An Affinity Propagation-Based DNA Motif Discovery Algorithm

An Affinity Propagation-Based DNA Motif Discovery Algorithm

The planted (l, d) motif search (PMS) is one of the fundamental problems in bioinformatics, which plays an important role in locating transcription factor binding sites (TFBSs) in DNA sequences. Nowadays, identifying weak motifs and reducing the effect of local optimum are still important but challe...

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Main Authors: Sun, Chunxiao, Huo, Hongwei, Yu, Qiang, Guo, Haitao, Sun, Zhigang
Format: Online
Language:English
Published: Hindawi Publishing Corporation 2015
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4547008/
id pubmed-4547008
recordtype oai_dc
spelling pubmed-45470082015-09-07 An Affinity Propagation-Based DNA Motif Discovery Algorithm Sun, Chunxiao Huo, Hongwei Yu, Qiang Guo, Haitao Sun, Zhigang Research Article The planted (l, d) motif search (PMS) is one of the fundamental problems in bioinformatics, which plays an important role in locating transcription factor binding sites (TFBSs) in DNA sequences. Nowadays, identifying weak motifs and reducing the effect of local optimum are still important but challenging tasks for motif discovery. To solve the tasks, we propose a new algorithm, APMotif, which first applies the Affinity Propagation (AP) clustering in DNA sequences to produce informative and good candidate motifs and then employs Expectation Maximization (EM) refinement to obtain the optimal motifs from the candidate motifs. Experimental results both on simulated data sets and real biological data sets show that APMotif usually outperforms four other widely used algorithms in terms of high prediction accuracy. Hindawi Publishing Corporation 2015 2015-08-10 /pmc/articles/PMC4547008/ /pubmed/26347887 http://dx.doi.org/10.1155/2015/853461 Text en Copyright © 2015 Chunxiao Sun et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
repository_type Open Access Journal
institution_category Foreign Institution
institution US National Center for Biotechnology Information
building NCBI PubMed
collection Online Access
language English
format Online
author Sun, Chunxiao
Huo, Hongwei
Yu, Qiang
Guo, Haitao
Sun, Zhigang
spellingShingle Sun, Chunxiao
Huo, Hongwei
Yu, Qiang
Guo, Haitao
Sun, Zhigang
An Affinity Propagation-Based DNA Motif Discovery Algorithm
author_facet Sun, Chunxiao
Huo, Hongwei
Yu, Qiang
Guo, Haitao
Sun, Zhigang
author_sort Sun, Chunxiao
title An Affinity Propagation-Based DNA Motif Discovery Algorithm
title_short An Affinity Propagation-Based DNA Motif Discovery Algorithm
title_full An Affinity Propagation-Based DNA Motif Discovery Algorithm
title_fullStr An Affinity Propagation-Based DNA Motif Discovery Algorithm
title_full_unstemmed An Affinity Propagation-Based DNA Motif Discovery Algorithm
title_sort affinity propagation-based dna motif discovery algorithm
description The planted (l, d) motif search (PMS) is one of the fundamental problems in bioinformatics, which plays an important role in locating transcription factor binding sites (TFBSs) in DNA sequences. Nowadays, identifying weak motifs and reducing the effect of local optimum are still important but challenging tasks for motif discovery. To solve the tasks, we propose a new algorithm, APMotif, which first applies the Affinity Propagation (AP) clustering in DNA sequences to produce informative and good candidate motifs and then employs Expectation Maximization (EM) refinement to obtain the optimal motifs from the candidate motifs. Experimental results both on simulated data sets and real biological data sets show that APMotif usually outperforms four other widely used algorithms in terms of high prediction accuracy.
publisher Hindawi Publishing Corporation
publishDate 2015
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4547008/
_version_ 1613262946999205888

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