Predicting the evolution of Escherichia coli by a data-driven approach

Predicting the evolution of Escherichia coli by a data-driven approach

How reproducible evolutionary processes are remains an important question in evolutionary biology. Here, the authors compile a compendium of more than 15,000 mutation events for Escherichia coli under 178 distinct environmental settings, and develop an ensemble of predictors to predict evolution at...

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Main Authors: Xiaokang Wang, Violeta Zorraquino, Minseung Kim, Athanasios Tsoukalas, Ilias Tagkopoulos
Format: Article
Language:English
Published: Nature Publishing Group 2018-09-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-018-05807-z
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spelling doaj-3d716d97abf6460ea0ada536a16d3e362021-05-11T09:29:32ZengNature Publishing GroupNature Communications2041-17232018-09-019111210.1038/s41467-018-05807-zPredicting the evolution of Escherichia coli by a data-driven approachXiaokang Wang0Violeta Zorraquino1Minseung Kim2Athanasios Tsoukalas3Ilias Tagkopoulos4Department of Biomedical Engineering, University of California, DavisGenome Center, University of California, DavisGenome Center, University of California, DavisGenome Center, University of California, DavisGenome Center, University of California, DavisHow reproducible evolutionary processes are remains an important question in evolutionary biology. Here, the authors compile a compendium of more than 15,000 mutation events for Escherichia coli under 178 distinct environmental settings, and develop an ensemble of predictors to predict evolution at a gene level.https://doi.org/10.1038/s41467-018-05807-z
collection DOAJ
language English
format Article
sources DOAJ
author Xiaokang Wang
Violeta Zorraquino
Minseung Kim
Athanasios Tsoukalas
Ilias Tagkopoulos
spellingShingle Xiaokang Wang
Violeta Zorraquino
Minseung Kim
Athanasios Tsoukalas
Ilias Tagkopoulos
Predicting the evolution of Escherichia coli by a data-driven approach
Nature Communications
author_facet Xiaokang Wang
Violeta Zorraquino
Minseung Kim
Athanasios Tsoukalas
Ilias Tagkopoulos
author_sort Xiaokang Wang
title Predicting the evolution of Escherichia coli by a data-driven approach
title_short Predicting the evolution of Escherichia coli by a data-driven approach
title_full Predicting the evolution of Escherichia coli by a data-driven approach
title_fullStr Predicting the evolution of Escherichia coli by a data-driven approach
title_full_unstemmed Predicting the evolution of Escherichia coli by a data-driven approach
title_sort predicting the evolution of escherichia coli by a data-driven approach
publisher Nature Publishing Group
series Nature Communications
issn 2041-1723
publishDate 2018-09-01
description How reproducible evolutionary processes are remains an important question in evolutionary biology. Here, the authors compile a compendium of more than 15,000 mutation events for Escherichia coli under 178 distinct environmental settings, and develop an ensemble of predictors to predict evolution at a gene level.
url https://doi.org/10.1038/s41467-018-05807-z
work_keys_str_mv AT xiaokangwang predictingtheevolutionofescherichiacolibyadatadrivenapproach
AT violetazorraquino predictingtheevolutionofescherichiacolibyadatadrivenapproach
AT minseungkim predictingtheevolutionofescherichiacolibyadatadrivenapproach
AT athanasiostsoukalas predictingtheevolutionofescherichiacolibyadatadrivenapproach
AT iliastagkopoulos predictingtheevolutionofescherichiacolibyadatadrivenapproach
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