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

By Kohei Hagiwara, Liang Ding, Michael N. Edmonson, Stephen V. Rice, Scott Newman, Soheil Meshinchi, Rhonda E. Ries, Michael Rusch, Jinghui Zhang

Posted 07 Jan 2019
bioRxiv DOI: 10.1101/512749 (published DOI: 10.1093/bioinformatics/btz753)

Reliable identification of expressed somatic insertion/deletion (indels) is an unmet demand 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 trasncriptome. By implementing features characterized by PCR-free whole-genome and whole-exome sequencing into a machine-learning framework, we present RNAIndel, a tool for predicting somatic, germline and artifact indels from tumor RNA-Seq data alone. RNAIndel robustly predicts 87□93% of somatic indels from 235 samples with heterogeneous conditions, 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 12 times more false positives. RNAIndel is freely available at <https://github.com/stjude/RNAIndel> Contact jinghui.zhang{at}stjude.org

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