MersV1, PubMed, Corpus, bibRecord, 001F60

Physicochemical property distributions for accurate and rapid pairwise protein homology detection.

Identifieur interne : 001F60 ( PubMed/Corpus ); précédent : 001F59; suivant : 001F61

Physicochemical property distributions for accurate and rapid pairwise protein homology detection.

Auteurs : Bobbie-Jo M. Webb-Robertson ; Kyle G. Ratuiste ; Christopher S. Oehmen

Source :

BMC bioinformatics [ 1471-2105 ] ; 2010.

RBID : pubmed:20302613

English descriptors

KwdEn :
- Computational Biology (methods), Databases, Protein, Pattern Recognition, Automated, Proteins (chemistry), Sequence Homology, Amino Acid.
MESH :
- chemical , chemistry : Proteins.
- methods : Computational Biology.
- Databases, Protein, Pattern Recognition, Automated, Sequence Homology, Amino Acid.

Abstract

The challenge of remote homology detection is that many evolutionarily related sequences have very little similarity at the amino acid level. Kernel-based discriminative methods, such as support vector machines (SVMs), that use vector representations of sequences derived from sequence properties have been shown to have superior accuracy when compared to traditional approaches for the task of remote homology detection.

DOI: 10.1186/1471-2105-11-145
PubMed: 20302613

Links to Exploration step

pubmed:20302613

Le document en format XML

<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Physicochemical property distributions for accurate and rapid pairwise protein homology detection.</title>
<author><name sortKey="Webb Robertson, Bobbie Jo M" sort="Webb Robertson, Bobbie Jo M" uniqKey="Webb Robertson B" first="Bobbie-Jo M" last="Webb-Robertson">Bobbie-Jo M. Webb-Robertson</name>
<affiliation><nlm:affiliation>Computational Biology and Bioinformatics, Pacific Northwest National Laboratory, Richland, WA 99352, USA. bj@pnl.gov</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Ratuiste, Kyle G" sort="Ratuiste, Kyle G" uniqKey="Ratuiste K" first="Kyle G" last="Ratuiste">Kyle G. Ratuiste</name>
</author>
<author><name sortKey="Oehmen, Christopher S" sort="Oehmen, Christopher S" uniqKey="Oehmen C" first="Christopher S" last="Oehmen">Christopher S. Oehmen</name>
</author>
</titleStmt>
<publicationStmt><idno type="wicri:source">PubMed</idno>
<date when="2010">2010</date>
<idno type="RBID">pubmed:20302613</idno>
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<idno type="doi">10.1186/1471-2105-11-145</idno>
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<sourceDesc><biblStruct><analytic><title xml:lang="en">Physicochemical property distributions for accurate and rapid pairwise protein homology detection.</title>
<author><name sortKey="Webb Robertson, Bobbie Jo M" sort="Webb Robertson, Bobbie Jo M" uniqKey="Webb Robertson B" first="Bobbie-Jo M" last="Webb-Robertson">Bobbie-Jo M. Webb-Robertson</name>
<affiliation><nlm:affiliation>Computational Biology and Bioinformatics, Pacific Northwest National Laboratory, Richland, WA 99352, USA. bj@pnl.gov</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Ratuiste, Kyle G" sort="Ratuiste, Kyle G" uniqKey="Ratuiste K" first="Kyle G" last="Ratuiste">Kyle G. Ratuiste</name>
</author>
<author><name sortKey="Oehmen, Christopher S" sort="Oehmen, Christopher S" uniqKey="Oehmen C" first="Christopher S" last="Oehmen">Christopher S. Oehmen</name>
</author>
</analytic>
<series><title level="j">BMC bioinformatics</title>
<idno type="eISSN">1471-2105</idno>
<imprint><date when="2010" type="published">2010</date>
</imprint>
</series>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Computational Biology (methods)</term>
<term>Databases, Protein</term>
<term>Pattern Recognition, Automated</term>
<term>Proteins (chemistry)</term>
<term>Sequence Homology, Amino Acid</term>
</keywords>
<keywords scheme="MESH" type="chemical" qualifier="chemistry" xml:lang="en"><term>Proteins</term>
</keywords>
<keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Computational Biology</term>
</keywords>
<keywords scheme="MESH" xml:lang="en"><term>Databases, Protein</term>
<term>Pattern Recognition, Automated</term>
<term>Sequence Homology, Amino Acid</term>
</keywords>
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<front><div type="abstract" xml:lang="en">The challenge of remote homology detection is that many evolutionarily related sequences have very little similarity at the amino acid level. Kernel-based discriminative methods, such as support vector machines (SVMs), that use vector representations of sequences derived from sequence properties have been shown to have superior accuracy when compared to traditional approaches for the task of remote homology detection.</div>
</front>
</TEI>
<pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">20302613</PMID>
<DateCompleted><Year>2010</Year>
<Month>05</Month>
<Day>11</Day>
</DateCompleted>
<DateRevised><Year>2018</Year>
<Month>11</Month>
<Day>13</Day>
</DateRevised>
<Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1471-2105</ISSN>
<JournalIssue CitedMedium="Internet"><Volume>11</Volume>
<PubDate><Year>2010</Year>
<Month>Mar</Month>
<Day>19</Day>
</PubDate>
</JournalIssue>
<Title>BMC bioinformatics</Title>
<ISOAbbreviation>BMC Bioinformatics</ISOAbbreviation>
</Journal>
<ArticleTitle>Physicochemical property distributions for accurate and rapid pairwise protein homology detection.</ArticleTitle>
<Pagination><MedlinePgn>145</MedlinePgn>
</Pagination>
<ELocationID EIdType="doi" ValidYN="Y">10.1186/1471-2105-11-145</ELocationID>
<Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">The challenge of remote homology detection is that many evolutionarily related sequences have very little similarity at the amino acid level. Kernel-based discriminative methods, such as support vector machines (SVMs), that use vector representations of sequences derived from sequence properties have been shown to have superior accuracy when compared to traditional approaches for the task of remote homology detection.</AbstractText>
<AbstractText Label="RESULTS" NlmCategory="RESULTS">We introduce a new method for feature vector representation based on the physicochemical properties of the primary protein sequence. A distribution of physicochemical property scores are assembled from 4-mers of the sequence and normalized based on the null distribution of the property over all possible 4-mers. With this approach there is little computational cost associated with the transformation of the protein into feature space, and overall performance in terms of remote homology detection is comparable with current state-of-the-art methods. We demonstrate that the features can be used for the task of pairwise remote homology detection with improved accuracy versus sequence-based methods such as BLAST and other feature-based methods of similar computational cost.</AbstractText>
<AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">A protein feature method based on physicochemical properties is a viable approach for extracting features in a computationally inexpensive manner while retaining the sensitivity of SVM protein homology detection. Furthermore, identifying features that can be used for generic pairwise homology detection in lieu of family-based homology detection is important for applications such as large database searches and comparative genomics.</AbstractText>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Webb-Robertson</LastName>
<ForeName>Bobbie-Jo M</ForeName>
<Initials>BJ</Initials>
<AffiliationInfo><Affiliation>Computational Biology and Bioinformatics, Pacific Northwest National Laboratory, Richland, WA 99352, USA. bj@pnl.gov</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Ratuiste</LastName>
<ForeName>Kyle G</ForeName>
<Initials>KG</Initials>
</Author>
<Author ValidYN="Y"><LastName>Oehmen</LastName>
<ForeName>Christopher S</ForeName>
<Initials>CS</Initials>
</Author>
</AuthorList>
<Language>eng</Language>
<PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType>
<PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType>
<PublicationType UI="D013486">Research Support, U.S. Gov't, Non-P.H.S.</PublicationType>
</PublicationTypeList>
<ArticleDate DateType="Electronic"><Year>2010</Year>
<Month>03</Month>
<Day>19</Day>
</ArticleDate>
</Article>
<MedlineJournalInfo><Country>England</Country>
<MedlineTA>BMC Bioinformatics</MedlineTA>
<NlmUniqueID>100965194</NlmUniqueID>
<ISSNLinking>1471-2105</ISSNLinking>
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<ChemicalList><Chemical><RegistryNumber>0</RegistryNumber>
<NameOfSubstance UI="D011506">Proteins</NameOfSubstance>
</Chemical>
</ChemicalList>
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<MeshHeadingList><MeshHeading><DescriptorName UI="D019295" MajorTopicYN="N">Computational Biology</DescriptorName>
<QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D030562" MajorTopicYN="N">Databases, Protein</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D010363" MajorTopicYN="N">Pattern Recognition, Automated</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D011506" MajorTopicYN="N">Proteins</DescriptorName>
<QualifierName UI="Q000737" MajorTopicYN="Y">chemistry</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D017386" MajorTopicYN="Y">Sequence Homology, Amino Acid</DescriptorName>
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<Month>10</Month>
<Day>24</Day>
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<PubMedPubDate PubStatus="accepted"><Year>2010</Year>
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<Day>19</Day>
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Physicochemical property distributions for accurate and rapid pairwise protein homology detection.

Physicochemical property distributions for accurate and rapid pairwise protein homology detection.

Source :

English descriptors

Abstract

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri

Pour générer des pages wiki