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EPA-ng: Massively Parallel Evolutionary Placement of Genetic Sequences

Barbera, P.; Kozlov, A. M.; Czech, L.; Morel, B.; Darriba, D.; Flouri, T.; Stamatakis, A.

Next generation sequencing (NGS) technologies have led to a ubiquity of molecular sequence data. This data avalanche is particularly challenging in metagenetics, which focuses on taxonomic identification of sequences obtained from diverse microbial environments. Phylogenetic placement methods determine how these sequences fit into an evolutionary context. Previous implementations of phylogenetic placement algorithms, such as the evolutionary placement algorithm (EPA) included in RAxML, or PPLACER, are being increasingly used for this purpose. However, due to the steady progress in NGS technologies, the current implementations face substantial scalability limitations. Herein, we present EPA-NG, a complete reimplementation of the EPA that is substantially faster, offers a distributed memory parallelization, and integrates concepts from both, RAxML-EPA and PPLACER. EPA-NG can be executed on standard shared memory, as well as on distributed memory systems (e.g., computing clusters). To demonstrate the scalability of EPA-NG, we placed 1 billion metagenetic reads from the Tara Oceans Project onto a reference tree with 3748 taxa in just under 7 h, using 2048 cores. ... mehr

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Verlagsausgabe §
DOI: 10.5445/IR/1000092518
Veröffentlicht am 27.03.2019
DOI: 10.1093/sysbio/syy054
Zitationen: 18
Web of Science
Zitationen: 14
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Theoretische Informatik (ITI)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2019
Sprache Englisch
Identifikator ISSN: 1063-5157, 1076-836X
KITopen-ID: 1000092518
Erschienen in Systematic biology
Band 68
Heft 2
Seiten 365-369
Vorab online veröffentlicht am 21.09.2018
Schlagwörter Metabarcoding, metagenomics, microbiome, phylogenetics, phylogenetic placement
Nachgewiesen in Web of Science
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