Continuous Embeddings of DNA Sequencing Reads and Application to Metagenomics

被引：21

作者：

Menegaux, Romain ^{[1
,2
]}

Vert, Jean-Philippe ^{[1
,2
,3
,4
]}

机构：

[1] PSL Res Univ, MINES ParisTech, CBIO Ctr Computat Biol, Paris, France

[2] PSL Res Univ, Inst Curie, INSERM, U900, Paris, France

[3] PSL Res Univ, CNRS, Dept Math & Applicat, Ecole Normale Super, Paris, France

[4] Google Brain, 8 Rue Londres, F-75009 Paris, France

来源：

JOURNAL OF COMPUTATIONAL BIOLOGY | 2019年 / 26卷 / 06期

关键词：

metagenomics; sequencing; classification; embedding; CLASSIFICATION;

D O I：

10.1089/cmb.2018.0174

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

We propose a new model for fast classification of DNA sequences output by next-generation sequencing machines. The model, which we call fastDNA, embeds DNA sequences in a vector space by learning continuous low-dimensional representations of the k-mers it contains. We show on metagenomics benchmarks that it outperforms the state-of-the-art methods in terms of accuracy and scalability.

引用

页码：509 / 518

页数：10

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