EPA-ng: Massively Parallel Evolutionary Placement of Genetic Sequences

Abstract

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. To achieve this, 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 O_SCPLOWPPLACERC_SCPLOW, are being increasingly used for this purpose. However, due to the steady progress in NGS technologies, the current implementations face substantial scalability limitations. Here we present EPA-O_SCPLOWNGC_SCPLOW, a complete reimplementation of the EPA that is substantially faster, offers a distributed memory parallelization, and integrates concepts from both, RAxML-EPA, and O_SCPLOWPPLACERC_SCPLOW. EPA-O_SCPLOWNGC_SCPLOW can be executed on standard shared memory, as well as on distributed memory systems (e.g., computing clusters). To demonstrate the scalability of EPA-O_SCPLOWNGC_SCPLOW we placed 1 billion metagenetic reads from the Tara Oceans Project onto a reference tree with 3,748 taxa in just under 7 hours, using 2,048 cores. Our performance assessment shows that EPA-O_SCPLOWNGC_SCPLOW outperforms RAxML-EPA and O_SCPLOWPPLACERC_SCPLOW by up to a factor of 30 in sequential execution mode, while attaining comparable parallel efficiency on shared memory systems. We further show that the distributed memory parallelization of EPA-O_SCPLOWNGC_SCPLOW scales well up to 3,520 cores. EPA-O_SCPLOWNGC_SCPLOW is available under the AGPLv3 license: https://github.com/Pbdas/epa-ng