Streaming histogram sketching for rapid microbiome analytics
Identifieur interne : 002146 ( Ncbi/Merge ); précédent : 002145; suivant : 002147Streaming histogram sketching for rapid microbiome analytics
Auteurs : Will Pm Rowe [Royaume-Uni] ; Anna Paola Carrieri [Royaume-Uni] ; Cristina Alcon-Giner [Royaume-Uni] ; Shabhonam Caim [Royaume-Uni] ; Alex Shaw [Royaume-Uni] ; Kathleen Sim [Royaume-Uni] ; J. Simon Kroll [Royaume-Uni] ; Lindsay J. Hall [Royaume-Uni] ; Edward O. Pyzer-Knapp [Royaume-Uni] ; Martyn D. Winn [Royaume-Uni]Source :
- Microbiome [ 2049-2618 ] ; 2019.
Descripteurs français
- KwdFr :
- MESH :
- traitement médicamenteux : Infections bactériennes.
- usage thérapeutique : Antibactériens.
- Analyse de séquence d'ADN, Apprentissage machine, Bactéries, Humains, Logiciel, Microbiome gastro-intestinal, Métagénomique, Nouveau-né, Prématuré, Études de cohortes.
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.
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.
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. (
Url:
DOI: 10.1186/s40168-019-0653-2
PubMed: 30878035
PubMed Central: 6420756
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Links to Exploration step
PMC:6420756Le document en format XML
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<term>Humans</term>
<term>Infant, Newborn</term>
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p id="Par1">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.</p>
<p id="Par2">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.</p>
</sec>
<sec><title>Results</title>
<p id="Par3">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.</p>
<p id="Par4">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.</p>
</sec>
<sec><title>Conclusions</title>
<p id="Par5">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. (<ext-link ext-link-type="uri" xlink:href="https://github.com/will-rowe/hulk">https://github.com/will-rowe/hulk</ext-link>
).</p>
</sec>
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<author><name sortKey="Shaw, Alex" sort="Shaw, Alex" uniqKey="Shaw A" first="Alex" last="Shaw">Alex Shaw</name>
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<institution-id institution-id-type="GRID">grid.7445.2</institution-id>
<institution>Department of Medicine, Section of Paediatrics,</institution>
<institution>Imperial College London,</institution>
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London, UK</nlm:aff>
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<wicri:regionArea>London</wicri:regionArea>
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<author><name sortKey="Sim, Kathleen" sort="Sim, Kathleen" uniqKey="Sim K" first="Kathleen" last="Sim">Kathleen Sim</name>
<affiliation wicri:level="1"><nlm:aff id="Aff4"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2113 8111</institution-id>
<institution-id institution-id-type="GRID">grid.7445.2</institution-id>
<institution>Department of Medicine, Section of Paediatrics,</institution>
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London, UK</nlm:aff>
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<wicri:regionArea>London</wicri:regionArea>
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<author><name sortKey="Kroll, J Simon" sort="Kroll, J Simon" uniqKey="Kroll J" first="J. Simon" last="Kroll">J. Simon Kroll</name>
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<institution-id institution-id-type="GRID">grid.7445.2</institution-id>
<institution>Department of Medicine, Section of Paediatrics,</institution>
<institution>Imperial College London,</institution>
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London, UK</nlm:aff>
<country xml:lang="fr">Royaume-Uni</country>
<wicri:regionArea>London</wicri:regionArea>
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<author><name sortKey="Hall, Lindsay J" sort="Hall, Lindsay J" uniqKey="Hall L" first="Lindsay J." last="Hall">Lindsay J. Hall</name>
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<institution>Quadram Institute Bioscience,</institution>
<institution>Norwich Research Park,</institution>
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Norwich, UK</nlm:aff>
<country xml:lang="fr">Royaume-Uni</country>
<wicri:regionArea>Norwich</wicri:regionArea>
</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 wicri:level="1"><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="GRID">grid.14467.30</institution-id>
<institution>IBM Research,</institution>
<institution>The Hartree Centre,</institution>
</institution-wrap>
Warrington, UK</nlm:aff>
<country xml:lang="fr">Royaume-Uni</country>
<wicri:regionArea>Warrington</wicri:regionArea>
</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 wicri:level="1"><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 0727 2226</institution-id>
<institution-id institution-id-type="GRID">grid.482271.a</institution-id>
<institution>Scientific Computing Department,</institution>
<institution>STFC Daresbury Laboratory,</institution>
</institution-wrap>
Warrington, UK</nlm:aff>
<country xml:lang="fr">Royaume-Uni</country>
<wicri:regionArea>Warrington</wicri:regionArea>
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</analytic>
<series><title level="j">Microbiome</title>
<idno type="eISSN">2049-2618</idno>
<imprint><date when="2019">2019</date>
</imprint>
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p id="Par1">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.</p>
<p id="Par2">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.</p>
</sec>
<sec><title>Results</title>
<p id="Par3">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.</p>
<p id="Par4">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.</p>
</sec>
<sec><title>Conclusions</title>
<p id="Par5">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. (<ext-link ext-link-type="uri" xlink:href="https://github.com/will-rowe/hulk">https://github.com/will-rowe/hulk</ext-link>
).</p>
</sec>
</div>
</front>
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<pubmed><TEI><teiHeader><fileDesc><titleStmt><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 wicri:level="1"><nlm:affiliation>Scientific Computing Department, STFC Daresbury Laboratory, Warrington, UK. will.rowe@stfc.ac.uk.</nlm:affiliation>
<country xml:lang="fr">Royaume-Uni</country>
<wicri:regionArea>Scientific Computing Department, STFC Daresbury Laboratory, Warrington</wicri:regionArea>
<wicri:noRegion>Warrington</wicri:noRegion>
</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 wicri:level="1"><nlm:affiliation>IBM Research, The Hartree Centre, Warrington, UK.</nlm:affiliation>
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<wicri:regionArea>IBM Research, The Hartree Centre, Warrington</wicri:regionArea>
<wicri:noRegion>Warrington</wicri:noRegion>
</affiliation>
</author>
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<affiliation wicri:level="1"><nlm:affiliation>Quadram Institute Bioscience, Norwich Research Park, Norwich, UK.</nlm:affiliation>
<country xml:lang="fr">Royaume-Uni</country>
<wicri:regionArea>Quadram Institute Bioscience, Norwich Research Park, Norwich</wicri:regionArea>
<wicri:noRegion>Norwich</wicri:noRegion>
</affiliation>
</author>
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<affiliation wicri:level="1"><nlm:affiliation>Quadram Institute Bioscience, Norwich Research Park, Norwich, UK.</nlm:affiliation>
<country xml:lang="fr">Royaume-Uni</country>
<wicri:regionArea>Quadram Institute Bioscience, Norwich Research Park, Norwich</wicri:regionArea>
<wicri:noRegion>Norwich</wicri:noRegion>
</affiliation>
</author>
<author><name sortKey="Shaw, Alex" sort="Shaw, Alex" uniqKey="Shaw A" first="Alex" last="Shaw">Alex Shaw</name>
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<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>
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<author><name sortKey="Winn, Martyn D" sort="Winn, Martyn D" uniqKey="Winn M" first="Martyn D" last="Winn">Martyn D. Winn</name>
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<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 wicri:level="1"><nlm:affiliation>Scientific Computing Department, STFC Daresbury Laboratory, Warrington, UK. will.rowe@stfc.ac.uk.</nlm:affiliation>
<country xml:lang="fr">Royaume-Uni</country>
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</author>
<author><name sortKey="Carrieri, Anna Paola" sort="Carrieri, Anna Paola" uniqKey="Carrieri A" first="Anna Paola" last="Carrieri">Anna Paola Carrieri</name>
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<country xml:lang="fr">Royaume-Uni</country>
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<wicri:noRegion>Warrington</wicri:noRegion>
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</author>
<author><name sortKey="Alcon Giner, Cristina" sort="Alcon Giner, Cristina" uniqKey="Alcon Giner C" first="Cristina" last="Alcon-Giner">Cristina Alcon-Giner</name>
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<author><name sortKey="Caim, Shabhonam" sort="Caim, Shabhonam" uniqKey="Caim S" first="Shabhonam" last="Caim">Shabhonam Caim</name>
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<author><name sortKey="Shaw, Alex" sort="Shaw, Alex" uniqKey="Shaw A" first="Alex" last="Shaw">Alex Shaw</name>
<affiliation wicri:level="3"><nlm:affiliation>Department of Medicine, Section of Paediatrics, Imperial College London, London, UK.</nlm:affiliation>
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<wicri:regionArea>Department of Medicine, Section of Paediatrics, Imperial College London, London</wicri:regionArea>
<placeName><settlement type="city">Londres</settlement>
<region type="country">Angleterre</region>
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</author>
<author><name sortKey="Sim, Kathleen" sort="Sim, Kathleen" uniqKey="Sim K" first="Kathleen" last="Sim">Kathleen Sim</name>
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<wicri:regionArea>Department of Medicine, Section of Paediatrics, Imperial College London, London</wicri:regionArea>
<placeName><settlement type="city">Londres</settlement>
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<region type="région" nuts="1">Grand Londres</region>
</placeName>
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<author><name sortKey="Kroll, J Simon" sort="Kroll, J Simon" uniqKey="Kroll J" first="J Simon" last="Kroll">J Simon Kroll</name>
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<placeName><settlement type="city">Londres</settlement>
<region type="country">Angleterre</region>
<region type="région" nuts="1">Grand Londres</region>
</placeName>
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</author>
<author><name sortKey="Hall, Lindsay J" sort="Hall, Lindsay J" uniqKey="Hall L" first="Lindsay J" last="Hall">Lindsay J. Hall</name>
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</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>
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<country xml:lang="fr">Royaume-Uni</country>
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<wicri:noRegion>Warrington</wicri:noRegion>
</affiliation>
</author>
<author><name sortKey="Winn, Martyn D" sort="Winn, Martyn D" uniqKey="Winn M" first="Martyn D" last="Winn">Martyn D. Winn</name>
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<series><title level="j">Microbiome</title>
<idno type="eISSN">2049-2618</idno>
<imprint><date when="2019" type="published">2019</date>
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<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="KwdFr" xml:lang="fr"><term>Analyse de séquence d'ADN</term>
<term>Antibactériens (usage thérapeutique)</term>
<term>Apprentissage machine</term>
<term>Bactéries ()</term>
<term>Humains</term>
<term>Infections bactériennes (traitement médicamenteux)</term>
<term>Logiciel</term>
<term>Microbiome gastro-intestinal</term>
<term>Métagénomique ()</term>
<term>Nouveau-né</term>
<term>Prématuré</term>
<term>Études de cohortes</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" qualifier="traitement médicamenteux" xml:lang="fr"><term>Infections bactériennes</term>
</keywords>
<keywords scheme="MESH" qualifier="usage thérapeutique" xml:lang="fr"><term>Antibactériens</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>
<keywords scheme="MESH" xml:lang="fr"><term>Analyse de séquence d'ADN</term>
<term>Apprentissage machine</term>
<term>Bactéries</term>
<term>Humains</term>
<term>Logiciel</term>
<term>Microbiome gastro-intestinal</term>
<term>Métagénomique</term>
<term>Nouveau-né</term>
<term>Prématuré</term>
<term>Études de cohortes</term>
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<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>
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