Continuous Embeddings of DNA Sequencing Reads and Application to Metagenomics

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
Romain Menegaux, Jean-Philippe Vert

Abstract

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.

References

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Nov 22, 2015·Bioinformatics·Kévin VervierJean-Philippe Vert
Jul 17, 2018·Bioinformatics·Yunan LuoJian Peng

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Citations

Apr 8, 2021·Nature Communications·Mehrdad BakhtiariVineet Bafna
Jun 19, 2021·Computational and Structural Biotechnology Journal·Hitoshi IuchiMichiaki Hamada
Oct 13, 2021·MSystems·Braden T TierneyChirag J Patel

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Methods Mentioned

BETA
RNA-seq

Software Mentioned

Bowtie
word2vec
BWA
fastDNA
DeclareMathSizes
fastText
Hi
VW
MEM

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