Publications

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87 Publications visible to you, out of a total of 87

Abstract

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Authors: Ivo Baar, Lukas Hubner, Peter Oettig, Adrian Zapletal, Sebastian Schlag, Alexandros Stamatakis, Benoit Morel

Date Published: 1st May 2019

Publication Type: Proceedings

Abstract

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Author: Alexandros Stamatakis

Date Published: 2019

Publication Type: InCollection

Abstract

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Authors: Dora Serdari, Evangelia-Georgia Kostaki, Dimitrios Paraskevis, Alexandros Stamatakis, Paschalia Kapli

Date Published: 2019

Publication Type: Journal

Abstract

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Authors: Diego Darriba, Tomáš Flouri, Alexandros Stamatakis

Date Published: 2018

Publication Type: Journal

Abstract

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Authors: Diep Thi Hoang, Le Sy Vinh, Tomáš Flouri, Alexandros Stamatakis, Arndt von Häseler, Bui Quang Minh

Date Published: 2018

Publication Type: Journal

Abstract

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Authors: Nikolaos Psonis, Aglaia Antoniou, Emmanouela Karameta, Adam D Leaché, Panayiota Kotsakiozi, Diego Darriba, Alexey Kozlov, Alexandros Stamatakis, Dimitris Poursanidis, Oleg Kukushkin, others

Date Published: 2018

Publication Type: Journal

Abstract (Expand)

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. 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 2048 cores. EPA-NG is available under the AGPLv3 license: https://github.com/Pbdas/epa-ng.

Authors: Pierre Barbera, Alexey M Kozlov, Lucas Czech, Benoit Morel, Diego Darriba, Tomáš Flouri, Alexandros Stamatakis

Date Published: 2018

Publication Type: Journal

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