Streaming histogram sketching for rapid microbiome analytics.
Identifieur interne : 000610 ( PubMed/Corpus ); précédent : 000609; suivant : 000611Streaming histogram sketching for rapid microbiome analytics.
Auteurs : Will Pm Rowe ; Anna Paola Carrieri ; Cristina Alcon-Giner ; Shabhonam Caim ; Alex Shaw ; Kathleen Sim ; J Simon Kroll ; Lindsay J. Hall ; Edward O. Pyzer-Knapp ; Martyn D. WinnSource :
- Microbiome [ 2049-2618 ] ; 2019.
English descriptors
- KwdEn :
- MESH :
- chemical , therapeutic use : Anti-Bacterial Agents.
- classification : Bacteria.
- drug therapy : Bacterial Infections.
- methods : Metagenomics.
- Cohort Studies, Gastrointestinal Microbiome, Humans, Infant, Newborn, Infant, Premature, Machine Learning, Sequence Analysis, DNA, Software.
Abstract
The growth in publically available microbiome data in recent years has yielded an invaluable resource for genomic research, allowing for the design of new studies, augmentation of novel datasets and reanalysis of published works. This vast amount of microbiome data, as well as the widespread proliferation of microbiome research and the looming era of clinical metagenomics, means there is an urgent need to develop analytics that can process huge amounts of data in a short amount of time. To address this need, we propose a new method for tyrhe compact representation of microbiome sequencing data using similarity-preserving sketches of streaming k-mer spectra. These sketches allow for dissimilarity estimation, rapid microbiome catalogue searching and classification of microbiome samples in near real time.
DOI: 10.1186/s40168-019-0653-2
PubMed: 30878035
Links to Exploration step
pubmed:30878035Le document en format XML
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Hall</name><affiliation><nlm:affiliation>Quadram Institute Bioscience, Norwich Research Park, Norwich, UK. Lindsay.Hall@quadram.ac.uk.</nlm:affiliation></affiliation></author><author><name sortKey="Pyzer Knapp, Edward O" sort="Pyzer Knapp, Edward O" uniqKey="Pyzer Knapp E" first="Edward O" last="Pyzer-Knapp">Edward O. Pyzer-Knapp</name><affiliation><nlm:affiliation>IBM Research, The Hartree Centre, Warrington, UK.</nlm:affiliation></affiliation></author><author><name sortKey="Winn, Martyn D" sort="Winn, Martyn D" uniqKey="Winn M" first="Martyn D" last="Winn">Martyn D. Winn</name><affiliation><nlm:affiliation>Scientific Computing Department, STFC Daresbury Laboratory, Warrington, UK.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2019">2019</date><idno type="RBID">pubmed:30878035</idno><idno type="pmid">30878035</idno><idno type="doi">10.1186/s40168-019-0653-2</idno><idno type="wicri:Area/PubMed/Corpus">000610</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000610</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Streaming histogram sketching for rapid microbiome analytics.</title><author><name sortKey="Rowe, Will Pm" sort="Rowe, Will Pm" uniqKey="Rowe W" first="Will Pm" last="Rowe">Will Pm Rowe</name><affiliation><nlm:affiliation>Scientific Computing Department, STFC Daresbury Laboratory, Warrington, UK. will.rowe@stfc.ac.uk.</nlm:affiliation></affiliation></author><author><name sortKey="Carrieri, Anna Paola" sort="Carrieri, Anna Paola" uniqKey="Carrieri A" first="Anna Paola" last="Carrieri">Anna Paola Carrieri</name><affiliation><nlm:affiliation>IBM Research, The Hartree Centre, Warrington, UK.</nlm:affiliation></affiliation></author><author><name sortKey="Alcon Giner, Cristina" sort="Alcon Giner, Cristina" uniqKey="Alcon Giner C" first="Cristina" last="Alcon-Giner">Cristina Alcon-Giner</name><affiliation><nlm:affiliation>Quadram Institute Bioscience, Norwich Research Park, Norwich, UK.</nlm:affiliation></affiliation></author><author><name sortKey="Caim, Shabhonam" sort="Caim, Shabhonam" uniqKey="Caim S" first="Shabhonam" last="Caim">Shabhonam Caim</name><affiliation><nlm:affiliation>Quadram Institute Bioscience, Norwich Research Park, Norwich, UK.</nlm:affiliation></affiliation></author><author><name sortKey="Shaw, Alex" sort="Shaw, Alex" uniqKey="Shaw A" first="Alex" last="Shaw">Alex Shaw</name><affiliation><nlm:affiliation>Department of Medicine, Section of Paediatrics, Imperial College London, London, UK.</nlm:affiliation></affiliation></author><author><name sortKey="Sim, Kathleen" sort="Sim, Kathleen" uniqKey="Sim K" first="Kathleen" last="Sim">Kathleen Sim</name><affiliation><nlm:affiliation>Department of Medicine, Section of Paediatrics, Imperial College London, London, UK.</nlm:affiliation></affiliation></author><author><name sortKey="Kroll, J Simon" sort="Kroll, J Simon" uniqKey="Kroll J" first="J Simon" last="Kroll">J Simon Kroll</name><affiliation><nlm:affiliation>Department of Medicine, Section of Paediatrics, Imperial College London, London, UK.</nlm:affiliation></affiliation></author><author><name sortKey="Hall, Lindsay J" sort="Hall, Lindsay J" uniqKey="Hall L" first="Lindsay J" last="Hall">Lindsay J. Hall</name><affiliation><nlm:affiliation>Quadram Institute Bioscience, Norwich Research Park, Norwich, UK. Lindsay.Hall@quadram.ac.uk.</nlm:affiliation></affiliation></author><author><name sortKey="Pyzer Knapp, Edward O" sort="Pyzer Knapp, Edward O" uniqKey="Pyzer Knapp E" first="Edward O" last="Pyzer-Knapp">Edward O. Pyzer-Knapp</name><affiliation><nlm:affiliation>IBM Research, The Hartree Centre, Warrington, UK.</nlm:affiliation></affiliation></author><author><name sortKey="Winn, Martyn D" sort="Winn, Martyn D" uniqKey="Winn M" first="Martyn D" last="Winn">Martyn D. Winn</name><affiliation><nlm:affiliation>Scientific Computing Department, STFC Daresbury Laboratory, Warrington, UK.</nlm:affiliation></affiliation></author></analytic><series><title level="j">Microbiome</title><idno type="eISSN">2049-2618</idno><imprint><date when="2019" type="published">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Anti-Bacterial Agents (therapeutic use)</term><term>Bacteria (classification)</term><term>Bacterial Infections (drug therapy)</term><term>Cohort Studies</term><term>Gastrointestinal Microbiome</term><term>Humans</term><term>Infant, Newborn</term><term>Infant, Premature</term><term>Machine Learning</term><term>Metagenomics (methods)</term><term>Sequence Analysis, DNA</term><term>Software</term></keywords><keywords scheme="MESH" type="chemical" qualifier="therapeutic use" xml:lang="en"><term>Anti-Bacterial Agents</term></keywords><keywords scheme="MESH" qualifier="classification" xml:lang="en"><term>Bacteria</term></keywords><keywords scheme="MESH" qualifier="drug therapy" xml:lang="en"><term>Bacterial Infections</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Metagenomics</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Cohort Studies</term><term>Gastrointestinal Microbiome</term><term>Humans</term><term>Infant, Newborn</term><term>Infant, Premature</term><term>Machine Learning</term><term>Sequence Analysis, DNA</term><term>Software</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The growth in publically available microbiome data in recent years has yielded an invaluable resource for genomic research, allowing for the design of new studies, augmentation of novel datasets and reanalysis of published works. This vast amount of microbiome data, as well as the widespread proliferation of microbiome research and the looming era of clinical metagenomics, means there is an urgent need to develop analytics that can process huge amounts of data in a short amount of time. To address this need, we propose a new method for tyrhe compact representation of microbiome sequencing data using similarity-preserving sketches of streaming k-mer spectra. These sketches allow for dissimilarity estimation, rapid microbiome catalogue searching and classification of microbiome samples in near real time.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">30878035</PMID><DateCompleted><Year>2019</Year><Month>06</Month><Day>10</Day></DateCompleted><DateRevised><Year>2020</Year><Month>03</Month><Day>11</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">2049-2618</ISSN><JournalIssue CitedMedium="Internet"><Volume>7</Volume><Issue>1</Issue><PubDate><Year>2019</Year><Month>03</Month><Day>16</Day></PubDate></JournalIssue><Title>Microbiome</Title><ISOAbbreviation>Microbiome</ISOAbbreviation></Journal><ArticleTitle>Streaming histogram sketching for rapid microbiome analytics.</ArticleTitle><Pagination><MedlinePgn>40</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/s40168-019-0653-2</ELocationID><Abstract><AbstractText Label="BACKGROUND">The growth in publically available microbiome data in recent years has yielded an invaluable resource for genomic research, allowing for the design of new studies, augmentation of novel datasets and reanalysis of published works. This vast amount of microbiome data, as well as the widespread proliferation of microbiome research and the looming era of clinical metagenomics, means there is an urgent need to develop analytics that can process huge amounts of data in a short amount of time. To address this need, we propose a new method for tyrhe compact representation of microbiome sequencing data using similarity-preserving sketches of streaming k-mer spectra. These sketches allow for dissimilarity estimation, rapid microbiome catalogue searching and classification of microbiome samples in near real time.</AbstractText><AbstractText Label="RESULTS">We apply streaming histogram sketching to microbiome samples as a form of dimensionality reduction, creating a compressed 'histosketch' that can efficiently represent microbiome k-mer spectra. Using public microbiome datasets, we show that histosketches can be clustered by sample type using the pairwise Jaccard similarity estimation, consequently allowing for rapid microbiome similarity searches via a locality sensitive hashing indexing scheme. Furthermore, we use a 'real life' example to show that histosketches can train machine learning classifiers to accurately label microbiome samples. Specifically, using a collection of 108 novel microbiome samples from a cohort of premature neonates, we trained and tested a random forest classifier that could accurately predict whether the neonate had received antibiotic treatment (97% accuracy, 96% precision) and could subsequently be used to classify microbiome data streams in less than 3 s.</AbstractText><AbstractText Label="CONCLUSIONS">Our method offers a new approach to rapidly process microbiome data streams, allowing samples to be rapidly clustered, indexed and classified. We also provide our implementation, Histosketching Using Little K-mers (HULK), which can histosketch a typical 2 GB microbiome in 50 s on a standard laptop using four cores, with the sketch occupying 3000 bytes of disk space. ( https://github.com/will-rowe/hulk ).</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Rowe</LastName><ForeName>Will Pm</ForeName><Initials>WP</Initials><Identifier Source="ORCID">0000-0003-0384-4463</Identifier><AffiliationInfo><Affiliation>Scientific Computing Department, STFC Daresbury Laboratory, Warrington, UK. will.rowe@stfc.ac.uk.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Carrieri</LastName><ForeName>Anna Paola</ForeName><Initials>AP</Initials><AffiliationInfo><Affiliation>IBM Research, The Hartree Centre, Warrington, UK.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Alcon-Giner</LastName><ForeName>Cristina</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>Quadram Institute Bioscience, Norwich Research Park, Norwich, UK.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Caim</LastName><ForeName>Shabhonam</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Quadram Institute Bioscience, Norwich Research Park, Norwich, UK.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Shaw</LastName><ForeName>Alex</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Department of Medicine, Section of Paediatrics, Imperial College London, London, UK.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Sim</LastName><ForeName>Kathleen</ForeName><Initials>K</Initials><AffiliationInfo><Affiliation>Department of Medicine, Section of Paediatrics, Imperial College London, London, UK.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kroll</LastName><ForeName>J Simon</ForeName><Initials>JS</Initials><AffiliationInfo><Affiliation>Department of Medicine, Section of Paediatrics, Imperial College London, London, UK.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Hall</LastName><ForeName>Lindsay J</ForeName><Initials>LJ</Initials><AffiliationInfo><Affiliation>Quadram Institute Bioscience, Norwich Research Park, Norwich, UK. Lindsay.Hall@quadram.ac.uk.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Pyzer-Knapp</LastName><ForeName>Edward O</ForeName><Initials>EO</Initials><AffiliationInfo><Affiliation>IBM Research, The Hartree Centre, Warrington, UK.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Winn</LastName><ForeName>Martyn D</ForeName><Initials>MD</Initials><AffiliationInfo><Affiliation>Scientific Computing Department, STFC Daresbury Laboratory, Warrington, UK.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>BBS/E/F/00044409</GrantID><Acronym>BB_</Acronym><Agency>Biotechnology and Biological Sciences Research Council</Agency><Country>United Kingdom</Country></Grant><Grant><GrantID>BB/M011216/1</GrantID><Acronym>BB_</Acronym><Agency>Biotechnology and Biological Sciences Research Council</Agency><Country>United 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Kingdom</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2019</Year><Month>03</Month><Day>16</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Microbiome</MedlineTA><NlmUniqueID>101615147</NlmUniqueID><ISSNLinking>2049-2618</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D000900">Anti-Bacterial Agents</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000900" MajorTopicYN="N">Anti-Bacterial Agents</DescriptorName><QualifierName UI="Q000627" MajorTopicYN="N">therapeutic use</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D001419" MajorTopicYN="N">Bacteria</DescriptorName><QualifierName UI="Q000145" MajorTopicYN="Y">classification</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D001424" MajorTopicYN="N">Bacterial Infections</DescriptorName><QualifierName UI="Q000188" MajorTopicYN="N">drug therapy</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D015331" MajorTopicYN="N">Cohort Studies</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000069196" MajorTopicYN="Y">Gastrointestinal Microbiome</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007231" MajorTopicYN="N">Infant, Newborn</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007234" MajorTopicYN="N">Infant, Premature</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000069550" MajorTopicYN="N">Machine Learning</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D056186" MajorTopicYN="N">Metagenomics</DescriptorName><QualifierName 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