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Bioinformatics (Oxford, England)
03 01, 2020;36 (5): 1382-1390.

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

Kohei Hagiwara, Liang Ding, Michael N Edmonson, Stephen V Rice, Scott Newman, John Easton, Juncheng Dai, Soheil Meshinchi, Rhonda E Ries, Michael Rusch, Jinghui Zhang
Author Information
  1. Kohei Hagiwara: Computational Biology, St Jude Children's Research Hospital, Memphis, TN 38105, USA.
  2. Liang Ding: Computational Biology, St Jude Children's Research Hospital, Memphis, TN 38105, USA.
  3. Michael N Edmonson: Computational Biology, St Jude Children's Research Hospital, Memphis, TN 38105, USA.
  4. Stephen V Rice: Computational Biology, St Jude Children's Research Hospital, Memphis, TN 38105, USA.
  5. Scott Newman: Computational Biology, St Jude Children's Research Hospital, Memphis, TN 38105, USA.
  6. John Easton: Computational Biology, St Jude Children's Research Hospital, Memphis, TN 38105, USA.
  7. Juncheng Dai: Department of Epidemiology, Nanjing Medical University School of Public Health, Jiangning District, Nanjing, 211166, People's Republic of China.
  8. Soheil Meshinchi: Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.
  9. Rhonda E Ries: Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.
  10. Michael Rusch: Computational Biology, St Jude Children's Research Hospital, Memphis, TN 38105, USA.
  11. Jinghui Zhang: Computational Biology, St Jude Children's Research Hospital, Memphis, TN 38105, USA.
PMID: 31593214 DOI: 10.1093/bioinformatics/btz753

Abstract

MOTIVATION: Reliable 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.
RESULTS: We 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 IMPLEMENTATION: RNAIndel is freely available at https://github.com/stjude/RNAIndel.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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Grants

  1. P50 GM115279/NIGMS NIH HHS

MeSH Term

Child
High-Throughput Nucleotide Sequencing
Humans
INDEL Mutation
Neoplasms
RNA-Seq
Software
Exome Sequencing
Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't
Article has an altmetric score of 6

Word Cloud

Created with Highcharts 10.0.0indelssomaticRNA-SeqdatatumorRNAIndelavailableMOTIVATION:Reliableidentificationexpressedinsertions/deletionsunmetneeddueartifactsgeneratedPCR-basedlibrarypreparationlacknormalpresentinganalyticalchallengesdiscoverytranscriptomeRESULTS:presenttoolpredictinggermlineartifactleveragesfeaturesderivedindelsequencecontextbiologicaleffectmachine-learningframeworkExceptsamplesmicrosatelliteinstabilityrobustlypredicts88-100%fivediversetestdatasetspediatricadultcancersevenrecoveringsubclonalVAFrange001-015drivermissedtargeteddeep-sequencingoutperformingcurrentbest-practicevariantcalling57%sensitivity14timesfalsepositivesAVAILABILITYANDIMPLEMENTATION:freelyhttps://githubcom/stjude/RNAIndelSUPPLEMENTARYINFORMATION:SupplementaryBioinformaticsonlineRNAIndel:discoveringcoding

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