EPA-ng: Massively Parallel Evolutionary Placement of Genetic Sequences
By P. Barbera, Alexey M. Kozlov, Lucas Czech, Benoit Morel, Diego Darriba, Tomás Flouri, Alexandros Stamatakis. bioRxiv, 2018
553 citations recorded by the source corpus.
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 pplacer, 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-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 3,748 taxa in just under 7 hours, using 2,048 cores. Our performance assessment shows that EPA-ng outperforms RAxML-EPA and pplacer 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-ng scales well up to 3,520 cores. EPA-ng is available under the AGPLv3 license: https://github.com/Pbdas/epa-ng
Subfields: Ecology, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/94065830c8b4725feec8d160389205cad5e27dbb
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Citations in the corpus
- Ultrafast Sample placement on Existing tRees (UShER) enables real-time phylogenetics for the SARS-CoV-2 pandemic (2021, background): “For example, EPA-ng 18 takes approximately 28 central processing unit (CPU) minutes to place one sample and requires 791 gigabytes (GB) of memory (Table 1).”
- Non‐specific amplification compromises environmental DNA metabarcoding with COI (2019, methodology): “The same reads were then run through the Evolutionary Placement Algorithm (EPA‐ng v0.”