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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<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>
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<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>
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</record>
Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Sante/explor/MersV1/Data/PubMed/Corpus
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000610 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/PubMed/Corpus/biblio.hfd -nk 000610 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien |wiki= Sante |area= MersV1 |flux= PubMed |étape= Corpus |type= RBID |clé= pubmed:30878035 |texte= Streaming histogram sketching for rapid microbiome analytics. }}
Pour générer des pages wiki
HfdIndexSelect -h $EXPLOR_AREA/Data/PubMed/Corpus/RBID.i -Sk "pubmed:30878035" \ | HfdSelect -Kh $EXPLOR_AREA/Data/PubMed/Corpus/biblio.hfd \ | NlmPubMed2Wicri -a MersV1
![]() | This area was generated with Dilib version V0.6.33. | ![]() |