Scalable metagenomic taxonomy classification using a reference genome database

By Sasha Ames, David A. Hysom, Shea N. Gardner, G. S. Lloyd, Maya B. Gokhale, Jonathan E. Allen. 2013

192 citations recorded by the source corpus.

Motivation: Deep metagenomic sequencing of biological samples has the potential to recover otherwise difficult-to-detect microorganisms and accurately characterize biological samples with limited prior knowledge of sample contents. Existing metagenomic taxonomic classification algorithms, however, do not scale well to analyze large metagenomic datasets, and balancing classification accuracy with computational efficiency presents a fundamental challenge. Results: A method is presented to shift computational costs to an off-line computation by creating a taxonomy/genome index that supports scalable metagenomic classification. Scalable performance is demonstrated on real and simulated data to show accurate classification in the presence of novel organisms on samples that include viruses, prokaryotes, fungi and protists. Taxonomic classification of the previously published 150 giga-base Tyrolean Iceman dataset was found to take <20 h on a single node 40 core large memory machine and provide new insights on the metagenomic contents of the sample. Availability: Software was implemented in C++ and is freely available at http://sourceforge.net/projects/lmat Contact: allen99@llnl.gov Supplementary information: Supplementary data are available at Bioinformatics online.

Subfields: Ecology, Endocrinology, Molecular Biology

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Steven L. Salzberg

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Citations in the corpus

KrakenUniq: confident and fast metagenomics classification using unique k-mer counts (2018, methodology): “[20], CLARK-S [21], Kraken [9], LMAT [22], and NBC [23]; and the marker-based methods GOTTCHA [24], MetaPhlAn2 [25], and PhyloSift [26].”
Machine learning for metagenomics: methods and tools (2015, background): “If the community of interest is associated with a host than its DNA has to be separated by either fractionation or selective lysis to ensure that minimal host DNA is present.”
Kraken: ultrafast metagenomic sequence classification using exact alignments (2014, methodology): “We also note the recent publication of a method, LMAT [12], which uses a k-mer indexing scheme similar to Kraken’s, but otherwise differs in its classification strategy.”
Alignment-free sequence comparison: benefits, applications, and tools (2017, background): “…genetic variant calling [71 – 74], de novo genome assembly [75 – 77], phylogenetic reconstruction [78 – 81], and taxonomic classification in metagenomic studies [82 – 86]) (Table 1), but also offer ways to obtain biologically meaningful information directly from raw next-generation sequencing data.”