DotAligner: Identification and clustering of RNA structure motifs

Martin A. Smith; Stefan E. Seemann; Xiu Cheng Quek; John S. Mattick

doi:10.1186/s13059-017-1371-3

DotAligner: Identification and clustering of RNA structure motifs

Martin A. Smith^*, Stefan E. Seemann, Xiu Cheng Quek, John S. Mattick

^*Corresponding author af dette arbejde

8 Citationer (Scopus)

48 Downloads (Pure)

Abstract

The diversity of processed transcripts in eukaryotic genomes poses a challenge for the classification of their biological functions. Sparse sequence conservation in non-coding sequences and the unreliable nature of RNA structure predictions further exacerbate this conundrum. Here, we describe a computational method, DotAligner, for the unsupervised discovery and classification of homologous RNA structure motifs from a set of sequences of interest. Our approach outperforms comparable algorithms at clustering known RNA structure families, both in speed and accuracy. It identifies clusters of known and novel structure motifs from ENCODE immunoprecipitation data for 44 RNA-binding proteins.

Originalsprog	Engelsk
Artikelnummer	244
Tidsskrift	Genome Biology
Vol/bind	18
Udgave nummer	1
Antal sider	12
ISSN	1474-7596
DOI	https://doi.org/10.1186/s13059-017-1371-3
Status	Udgivet - dec. 2017

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10.1186/s13059-017-1371-3Licens: CC BY

DotAligner: identification and clustering of RNA structure motifsForlagets udgivne version, 2,39 MBLicens: CC BY

Citationsformater

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abstract = "The diversity of processed transcripts in eukaryotic genomes poses a challenge for the classification of their biological functions. Sparse sequence conservation in non-coding sequences and the unreliable nature of RNA structure predictions further exacerbate this conundrum. Here, we describe a computational method, DotAligner, for the unsupervised discovery and classification of homologous RNA structure motifs from a set of sequences of interest. Our approach outperforms comparable algorithms at clustering known RNA structure families, both in speed and accuracy. It identifies clusters of known and novel structure motifs from ENCODE immunoprecipitation data for 44 RNA-binding proteins.",

keywords = "Functional genome annotation, Functions of RNA structures, Machine learning, Regulation by non-coding RNAs, RNA structure clustering, RNA-protein interactions",

author = "Smith, {Martin A.} and Seemann, {Stefan E.} and Quek, {Xiu Cheng} and Mattick, {John S.}",

year = "2017",

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T2 - Identification and clustering of RNA structure motifs

AU - Smith, Martin A.

AU - Seemann, Stefan E.

AU - Quek, Xiu Cheng

AU - Mattick, John S.

PY - 2017/12

Y1 - 2017/12

N2 - The diversity of processed transcripts in eukaryotic genomes poses a challenge for the classification of their biological functions. Sparse sequence conservation in non-coding sequences and the unreliable nature of RNA structure predictions further exacerbate this conundrum. Here, we describe a computational method, DotAligner, for the unsupervised discovery and classification of homologous RNA structure motifs from a set of sequences of interest. Our approach outperforms comparable algorithms at clustering known RNA structure families, both in speed and accuracy. It identifies clusters of known and novel structure motifs from ENCODE immunoprecipitation data for 44 RNA-binding proteins.

AB - The diversity of processed transcripts in eukaryotic genomes poses a challenge for the classification of their biological functions. Sparse sequence conservation in non-coding sequences and the unreliable nature of RNA structure predictions further exacerbate this conundrum. Here, we describe a computational method, DotAligner, for the unsupervised discovery and classification of homologous RNA structure motifs from a set of sequences of interest. Our approach outperforms comparable algorithms at clustering known RNA structure families, both in speed and accuracy. It identifies clusters of known and novel structure motifs from ENCODE immunoprecipitation data for 44 RNA-binding proteins.

KW - Functional genome annotation

KW - Functions of RNA structures

KW - Machine learning

KW - Regulation by non-coding RNAs

KW - RNA structure clustering

KW - RNA-protein interactions

U2 - 10.1186/s13059-017-1371-3

DO - 10.1186/s13059-017-1371-3

M3 - Journal article

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SN - 1474-7596

VL - 18

JO - Genome Biology

JF - Genome Biology

IS - 1

M1 - 244

ER -

DotAligner: Identification and clustering of RNA structure motifs

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