Metagenomic binning through low-density hashing
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bty611.pdf
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Published version
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365.2 KB
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Author(s) • • • •
Luo, Yunan
Yu, Yun William
Zeng, Jianyang
Berger Leighton, Bonnie
Peng, Jian
Date Issued
July 13, 2018
Journal
Bioinformatics
Publisher
Oxford University Press (OUP)
Citation
Lou, Yunan, et al. "Metagenomic binning through low-density hashing." Bioinformatics 35, 2, (January 2019): 219–226 © 2018 The Author(s)
Version
Final published version
Abstract
Motivation: 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. Results: 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. Availability and implementation: Full source code and datasets are available at http://opal.csail.mit.edu and https://github.com/yunwilliamyu/opal. Supplementary information: Supplementary data are available at Bioinformatics online.
MIT Department
Massachusetts Institute of Technology. Department of Mathematics
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution NonCommercial License 4.0
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DOI of Published Version
https://doi.org/10.1093/bioinformatics/bty611