Metagenomic binning through low-density hashing

Bioinformatics
Yunan LuoJian Peng

Abstract

Vastly greater quantities of microbial genome data are being generated where environmental samples mix together the DNA from many different species. Here, we present Opal for metagenomic binning, the task of identifying the origin species of DNA sequencing reads. We introduce 'low-density' locality sensitive hashing to bioinformatics, with the addition of Gallager codes for even coverage, enabling quick and accurate metagenomic binning. On public benchmarks, Opal halves the error on precision/recall (F1-score) as compared with both alignment-based and alignment-free methods for species classification. We demonstrate even more marked improvement at higher taxonomic levels, allowing for the discovery of novel lineages. Furthermore, the innovation of low-density, even-coverage hashing should itself prove an essential methodological advance as it enables the application of machine learning to other bioinformatic challenges. Full source code and datasets are available at http://opal.csail.mit.edu and https://github.com/yunwilliamyu/opal. Supplementary data are available at Bioinformatics online.

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Citations

Feb 21, 2019·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Romain Menegaux, Jean-Philippe Vert
Dec 6, 2019·Genome Biology·Daniel N Baker, Ben Langmead
Mar 14, 2020·Bioinformatics·Vijini MallawaarachchiYu Lin
Jul 14, 2020·Bioinformatics·Anuradha WickramarachchiYu Lin
Mar 18, 2019·Microbiome·Will Pm RoweMartyn D Winn
Apr 22, 2021·Journal of Gastroenterology and Hepatology·Tao ZengZhangran Chen
May 6, 2021·Algorithms for Molecular Biology : AMB·Vijini G MallawaarachchiYu Lin
Aug 12, 2021·NAR Genomics and Bioinformatics·Eleonora RachtmanSiavash Mirarab

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Methods Mentioned

BETA
Hi-Seq

Software Mentioned

PhyloPythia
Latent Analysis
Kaiju
PhyloPythiaS
CLARK
BWA
Kraken
OPAL
BLAST
PatternHunter

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