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

被引:0
作者
Nathan LaPierre
Mohammed Alser
Eleazar Eskin
David Koslicki
Serghei Mangul
机构
[1] University of California,Department of Computer Science
[2] ETH Zurich,Department of Computer Science
[3] University of California,Department of Computational Medicine
[4] University of California,Department of Human Genetics
[5] The Pennsylvania State University,Department of Computer Science and Engineering
[6] The Pennsylvania State University,Department of Biology
[7] The Pennsylvania State University,Huck Institutes of the Life Sciences
[8] University of Southern California,Department of Clinical Pharmacy
来源
Genome Biology | / 21卷
关键词
Metagenomics; Abundance estimation; Profiling; Alignment;
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摘要
Metagenomic profiling, predicting the presence and relative abundances of microbes in a sample, is a critical first step in microbiome analysis. Alignment-based approaches are often considered accurate yet computationally infeasible. Here, we present a novel method, Metalign, that performs efficient and accurate alignment-based metagenomic profiling. We use a novel containment min hash approach to pre-filter the reference database prior to alignment and then process both uniquely aligned and multi-aligned reads to produce accurate abundance estimates. In performance evaluations on both real and simulated datasets, Metalign is the only method evaluated that maintained high performance and competitive running time across all datasets.
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