DotAligner: Identification and clustering of RNA structure motifs

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Standard

DotAligner : Identification and clustering of RNA structure motifs. / Smith, Martin A.; Seemann, Stefan E.; Quek, Xiu Cheng; Mattick, John S.

In: Genome Biology, Vol. 18, No. 1, 244, 12.2017.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Smith, MA, Seemann, SE, Quek, XC & Mattick, JS 2017, 'DotAligner: Identification and clustering of RNA structure motifs', Genome Biology, vol. 18, no. 1, 244. https://doi.org/10.1186/s13059-017-1371-3

APA

Smith, M. A., Seemann, S. E., Quek, X. C., & Mattick, J. S. (2017). DotAligner: Identification and clustering of RNA structure motifs. Genome Biology, 18(1), [244]. https://doi.org/10.1186/s13059-017-1371-3

Vancouver

Smith MA, Seemann SE, Quek XC, Mattick JS. DotAligner: Identification and clustering of RNA structure motifs. Genome Biology. 2017 Dec;18(1). 244. https://doi.org/10.1186/s13059-017-1371-3

Author

Smith, Martin A. ; Seemann, Stefan E. ; Quek, Xiu Cheng ; Mattick, John S. / DotAligner : Identification and clustering of RNA structure motifs. In: Genome Biology. 2017 ; Vol. 18, No. 1.

Bibtex

@article{ceffe562173248e48743dc549d9e171e,
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",
author = "Smith, {Martin A.} and Seemann, {Stefan E.} and Quek, {Xiu Cheng} and Mattick, {John S.}",
year = "2017",
month = dec,
doi = "10.1186/s13059-017-1371-3",
language = "English",
volume = "18",
journal = "Genome Biology (Online Edition)",
issn = "1474-7596",
publisher = "BioMed Central Ltd.",
number = "1",

}

RIS

TY - JOUR

T1 - DotAligner

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

C2 - 29284541

AN - SCOPUS:85039750142

VL - 18

JO - Genome Biology (Online Edition)

JF - Genome Biology (Online Edition)

SN - 1474-7596

IS - 1

M1 - 244

ER -

ID: 188367767