RNAIndel: a machine-learning framework for discovery of somatic coding indels using tumor RNA-Seq data

Hagiwara, K.; Ding, L.; Edmonson, M. N.; Rice, S. V.; Newman, S.; Meshinchi, S.; Ries, R. E.; Rusch, M.; Zhang, J.

doi:10.1101/512749

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bioRxiv · 10.1101/512749

RNAIndel: a machine-learning framework for discovery of somatic coding indels using tumor RNA-Seq data

Hagiwara, K.·Ding, L.·Edmonson, M. N.·Rice, S. V.·Newman, S.·Meshinchi, S.·Ries, R. E.·Rusch, M.·Zhang, J.

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Abstract

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.\n\nBy 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{square}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.\n\nRNAIndel is freely available at https://github.com/stjude/RNAIndel\n\nContactjinghui.zhang@stjude.org

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Hagiwara, K., Ding, L., Edmonson, M. N., Rice, S. V., Newman, S., Meshinchi, S., Ries, R. E., Rusch, M., Zhang, J.. 2019-01-07. RNAIndel: a machine-learning framework for discovery of somatic coding indels using tumor RNA-Seq data. https://doi.org/10.1101/512749

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