diffBUM-HMM: A robust statistical modeling approach for detecting RNA flexibility changes in high-throughput structure probing data

Paolo Marangio, Ka Ying Toby Law, Guido Sanguinetti, Sander Granneman

Research output: Contribution to journalArticlepeer-review

Abstract

Advancing RNA structural probing techniques with next-generation sequencing has generated demands for complementary computational tools to robustly extract RNA structural information amidst sampling noise and variability. We present diffBUM-HMM, a noise-aware model that enables accurate detection of RNA flexibility and conformational changes from high-throughput RNA structure-probing data. diffBUM-HMM is widely compatible, accounting for sampling variation and sequence coverage biases, and displays higher sensitivity than existing methods while robust against false positives. Our analyses of datasets generated with a variety of RNA probing chemistries demonstrate the value of diffBUM-HMM for quantitatively detecting RNA structural changes and RNA-binding protein binding sites.
Original languageEnglish
Article number165
Number of pages21
JournalGenome Biology
Volume22
Issue number1
DOIs
Publication statusPublished - 27 May 2021

Keywords

  • hidden Markov model
  • high-throughput RNA structure probing
  • RNA structural changes

Fingerprint Dive into the research topics of 'diffBUM-HMM: A robust statistical modeling approach for detecting RNA flexibility changes in high-throughput structure probing data'. Together they form a unique fingerprint.

Cite this