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 for this work

8 Citations (Scopus)

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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.

Original language	English
Article number	244
Journal	Genome Biology
Volume	18
Issue number	1
Number of pages	12
ISSN	1474-7596
DOIs	https://doi.org/10.1186/s13059-017-1371-3
Publication status	Published - Dec 2017

Keywords

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

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

DotAligner: identification and clustering of RNA structure motifsFinal published version, 2.39 MBLicence: CC BY

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title = "DotAligner: Identification and clustering of RNA structure motifs",

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",

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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

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DO - 10.1186/s13059-017-1371-3

M3 - Journal article

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SN - 1474-7596

VL - 18

JO - Genome Biology (Online Edition)

JF - Genome Biology (Online Edition)

IS - 1

M1 - 244

ER -

DotAligner: Identification and clustering of RNA structure motifs

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Keywords

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