Genome Biology (Jul 2023)

SEESAW: detecting isoform-level allelic imbalance accounting for inferential uncertainty

  • Euphy Y. Wu,
  • Noor P. Singh,
  • Kwangbom Choi,
  • Mohsen Zakeri,
  • Matthew Vincent,
  • Gary A. Churchill,
  • Cheryl L. Ackert-Bicknell,
  • Rob Patro,
  • Michael I. Love

DOI
https://doi.org/10.1186/s13059-023-03003-x
Journal volume & issue
Vol. 24, no. 1
pp. 1 – 21

Abstract

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Abstract Detecting allelic imbalance at the isoform level requires accounting for inferential uncertainty, caused by multi-mapping of RNA-seq reads. Our proposed method, SEESAW, uses Salmon and Swish to offer analysis at various levels of resolution, including gene, isoform, and aggregating isoforms to groups by transcription start site. The aggregation strategies strengthen the signal for transcripts with high uncertainty. The SEESAW suite of methods is shown to have higher power than other allelic imbalance methods when there is isoform-level allelic imbalance. We also introduce a new test for detecting imbalance that varies across a covariate, such as time.