Descriptive Statistics of the Genome: Phylogenetic Classification of Viruses.
Identifieur interne : 001048 ( PubMed/Corpus ); précédent : 001047; suivant : 001049Descriptive Statistics of the Genome: Phylogenetic Classification of Viruses.
Auteurs : Troy Hernandez ; Jie YangSource :
- Journal of computational biology : a journal of computational molecular cell biology [ 1557-8666 ] ; 2016.
English descriptors
- KwdEn :
- MESH :
- classification : Viruses.
- genetics : Viruses.
- methods : Computational Biology, Sequence Alignment.
- Algorithms, Genome, Viral, Phylogeny, Software.
Abstract
The typical process for classifying and submitting a newly sequenced virus to the NCBI database involves two steps. First, a BLAST search is performed to determine likely family candidates. That is followed by checking the candidate families with the pairwise sequence alignment tool for similar species. The submitter's judgment is then used to determine the most likely species classification. The aim of this article is to show that this process can be automated into a fast, accurate, one-step process using the proposed alignment-free method and properly implemented machine learning techniques. We present a new family of alignment-free vectorizations of the genome, the generalized vector, that maintains the speed of existing alignment-free methods while outperforming all available methods. This new alignment-free vectorization uses the frequency of genomic words (k-mers), as is done in the composition vector, and incorporates descriptive statistics of those k-mers' positional information, as inspired by the natural vector. We analyze five different characterizations of genome similarity using k-nearest neighbor classification and evaluate these on two collections of viruses totaling over 10,000 viruses. We show that our proposed method performs better than, or as well as, other methods at every level of the phylogenetic hierarchy. The data and R code is available upon request.
DOI: 10.1089/cmb.2013.0132
PubMed: 27409298
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First, a BLAST search is performed to determine likely family candidates. That is followed by checking the candidate families with the pairwise sequence alignment tool for similar species. The submitter's judgment is then used to determine the most likely species classification. The aim of this article is to show that this process can be automated into a fast, accurate, one-step process using the proposed alignment-free method and properly implemented machine learning techniques. We present a new family of alignment-free vectorizations of the genome, the generalized vector, that maintains the speed of existing alignment-free methods while outperforming all available methods. This new alignment-free vectorization uses the frequency of genomic words (k-mers), as is done in the composition vector, and incorporates descriptive statistics of those k-mers' positional information, as inspired by the natural vector. We analyze five different characterizations of genome similarity using k-nearest neighbor classification and evaluate these on two collections of viruses totaling over 10,000 viruses. We show that our proposed method performs better than, or as well as, other methods at every level of the phylogenetic hierarchy. The data and R code is available upon request. </div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">27409298</PMID><DateCompleted><Year>2017</Year><Month>10</Month><Day>31</Day></DateCompleted><DateRevised><Year>2017</Year><Month>10</Month><Day>31</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1557-8666</ISSN><JournalIssue CitedMedium="Internet"><Volume>23</Volume><Issue>10</Issue><PubDate><Year>2016</Year><Month>Oct</Month></PubDate></JournalIssue><Title>Journal of computational biology : a journal of computational molecular cell biology</Title><ISOAbbreviation>J. Comput. Biol.</ISOAbbreviation></Journal><ArticleTitle>Descriptive Statistics of the Genome: Phylogenetic Classification of Viruses.</ArticleTitle><Pagination><MedlinePgn>810-20</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1089/cmb.2013.0132</ELocationID><Abstract><AbstractText>The typical process for classifying and submitting a newly sequenced virus to the NCBI database involves two steps. First, a BLAST search is performed to determine likely family candidates. That is followed by checking the candidate families with the pairwise sequence alignment tool for similar species. The submitter's judgment is then used to determine the most likely species classification. The aim of this article is to show that this process can be automated into a fast, accurate, one-step process using the proposed alignment-free method and properly implemented machine learning techniques. We present a new family of alignment-free vectorizations of the genome, the generalized vector, that maintains the speed of existing alignment-free methods while outperforming all available methods. This new alignment-free vectorization uses the frequency of genomic words (k-mers), as is done in the composition vector, and incorporates descriptive statistics of those k-mers' positional information, as inspired by the natural vector. We analyze five different characterizations of genome similarity using k-nearest neighbor classification and evaluate these on two collections of viruses totaling over 10,000 viruses. We show that our proposed method performs better than, or as well as, other methods at every level of the phylogenetic hierarchy. The data and R code is available upon request. </AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Hernandez</LastName><ForeName>Troy</ForeName><Initials>T</Initials><AffiliationInfo><Affiliation>1 Mathematical Sciences Center, Tsinghua University , Beijing, China .</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Yang</LastName><ForeName>Jie</ForeName><Initials>J</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2016</Year><Month>07</Month><Day>13</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>J Comput Biol</MedlineTA><NlmUniqueID>9433358</NlmUniqueID><ISSNLinking>1066-5277</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="N">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D019295" MajorTopicYN="N">Computational Biology</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D016679" MajorTopicYN="Y">Genome, Viral</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D010802" MajorTopicYN="Y">Phylogeny</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D016415" MajorTopicYN="N">Sequence Alignment</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D012984" MajorTopicYN="Y">Software</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D014780" MajorTopicYN="N">Viruses</DescriptorName><QualifierName UI="Q000145" MajorTopicYN="Y">classification</QualifierName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">alignment-free</Keyword><Keyword MajorTopicYN="N">classification</Keyword><Keyword MajorTopicYN="N">machine learning</Keyword><Keyword MajorTopicYN="N">phylogenetics</Keyword><Keyword MajorTopicYN="N">virology</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2016</Year><Month>7</Month><Day>14</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2016</Year><Month>7</Month><Day>14</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2017</Year><Month>11</Month><Day>1</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">27409298</ArticleId><ArticleId IdType="doi">10.1089/cmb.2013.0132</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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