• Kohei Hagiwara, Liang Ding, Michael N Edmonson, Stephen V Rice, Scott Newman, John Easton, Juncheng Dai, Soheil Meshinchi, Rhonda E Ries, Michael Rusch, and Jinghui Zhang.
• Computational Biology, St Jude Children's Research Hospital, Memphis, TN 38105, USA.
• Bioinformatics. 2020 Mar 1; 36 (5): 1382-1390.

MotivationReliable identification of expressed somatic insertions/deletions (indels) is an unmet need due to artifacts generated in PCR-based RNA-Seq library preparation and the lack of normal RNA-Seq data, presenting analytical challenges for discovery of somatic indels in tumor transcriptome.ResultsWe present RNAIndel, a tool for predicting somatic, germline and artifact indels from tumor RNA-Seq data. RNAIndel leverages features derived from indel sequence context and biological effect in a machine-learning framework. Except for tumor samples with microsatellite instability, RNAIndel robustly predicts 88-100% of somatic indels in five diverse test datasets of pediatric and adult cancers, even recovering subclonal (VAF range 0.01-0.15) driver indels missed by targeted deep-sequencing, outperforming the current best-practice for RNA-Seq variant calling which had 57% sensitivity but with 14 times more false positives.Availability And ImplementationRNAIndel is freely available at https://github.com/stjude/RNAIndel.Supplementary InformationSupplementary data are available at Bioinformatics online.© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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