Discovering sequence motifs with arbitrary insertions and deletions.

BIOLOGY IS ENCODED IN MOLECULAR SEQUENCES: deciphering this encoding remains a grand scientific challenge. Functional regions of DNA, RNA, and protein sequences often exhibit characteristic but subtle motifs; thus, computational discovery of motifs in sequences is a fundamental and much-studied prob...

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Main Authors: Martin C Frith, Neil F W Saunders, Bostjan Kobe, Timothy L Bailey
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2008-05-01
Series:PLoS Computational Biology
Online Access:http://europepmc.org/articles/PMC2323616?pdf=render
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spelling doaj-c4bcaa1c08354951850c7ba2749302662020-11-25T01:08:22ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582008-05-0144e100007110.1371/journal.pcbi.1000071Discovering sequence motifs with arbitrary insertions and deletions.Martin C FrithNeil F W SaundersBostjan KobeTimothy L BaileyBIOLOGY IS ENCODED IN MOLECULAR SEQUENCES: deciphering this encoding remains a grand scientific challenge. Functional regions of DNA, RNA, and protein sequences often exhibit characteristic but subtle motifs; thus, computational discovery of motifs in sequences is a fundamental and much-studied problem. However, most current algorithms do not allow for insertions or deletions (indels) within motifs, and the few that do have other limitations. We present a method, GLAM2 (Gapped Local Alignment of Motifs), for discovering motifs allowing indels in a fully general manner, and a companion method GLAM2SCAN for searching sequence databases using such motifs. glam2 is a generalization of the gapless Gibbs sampling algorithm. It re-discovers variable-width protein motifs from the PROSITE database significantly more accurately than the alternative methods PRATT and SAM-T2K. Furthermore, it usefully refines protein motifs from the ELM database: in some cases, the refined motifs make orders of magnitude fewer overpredictions than the original ELM regular expressions. GLAM2 performs respectably on the BAliBASE multiple alignment benchmark, and may be superior to leading multiple alignment methods for "motif-like" alignments with N- and C-terminal extensions. Finally, we demonstrate the use of GLAM2 to discover protein kinase substrate motifs and a gapped DNA motif for the LIM-only transcriptional regulatory complex: using GLAM2SCAN, we identify promising targets for the latter. GLAM2 is especially promising for short protein motifs, and it should improve our ability to identify the protein cleavage sites, interaction sites, post-translational modification attachment sites, etc., that underlie much of biology. It may be equally useful for arbitrarily gapped motifs in DNA and RNA, although fewer examples of such motifs are known at present. GLAM2 is public domain software, available for download at http://bioinformatics.org.au/glam2.http://europepmc.org/articles/PMC2323616?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Martin C Frith
Neil F W Saunders
Bostjan Kobe
Timothy L Bailey
spellingShingle Martin C Frith
Neil F W Saunders
Bostjan Kobe
Timothy L Bailey
Discovering sequence motifs with arbitrary insertions and deletions.
PLoS Computational Biology
author_facet Martin C Frith
Neil F W Saunders
Bostjan Kobe
Timothy L Bailey
author_sort Martin C Frith
title Discovering sequence motifs with arbitrary insertions and deletions.
title_short Discovering sequence motifs with arbitrary insertions and deletions.
title_full Discovering sequence motifs with arbitrary insertions and deletions.
title_fullStr Discovering sequence motifs with arbitrary insertions and deletions.
title_full_unstemmed Discovering sequence motifs with arbitrary insertions and deletions.
title_sort discovering sequence motifs with arbitrary insertions and deletions.
publisher Public Library of Science (PLoS)
series PLoS Computational Biology
issn 1553-734X
1553-7358
publishDate 2008-05-01
description BIOLOGY IS ENCODED IN MOLECULAR SEQUENCES: deciphering this encoding remains a grand scientific challenge. Functional regions of DNA, RNA, and protein sequences often exhibit characteristic but subtle motifs; thus, computational discovery of motifs in sequences is a fundamental and much-studied problem. However, most current algorithms do not allow for insertions or deletions (indels) within motifs, and the few that do have other limitations. We present a method, GLAM2 (Gapped Local Alignment of Motifs), for discovering motifs allowing indels in a fully general manner, and a companion method GLAM2SCAN for searching sequence databases using such motifs. glam2 is a generalization of the gapless Gibbs sampling algorithm. It re-discovers variable-width protein motifs from the PROSITE database significantly more accurately than the alternative methods PRATT and SAM-T2K. Furthermore, it usefully refines protein motifs from the ELM database: in some cases, the refined motifs make orders of magnitude fewer overpredictions than the original ELM regular expressions. GLAM2 performs respectably on the BAliBASE multiple alignment benchmark, and may be superior to leading multiple alignment methods for "motif-like" alignments with N- and C-terminal extensions. Finally, we demonstrate the use of GLAM2 to discover protein kinase substrate motifs and a gapped DNA motif for the LIM-only transcriptional regulatory complex: using GLAM2SCAN, we identify promising targets for the latter. GLAM2 is especially promising for short protein motifs, and it should improve our ability to identify the protein cleavage sites, interaction sites, post-translational modification attachment sites, etc., that underlie much of biology. It may be equally useful for arbitrarily gapped motifs in DNA and RNA, although fewer examples of such motifs are known at present. GLAM2 is public domain software, available for download at http://bioinformatics.org.au/glam2.
url http://europepmc.org/articles/PMC2323616?pdf=render
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