A study on the application of topic models to motif finding algorithms.
Identifieur interne : 000D96 ( PubMed/Curation ); précédent : 000D95; suivant : 000D97A study on the application of topic models to motif finding algorithms.
Auteurs : Josep Basha Gutierrez [Japon] ; Kenta Nakai [Japon]Source :
- BMC bioinformatics [ 1471-2105 ] ; 2016.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- chemical , metabolism : Transcription Factors.
- genetics : Nucleotide Motifs, Regulatory Sequences, Nucleic Acid.
- methods : Computational Biology, Sequence Analysis, DNA.
- Algorithms, Binding Sites, Humans, Models, Theoretical, Monte Carlo Method, Protein Binding.
Abstract
Topic models are statistical algorithms which try to discover the structure of a set of documents according to the abstract topics contained in them. Here we try to apply this approach to the discovery of the structure of the transcription factor binding sites (TFBS) contained in a set of biological sequences, which is a fundamental problem in molecular biology research for the understanding of transcriptional regulation. Here we present two methods that make use of topic models for motif finding. First, we developed an algorithm in which first a set of biological sequences are treated as text documents, and the k-mers contained in them as words, to then build a correlated topic model (CTM) and iteratively reduce its perplexity. We also used the perplexity measurement of CTMs to improve our previous algorithm based on a genetic algorithm and several statistical coefficients.
DOI: 10.1186/s12859-016-1364-3
PubMed: 28155646
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algorithms.</title><author><name sortKey="Basha Gutierrez, Josep" sort="Basha Gutierrez, Josep" uniqKey="Basha Gutierrez J" first="Josep" last="Basha Gutierrez">Josep Basha Gutierrez</name><affiliation wicri:level="1"><nlm:affiliation>Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 277-8561, Chiba, Japan.</nlm:affiliation><country xml:lang="fr">Japon</country><wicri:regionArea>Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 277-8561, Chiba</wicri:regionArea></affiliation></author><author><name sortKey="Nakai, Kenta" sort="Nakai, Kenta" uniqKey="Nakai K" first="Kenta" last="Nakai">Kenta Nakai</name><affiliation wicri:level="1"><nlm:affiliation>Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 277-8561, Chiba, Japan. knakai@ims.u-tokyo.ac.jp.</nlm:affiliation><country 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()</term><term>Biologie informatique ()</term><term>Facteurs de transcription (métabolisme)</term><term>Humains</term><term>Liaison aux protéines</term><term>Modèles théoriques</term><term>Motifs nucléotidiques (génétique)</term><term>Méthode de Monte-Carlo</term><term>Sites de fixation</term><term>Séquences d'acides nucléiques régulatrices (génétique)</term></keywords><keywords scheme="MESH" type="chemical" qualifier="metabolism" xml:lang="en"><term>Transcription Factors</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Nucleotide Motifs</term><term>Regulatory Sequences, Nucleic Acid</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Motifs nucléotidiques</term><term>Séquences d'acides nucléiques régulatrices</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Computational Biology</term><term>Sequence Analysis, DNA</term></keywords><keywords scheme="MESH" qualifier="métabolisme" xml:lang="fr"><term>Facteurs de transcription</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Binding Sites</term><term>Humans</term><term>Models, Theoretical</term><term>Monte Carlo Method</term><term>Protein Binding</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Analyse de séquence d'ADN</term><term>Biologie informatique</term><term>Humains</term><term>Liaison aux protéines</term><term>Modèles théoriques</term><term>Méthode de Monte-Carlo</term><term>Sites de fixation</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Topic models are statistical algorithms which try to discover the structure of a set of documents according to the abstract topics contained in them. Here we try to apply this approach to the discovery of the structure of the transcription factor binding sites (TFBS) contained in a set of biological sequences, which is a fundamental problem in molecular biology research for the understanding of transcriptional regulation. Here we present two methods that make use of topic models for motif finding. First, we developed an algorithm in which first a set of biological sequences are treated as text documents, and the k-mers contained in them as words, to then build a correlated topic model (CTM) and iteratively reduce its perplexity. We also used the perplexity measurement of CTMs to improve our previous algorithm based on a genetic algorithm and several statistical coefficients.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">28155646</PMID><DateCompleted><Year>2017</Year><Month>08</Month><Day>17</Day></DateCompleted><DateRevised><Year>2018</Year><Month>12</Month><Day>02</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1471-2105</ISSN><JournalIssue CitedMedium="Internet"><Volume>17</Volume><Issue>Suppl 19</Issue><PubDate><Year>2016</Year><Month>Dec</Month><Day>22</Day></PubDate></JournalIssue><Title>BMC bioinformatics</Title><ISOAbbreviation>BMC Bioinformatics</ISOAbbreviation></Journal><ArticleTitle>A study on the application of topic models to motif finding algorithms.</ArticleTitle><Pagination><MedlinePgn>502</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/s12859-016-1364-3</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Topic models are statistical algorithms which try to discover the structure of a set of documents according to the abstract topics contained in them. Here we try to apply this approach to the discovery of the structure of the transcription factor binding sites (TFBS) contained in a set of biological sequences, which is a fundamental problem in molecular biology research for the understanding of transcriptional regulation. Here we present two methods that make use of topic models for motif finding. First, we developed an algorithm in which first a set of biological sequences are treated as text documents, and the k-mers contained in them as words, to then build a correlated topic model (CTM) and iteratively reduce its perplexity. We also used the perplexity measurement of CTMs to improve our previous algorithm based on a genetic algorithm and several statistical coefficients.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">The algorithms were tested with 56 data sets from four different species and compared to 14 other methods by the use of several coefficients both at nucleotide and site level. The results of our first approach showed a performance comparable to the other methods studied, especially at site level and in sensitivity scores, in which it scored better than any of the 14 existing tools. In the case of our previous algorithm, the new approach with the addition of the perplexity measurement clearly outperformed all of the other methods in sensitivity, both at nucleotide and site level, and in overall performance at site level.</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">The statistics obtained show that the performance of a motif finding method based on the use of a CTM is satisfying enough to conclude that the application of topic models is a valid method for developing motif finding algorithms. Moreover, the addition of topic models to a previously developed method dramatically increased its performance, suggesting that this combined algorithm can be a useful tool to successfully predict motifs in different kinds of sets of DNA sequences.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Basha Gutierrez</LastName><ForeName>Josep</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 277-8561, Chiba, Japan.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Human Genome Center, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokane-dai, Minato-ku, 108-8639, Tokyo, Japan.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Nakai</LastName><ForeName>Kenta</ForeName><Initials>K</Initials><AffiliationInfo><Affiliation>Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 277-8561, Chiba, Japan. knakai@ims.u-tokyo.ac.jp.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Human Genome Center, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokane-dai, Minato-ku, 108-8639, Tokyo, Japan. knakai@ims.u-tokyo.ac.jp.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2016</Year><Month>12</Month><Day>22</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>BMC Bioinformatics</MedlineTA><NlmUniqueID>100965194</NlmUniqueID><ISSNLinking>1471-2105</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D014157">Transcription Factors</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="Y">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D001665" MajorTopicYN="N">Binding Sites</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D019295" MajorTopicYN="N">Computational Biology</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008962" MajorTopicYN="Y">Models, Theoretical</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D009010" MajorTopicYN="N">Monte Carlo Method</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D059372" MajorTopicYN="N">Nucleotide Motifs</DescriptorName><QualifierName UI="Q000235" 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