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Metalign: Efficient alignment-based metagenomic profiling via containment min hash

View ORCID ProfileNathan LaPierre, Mohammed Alser, Eleazar Eskin, View ORCID ProfileDavid Koslicki, Serghei Mangul
doi: https://doi.org/10.1101/2020.01.17.910521
Nathan LaPierre
1Department of Computer Science, University of California, Los Angeles, California, 90095, United States
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Mohammed Alser
4Department of Computer Science, ETH Zurich, Rämistrasse 101 CH-8092 Zurich, Switzerland
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Eleazar Eskin
1Department of Computer Science, University of California, Los Angeles, California, 90095, United States
2Department of Computational Medicine, University of California, Los Angeles, California, 90095, United States
3Department of Human Genetics, University of California, Los Angeles, California, 90095, United States
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David Koslicki
5Department of Computer Science and Engineering, The Pennsylvania State University, University Park, PA, USA
6Department of Biology, The Pennsylvania State University, University Park, PA, USA
7Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, USA
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Serghei Mangul
8Department of Clinical Pharmacy, University of Southern California, Los Angeles, California, 90089, United States
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  • For correspondence: mangul@usc.edu
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Abstract

Whole-genome shotgun sequencing enables the analysis of microbial communities in unprecedented detail, with major implications in medicine and ecology. Predicting the presence and relative abundances of microbes in a sample, known as “metagenomic profiling”, is a critical first step in microbiome analysis. Existing profiling methods have been shown to suffer from poor false positive or false negative rates, while alignment-based approaches are often considered accurate but computationally infeasible. Here we present a novel method, Metalign, that addresses these concerns by performing efficient alignment-based metagenomic profiling. We use a containment min hash approach to reduce the reference database size dramatically before alignment and a method to estimate organism relative abundances in the sample by resolving reads aligned to multiple genomes. We show that Metalign achieves significantly improved results over existing methods on simulated datasets from a large benchmarking study, CAMI, and performs well on in vitro mock community data and environmental data from the Tara Oceans project. Metalign is freely available at https://github.com/nlapier2/Metalign, along with the results and plots used in this paper, and a docker image is also available at https://hub.docker.com/repository/docker/nlapier2/metalign.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted January 18, 2020.
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Metalign: Efficient alignment-based metagenomic profiling via containment min hash
Nathan LaPierre, Mohammed Alser, Eleazar Eskin, David Koslicki, Serghei Mangul
bioRxiv 2020.01.17.910521; doi: https://doi.org/10.1101/2020.01.17.910521
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Metalign: Efficient alignment-based metagenomic profiling via containment min hash
Nathan LaPierre, Mohammed Alser, Eleazar Eskin, David Koslicki, Serghei Mangul
bioRxiv 2020.01.17.910521; doi: https://doi.org/10.1101/2020.01.17.910521

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