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

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

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 det...

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Main Authors: Paolo Marangio, Ka Ying Toby Law, Guido Sanguinetti, Sander Granneman
Format: Article
Language:English
Published: BMC 2021-05-01
Series:Genome Biology
Subjects:
Hidden Markov model
High-throughput RNA structure probing
RNA structural changes
Online Access:https://doi.org/10.1186/s13059-021-02379-y
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spelling doaj-29b52baaa3bf4beb9df2897430b642ce2021-05-30T11:48:42ZengBMCGenome Biology1474-760X2021-05-0122112110.1186/s13059-021-02379-ydiffBUM-HMM: a robust statistical modeling approach for detecting RNA flexibility changes in high-throughput structure probing dataPaolo Marangio0Ka Ying Toby Law1Guido Sanguinetti2Sander Granneman3School of Informatics, The University of EdinburghCentre for Synthetic and Systems Biology, The University of EdinburghCentre for Synthetic and Systems Biology, The University of EdinburghCentre for Synthetic and Systems Biology, The University of EdinburghAbstract 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.https://doi.org/10.1186/s13059-021-02379-yHidden Markov modelHigh-throughput RNA structure probingRNA structural changes
collection DOAJ
language English
format Article
sources DOAJ
author Paolo Marangio
Ka Ying Toby Law
Guido Sanguinetti
Sander Granneman
spellingShingle Paolo Marangio
Ka Ying Toby Law
Guido Sanguinetti
Sander Granneman
diffBUM-HMM: a robust statistical modeling approach for detecting RNA flexibility changes in high-throughput structure probing data
Genome Biology
Hidden Markov model
High-throughput RNA structure probing
RNA structural changes
author_facet Paolo Marangio
Ka Ying Toby Law
Guido Sanguinetti
Sander Granneman
author_sort Paolo Marangio
title diffBUM-HMM: a robust statistical modeling approach for detecting RNA flexibility changes in high-throughput structure probing data
title_short diffBUM-HMM: a robust statistical modeling approach for detecting RNA flexibility changes in high-throughput structure probing data
title_full diffBUM-HMM: a robust statistical modeling approach for detecting RNA flexibility changes in high-throughput structure probing data
title_fullStr diffBUM-HMM: a robust statistical modeling approach for detecting RNA flexibility changes in high-throughput structure probing data
title_full_unstemmed diffBUM-HMM: a robust statistical modeling approach for detecting RNA flexibility changes in high-throughput structure probing data
title_sort diffbum-hmm: a robust statistical modeling approach for detecting rna flexibility changes in high-throughput structure probing data
publisher BMC
series Genome Biology
issn 1474-760X
publishDate 2021-05-01
description 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.
topic Hidden Markov model
High-throughput RNA structure probing
RNA structural changes
url https://doi.org/10.1186/s13059-021-02379-y
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AT kayingtobylaw diffbumhmmarobuststatisticalmodelingapproachfordetectingrnaflexibilitychangesinhighthroughputstructureprobingdata
AT guidosanguinetti diffbumhmmarobuststatisticalmodelingapproachfordetectingrnaflexibilitychangesinhighthroughputstructureprobingdata
AT sandergranneman diffbumhmmarobuststatisticalmodelingapproachfordetectingrnaflexibilitychangesinhighthroughputstructureprobingdata
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