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K-Pax2: Bayesian identification of cluster-defining amino acid positions in large sequence datasets

The recent growth in publicly available sequence data has introduced new opportunities for studying microbial evolution and spread. Because the pace of sequence accumulation tends to exceed the pace of experimental studies of protein function and the roles of individual amino acids, statistical tool...

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Autores principales: Pessia, Alberto, Grad, Yonatan, Cobey, Sarah, Puranen, Juha Santeri, Corander, Jukka
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Society for General Microbiology 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5320600/
https://www.ncbi.nlm.nih.gov/pubmed/28348810
http://dx.doi.org/10.1099/mgen.0.000025
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author Pessia, Alberto
Grad, Yonatan
Cobey, Sarah
Puranen, Juha Santeri
Corander, Jukka
author_facet Pessia, Alberto
Grad, Yonatan
Cobey, Sarah
Puranen, Juha Santeri
Corander, Jukka
author_sort Pessia, Alberto
collection PubMed
description The recent growth in publicly available sequence data has introduced new opportunities for studying microbial evolution and spread. Because the pace of sequence accumulation tends to exceed the pace of experimental studies of protein function and the roles of individual amino acids, statistical tools to identify meaningful patterns in protein diversity are essential. Large sequence alignments from fast-evolving micro-organisms are particularly challenging to dissect using standard tools from phylogenetics and multivariate statistics because biologically relevant functional signals are easily masked by neutral variation and noise. To meet this need, a novel computational method is introduced that is easily executed in parallel using a cluster environment and can handle thousands of sequences with minimal subjective input from the user. The usefulness of this kind of machine learning is demonstrated by applying it to nearly 5000 haemagglutinin sequences of influenza A/H3N2.Antigenic and 3D structural mapping of the results show that the method can recover the major jumps in antigenic phenotype that occurred between 1968 and 2013 and identify specific amino acids associated with these changes. The method is expected to provide a useful tool to uncover patterns of protein evolution.
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spelling pubmed-53206002017-03-27 K-Pax2: Bayesian identification of cluster-defining amino acid positions in large sequence datasets Pessia, Alberto Grad, Yonatan Cobey, Sarah Puranen, Juha Santeri Corander, Jukka Microb Genom Research Paper The recent growth in publicly available sequence data has introduced new opportunities for studying microbial evolution and spread. Because the pace of sequence accumulation tends to exceed the pace of experimental studies of protein function and the roles of individual amino acids, statistical tools to identify meaningful patterns in protein diversity are essential. Large sequence alignments from fast-evolving micro-organisms are particularly challenging to dissect using standard tools from phylogenetics and multivariate statistics because biologically relevant functional signals are easily masked by neutral variation and noise. To meet this need, a novel computational method is introduced that is easily executed in parallel using a cluster environment and can handle thousands of sequences with minimal subjective input from the user. The usefulness of this kind of machine learning is demonstrated by applying it to nearly 5000 haemagglutinin sequences of influenza A/H3N2.Antigenic and 3D structural mapping of the results show that the method can recover the major jumps in antigenic phenotype that occurred between 1968 and 2013 and identify specific amino acids associated with these changes. The method is expected to provide a useful tool to uncover patterns of protein evolution. Society for General Microbiology 2015-07-15 /pmc/articles/PMC5320600/ /pubmed/28348810 http://dx.doi.org/10.1099/mgen.0.000025 Text en © 2015 The Authors http://creativecommons.org/licenses/by/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Research Paper
Pessia, Alberto
Grad, Yonatan
Cobey, Sarah
Puranen, Juha Santeri
Corander, Jukka
K-Pax2: Bayesian identification of cluster-defining amino acid positions in large sequence datasets
title K-Pax2: Bayesian identification of cluster-defining amino acid positions in large sequence datasets
title_full K-Pax2: Bayesian identification of cluster-defining amino acid positions in large sequence datasets
title_fullStr K-Pax2: Bayesian identification of cluster-defining amino acid positions in large sequence datasets
title_full_unstemmed K-Pax2: Bayesian identification of cluster-defining amino acid positions in large sequence datasets
title_short K-Pax2: Bayesian identification of cluster-defining amino acid positions in large sequence datasets
title_sort k-pax2: bayesian identification of cluster-defining amino acid positions in large sequence datasets
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5320600/
https://www.ncbi.nlm.nih.gov/pubmed/28348810
http://dx.doi.org/10.1099/mgen.0.000025
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