Indel detection from RNA-seq data: tool evaluation and strategies for accurate detection of actionable mutations

Abstract

Driver somatic mutations are a hallmark of a tumor that can be used for diagnosis and targeted therapy. Mutations are primarily detected from tumor DNA. As dynamic molecules of gene activities, transcriptome profiling by RNA sequence (RNA-seq) is becoming increasingly popular, which not only measures gene expression but also structural variations such as mutations and fusion transcripts. Although single-nucleotide variants (SNVs) can be easily identified from RNA-seq, intermediate long insertions/deletions (indels > 2 bases and less than sequence reads) cause significant challenges and are ignored by most RNA-seq analysis tools. This study evaluates commonly used RNA-seq analysis programs along with variant and somatic mutation callers in a series of data sets with simulated and known indels. The aim is to develop strategies for accurate indel detection. Our results show that the RNA-seq alignment is the most important step for indel identification and the evaluated programs have a wide range of sensitivity to map sequence reads with indels, from not at all to decently sensitive. The sensitivity is impacted by sequence read lengths. Most variant calling programs rely on hard evidence indels marked in the alignment and the programs with realignment may use soft-clipped reads for indel inferencing. Based on the observations, we have provided practical recommendations for indel detection when different RNA-seq aligners are used and demonstrated the best option with highly reliable results. With careful customization of bioinformatics algorithms, RNA-seq can be reliably used for both SNV and indel mutation detection that can be used for clinical decision-making.

RNAIndel: discovering somatic coding indels from tumor RNA-Seq data

“…First, GATK Best Practice of RNA-Seq variant calling has been documented in detail for GATK-HC with STAR (Materials and Methods). Second, the STAR/GATK-HC pipeline performed the best in a recent comparison study where combinations of a RNA-Seq mapper and a variant caller were tested for detecting known somatic EGFR indels in lung cancer (Sun et al, 2017). Following this procedure, we evaluated the performance of three approaches for detecting expressed pathogenic indels: RNAIndel with the built-in caller, RNAIndel with GATK-HC, and the Best Practice-based approach without RNAIndel (Fig.…”

“…and has been largely unexplored (Sun et al, 2017). Even in DNA-Seq, indel discovery suffers from a high false discovery rate (Fang et al, 2014).…”

“…Opossum (Oikkonen and Lise 2017) preprocesses RNA-Seq reads for SNV calling by splitting intron-spanning reads and removing spurious reads. By contrast, inde detection in transcriptome is more challengingand has been largely unexplored (Sun et al, 2017). Even in DNA-Seq, indel discovery suffers from a high false discovery rate (Fang et al, 2014).…”

Finding a suitable library size to call variants in RNA-seq

“…The reference fasta genome and gtf files necessary to build the STAR index were downloaded from https://sapac.support.illumina.com/sequencing/ sequencing_software/igenome.html. STAR was chosen for several reasons: its speed; its good performance in the correct alignment of indels [28]; and since it is the suggested choice in the GATK [29] Best Practices for RNA-Seq variant calling. Read groups were added to the aligned BAM files using AddOrReplaceReadGroups from Picard tools[30] 2.9.4 and PCR duplicates were marked with sambamba[31] 0.6.6 markdup.…”

CaSpER: Identification, visualization and integrative analysis of CNV events in multiscale resolution using single-cell or bulk RNA sequencing data

“…These datasets, however, contain a substantial amount of information about the genomic variants in the samples and are severely underutilized. For example, RNA-Seq data has been used to identify single nucleotide polymorphisms (SNP) and short indels [12][13][14] . Identification of these variants from RNA-Seq data increases the utility of RNA-Seq experiments significantly compared to using RNA-Seq only for gene expression quantification because researchers can integrate a portion of the genomic landscape of the tumor cells (as much as it is revealed by RNA-Seq) with the transcriptomic landscape rather than studying the transcriptomic landscape of the cells alone.…”