16S rRNA sequence embeddings: Meaningful numeric feature representations of nucleotide sequences that are convenient for downstream analyses
Identifieur interne : 000750 ( Main/Exploration ); précédent : 000749; suivant : 00075116S rRNA sequence embeddings: Meaningful numeric feature representations of nucleotide sequences that are convenient for downstream analyses
Auteurs : Stephen Woloszynek [États-Unis] ; Zhengqiao Zhao [États-Unis] ; Jian Chen [États-Unis] ; Gail L. Rosen [États-Unis]Source :
- PLoS Computational Biology [ 1553-734X ] ; 2019.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- chemical , genetics : RNA, Ribosomal, 16S.
- genetics : Microbiota.
- methods : Computational Biology, High-Throughput Nucleotide Sequencing, Sequence Analysis, RNA.
- chemical , physiology : RNA, Ribosomal, 16S.
- Algorithms, Cluster Analysis.
Abstract
Advances in high-throughput sequencing have increased the availability of microbiome sequencing data that can be exploited to characterize microbiome community structure
Url:
DOI: 10.1371/journal.pcbi.1006721
PubMed: 30807567
PubMed Central: 6407789
Affiliations:
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Le document en format XML
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<author><name sortKey="Zhao, Zhengqiao" sort="Zhao, Zhengqiao" uniqKey="Zhao Z" first="Zhengqiao" last="Zhao">Zhengqiao Zhao</name>
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<term>Microbiota (genetics)</term>
<term>RNA, Ribosomal, 16S (genetics)</term>
<term>RNA, Ribosomal, 16S (physiology)</term>
<term>Sequence Analysis, RNA (methods)</term>
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<term>ARN ribosomique 16S (physiologie)</term>
<term>Algorithmes</term>
<term>Analyse de regroupements</term>
<term>Analyse de séquence d'ARN ()</term>
<term>Biologie informatique ()</term>
<term>Microbiote (génétique)</term>
<term>Séquençage nucléotidique à haut débit ()</term>
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<term>Cluster Analysis</term>
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<term>Analyse de regroupements</term>
<term>Analyse de séquence d'ARN</term>
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<front><div type="abstract" xml:lang="en"><p>Advances in high-throughput sequencing have increased the availability of microbiome sequencing data that can be exploited to characterize microbiome community structure <italic>in situ</italic>
. We explore using word and sentence embedding approaches for nucleotide sequences since they may be a suitable numerical representation for downstream machine learning applications (especially deep learning). This work involves first encoding (“embedding”) each sequence into a dense, low-dimensional, numeric vector space. Here, we use Skip-Gram word2vec to embed <italic>k</italic>
-mers, obtained from 16S rRNA amplicon surveys, and then leverage an existing sentence embedding technique to embed all sequences belonging to specific body sites or samples. We demonstrate that these representations are meaningful, and hence the embedding space can be exploited as a form of feature extraction for exploratory analysis. We show that sequence embeddings preserve relevant information about the sequencing data such as <italic>k</italic>
-mer context, sequence taxonomy, and sample class. Specifically, the sequence embedding space resolved differences among phyla, as well as differences among genera within the same family. Distances between sequence embeddings had similar qualities to distances between alignment identities, and embedding multiple sequences can be thought of as generating a consensus sequence. In addition, embeddings are versatile features that can be used for many downstream tasks, such as taxonomic and sample classification. Using sample embeddings for body site classification resulted in negligible performance loss compared to using OTU abundance data, and clustering embeddings yielded high fidelity species clusters. Lastly, the <italic>k</italic>
-mer embedding space captured distinct <italic>k</italic>
-mer profiles that mapped to specific regions of the 16S rRNA gene and corresponded with particular body sites. Together, our results show that embedding sequences results in meaningful representations that can be used for exploratory analyses or for downstream machine learning applications that require numeric data. Moreover, because the embeddings are trained in an unsupervised manner, unlabeled data can be embedded and used to bolster supervised machine learning tasks.</p>
</div>
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</div1>
</back>
</TEI>
<affiliations><list><country><li>États-Unis</li>
</country>
<region><li>Pennsylvanie</li>
<li>État de New York</li>
</region>
</list>
<tree><country name="États-Unis"><region name="Pennsylvanie"><name sortKey="Woloszynek, Stephen" sort="Woloszynek, Stephen" uniqKey="Woloszynek S" first="Stephen" last="Woloszynek">Stephen Woloszynek</name>
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<name sortKey="Chen, Jian" sort="Chen, Jian" uniqKey="Chen J" first="Jian" last="Chen">Jian Chen</name>
<name sortKey="Rosen, Gail L" sort="Rosen, Gail L" uniqKey="Rosen G" first="Gail L." last="Rosen">Gail L. Rosen</name>
<name sortKey="Zhao, Zhengqiao" sort="Zhao, Zhengqiao" uniqKey="Zhao Z" first="Zhengqiao" last="Zhao">Zhengqiao Zhao</name>
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</tree>
</affiliations>
</record>
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