided in Isoviz and used for non-sequence features in Supplementary Figure S2) were generated using Regtools extract v0.5.2 (Cotto et al 2023) with ‘-a 8 -m 50 -M 500000 -s 0’ parameters.…”

Global impact of aberrant splicing on human gene expression levels

“…To quantify splicing in the RNA-seq datasets, we extracted junction reads by running regtools version 0.5.2 80 on the filtered BAM files, requiring a minimum intron length of 20 bp. We merged the resulting .junc files into a single database of observed splice junctions and splice junction read counts across all samples.…”