Nephele: genotyping via complete composition vectors and MapReduce.
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Auteurs : Marc E. Colosimo [États-Unis] ; Matthew W. Peterson ; Scott Mardis ; Lynette HirschmanSource :
- Source code for biology and medicine [ 1751-0473 ] ; 2011.
Abstract
Current sequencing technology makes it practical to sequence many samples of a given organism, raising new challenges for the processing and interpretation of large genomics data sets with associated metadata. Traditional computational phylogenetic methods are ideal for studying the evolution of gene/protein families and using those to infer the evolution of an organism, but are less than ideal for the study of the whole organism mainly due to the presence of insertions/deletions/rearrangements. These methods provide the researcher with the ability to group a set of samples into distinct genotypic groups based on sequence similarity, which can then be associated with metadata, such as host information, pathogenicity, and time or location of occurrence. Genotyping is critical to understanding, at a genomic level, the origin and spread of infectious diseases. Increasingly, genotyping is coming into use for disease surveillance activities, as well as for microbial forensics. The classic genotyping approach has been based on phylogenetic analysis, starting with a multiple sequence alignment. Genotypes are then established by expert examination of phylogenetic trees. However, these traditional single-processor methods are suboptimal for rapidly growing sequence datasets being generated by next-generation DNA sequencing machines, because they increase in computational complexity quickly with the number of sequences.
DOI: 10.1186/1751-0473-6-13
PubMed: 21851626
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Peterson</name></author><author><name sortKey="Mardis, Scott" sort="Mardis, Scott" uniqKey="Mardis S" first="Scott" last="Mardis">Scott Mardis</name></author><author><name sortKey="Hirschman, Lynette" sort="Hirschman, Lynette" uniqKey="Hirschman L" first="Lynette" last="Hirschman">Lynette Hirschman</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2011">2011</date><idno type="RBID">pubmed:21851626</idno><idno type="pmid">21851626</idno><idno type="doi">10.1186/1751-0473-6-13</idno><idno type="wicri:Area/PubMed/Corpus">001E51</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">001E51</idno><idno type="wicri:Area/PubMed/Curation">001E51</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">001E51</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Nephele: genotyping via complete composition vectors and MapReduce.</title><author><name sortKey="Colosimo, Marc E" sort="Colosimo, Marc E" uniqKey="Colosimo M" first="Marc E" last="Colosimo">Marc E. Colosimo</name><affiliation wicri:level="1"><nlm:affiliation>The MITRE Corporation, 202 Burlington Rd, Bedford MA 01730, USA. mcolosimo@mitre.org.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>The MITRE Corporation, 202 Burlington Rd, Bedford MA 01730</wicri:regionArea></affiliation></author><author><name sortKey="Peterson, Matthew W" sort="Peterson, Matthew W" uniqKey="Peterson M" first="Matthew W" last="Peterson">Matthew W. Peterson</name></author><author><name sortKey="Mardis, Scott" sort="Mardis, Scott" uniqKey="Mardis S" first="Scott" last="Mardis">Scott Mardis</name></author><author><name sortKey="Hirschman, Lynette" sort="Hirschman, Lynette" uniqKey="Hirschman L" first="Lynette" last="Hirschman">Lynette Hirschman</name></author></analytic><series><title level="j">Source code for biology and medicine</title><idno type="eISSN">1751-0473</idno><imprint><date when="2011" type="published">2011</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Current sequencing technology makes it practical to sequence many samples of a given organism, raising new challenges for the processing and interpretation of large genomics data sets with associated metadata. Traditional computational phylogenetic methods are ideal for studying the evolution of gene/protein families and using those to infer the evolution of an organism, but are less than ideal for the study of the whole organism mainly due to the presence of insertions/deletions/rearrangements. These methods provide the researcher with the ability to group a set of samples into distinct genotypic groups based on sequence similarity, which can then be associated with metadata, such as host information, pathogenicity, and time or location of occurrence. Genotyping is critical to understanding, at a genomic level, the origin and spread of infectious diseases. Increasingly, genotyping is coming into use for disease surveillance activities, as well as for microbial forensics. The classic genotyping approach has been based on phylogenetic analysis, starting with a multiple sequence alignment. Genotypes are then established by expert examination of phylogenetic trees. However, these traditional single-processor methods are suboptimal for rapidly growing sequence datasets being generated by next-generation DNA sequencing machines, because they increase in computational complexity quickly with the number of sequences.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">21851626</PMID><DateCompleted><Year>2011</Year><Month>11</Month><Day>10</Day></DateCompleted><DateRevised><Year>2018</Year><Month>11</Month><Day>13</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1751-0473</ISSN><JournalIssue CitedMedium="Internet"><Volume>6</Volume><PubDate><Year>2011</Year><Month>Aug</Month><Day>18</Day></PubDate></JournalIssue><Title>Source code for biology and medicine</Title><ISOAbbreviation>Source Code Biol Med</ISOAbbreviation></Journal><ArticleTitle>Nephele: genotyping via complete composition vectors and MapReduce.</ArticleTitle><Pagination><MedlinePgn>13</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/1751-0473-6-13</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Current sequencing technology makes it practical to sequence many samples of a given organism, raising new challenges for the processing and interpretation of large genomics data sets with associated metadata. Traditional computational phylogenetic methods are ideal for studying the evolution of gene/protein families and using those to infer the evolution of an organism, but are less than ideal for the study of the whole organism mainly due to the presence of insertions/deletions/rearrangements. These methods provide the researcher with the ability to group a set of samples into distinct genotypic groups based on sequence similarity, which can then be associated with metadata, such as host information, pathogenicity, and time or location of occurrence. Genotyping is critical to understanding, at a genomic level, the origin and spread of infectious diseases. Increasingly, genotyping is coming into use for disease surveillance activities, as well as for microbial forensics. The classic genotyping approach has been based on phylogenetic analysis, starting with a multiple sequence alignment. Genotypes are then established by expert examination of phylogenetic trees. However, these traditional single-processor methods are suboptimal for rapidly growing sequence datasets being generated by next-generation DNA sequencing machines, because they increase in computational complexity quickly with the number of sequences.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">Nephele is a suite of tools that uses the complete composition vector algorithm to represent each sequence in the dataset as a vector derived from its constituent k-mers by passing the need for multiple sequence alignment, and affinity propagation clustering to group the sequences into genotypes based on a distance measure over the vectors. Our methods produce results that correlate well with expert-defined clades or genotypes, at a fraction of the computational cost of traditional phylogenetic methods run on traditional hardware. Nephele can use the open-source Hadoop implementation of MapReduce to parallelize execution using multiple compute nodes. We were able to generate a neighbour-joined tree of over 10,000 16S samples in less than 2 hours.</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">We conclude that using Nephele can substantially decrease the processing time required for generating genotype trees of tens to hundreds of organisms at genome scale sequence coverage.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Colosimo</LastName><ForeName>Marc E</ForeName><Initials>ME</Initials><AffiliationInfo><Affiliation>The MITRE Corporation, 202 Burlington Rd, Bedford MA 01730, USA. mcolosimo@mitre.org.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Peterson</LastName><ForeName>Matthew W</ForeName><Initials>MW</Initials></Author><Author ValidYN="Y"><LastName>Mardis</LastName><ForeName>Scott</ForeName><Initials>S</Initials></Author><Author ValidYN="Y"><LastName>Hirschman</LastName><ForeName>Lynette</ForeName><Initials>L</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2011</Year><Month>08</Month><Day>18</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Source Code Biol Med</MedlineTA><NlmUniqueID>101276533</NlmUniqueID><ISSNLinking>1751-0473</ISSNLinking></MedlineJournalInfo></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2011</Year><Month>04</Month><Day>05</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2011</Year><Month>08</Month><Day>18</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2011</Year><Month>8</Month><Day>20</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate 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