Identification of positive selection in genes is greatly improved by using experimentally informed site-specific models
Identifieur interne : 000D42 ( Ncbi/Merge ); précédent : 000D41; suivant : 000D43Identification of positive selection in genes is greatly improved by using experimentally informed site-specific models
Auteurs : Jesse D. Bloom [États-Unis]Source :
- Biology Direct [ 1745-6150 ] ; 2017.
Abstract
Sites of positive selection are identified by comparing observed evolutionary patterns to those expected under a null model for evolution in the absence of such selection. For protein-coding genes, the most common null model is that nonsynonymous and synonymous mutations fix at equal rates; this unrealistic model has limited power to detect many interesting forms of selection.
I describe a new approach that uses a null model based on experimental measurements of a gene’s site-specific amino-acid preferences generated by deep mutational scanning in the lab. This null model makes it possible to identify both diversifying selection for repeated amino-acid change and differential selection for mutations to amino acids that are unexpected given the measurements made in the lab. I show that this approach identifies sites of adaptive substitutions in four genes (lactamase, Gal4, influenza nucleoprotein, and influenza hemagglutinin) far better than a comparable method that simply compares the rates of nonsynonymous and synonymous substitutions.
As rapid increases in biological data enable increasingly nuanced descriptions of the constraints on individual protein sites, approaches like the one here can improve our ability to identify many interesting forms of selection in natural sequences.
This article was reviewed by Sebastian Maurer-Stroh, Olivier Tenaillon, and Tal Pupko. All three reviewers are members of the
The online version of this article (doi:10.1186/s13062-016-0172-z) contains supplementary material, which is available to authorized users.
Url:
DOI: 10.1186/s13062-016-0172-z
PubMed: 28095902
PubMed Central: 5240389
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PMC:5240389Le document en format XML
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Identification of positive selection in genes is greatly improved by using experimentally informed site-specific models</title>
<author><name sortKey="Bloom, Jesse D" sort="Bloom, Jesse D" uniqKey="Bloom J" first="Jesse D." last="Bloom">Jesse D. Bloom</name>
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>Sites of positive selection are identified by comparing observed evolutionary patterns to those expected under a null model for evolution in the absence of such selection. For protein-coding genes, the most common null model is that nonsynonymous and synonymous mutations fix at equal rates; this unrealistic model has limited power to detect many interesting forms of selection.</p>
</sec>
<sec><title>Results</title>
<p>I describe a new approach that uses a null model based on experimental measurements of a gene’s site-specific amino-acid preferences generated by deep mutational scanning in the lab. This null model makes it possible to identify both diversifying selection for repeated amino-acid change and differential selection for mutations to amino acids that are unexpected given the measurements made in the lab. I show that this approach identifies sites of adaptive substitutions in four genes (lactamase, Gal4, influenza nucleoprotein, and influenza hemagglutinin) far better than a comparable method that simply compares the rates of nonsynonymous and synonymous substitutions.</p>
</sec>
<sec><title>Conclusions</title>
<p>As rapid increases in biological data enable increasingly nuanced descriptions of the constraints on individual protein sites, approaches like the one here can improve our ability to identify many interesting forms of selection in natural sequences.</p>
</sec>
<sec><title>Reviewers</title>
<p>This article was reviewed by Sebastian Maurer-Stroh, Olivier Tenaillon, and Tal Pupko. All three reviewers are members of the <italic>Biology Direct</italic>
editorial board.</p>
</sec>
<sec><title>Electronic supplementary material</title>
<p>The online version of this article (doi:10.1186/s13062-016-0172-z) contains supplementary material, which is available to authorized users.</p>
</sec>
</div>
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</author>
<author><name sortKey="Usacheva, Ov" uniqKey="Usacheva O">OV Usacheva</name>
</author>
<author><name sortKey="Prilipov, Ag" uniqKey="Prilipov A">AG Prilipov</name>
</author>
<author><name sortKey="Webster, Rg" uniqKey="Webster R">RG Webster</name>
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<author><name sortKey="Kaverin, Nv" uniqKey="Kaverin N">NV Kaverin</name>
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<author><name sortKey="Berkhoff, E" uniqKey="Berkhoff E">E Berkhoff</name>
</author>
<author><name sortKey="Nieuwkoop, N" uniqKey="Nieuwkoop N">N Nieuwkoop</name>
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<author><name sortKey="De Mutsert, G" uniqKey="De Mutsert G">G de Mutsert</name>
</author>
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<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Biol Direct</journal-id>
<journal-id journal-id-type="iso-abbrev">Biol. Direct</journal-id>
<journal-title-group><journal-title>Biology Direct</journal-title>
</journal-title-group>
<issn pub-type="epub">1745-6150</issn>
<publisher><publisher-name>BioMed Central</publisher-name>
<publisher-loc>London</publisher-loc>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">28095902</article-id>
<article-id pub-id-type="pmc">5240389</article-id>
<article-id pub-id-type="publisher-id">172</article-id>
<article-id pub-id-type="doi">10.1186/s13062-016-0172-z</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Research</subject>
</subj-group>
</article-categories>
<title-group><article-title>Identification of positive selection in genes is greatly improved by using experimentally informed site-specific models</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">http://orcid.org/0000-0003-1267-3408</contrib-id>
<name><surname>Bloom</surname>
<given-names>Jesse D.</given-names>
</name>
<address><email>jbloom@fredhutch.org</email>
</address>
<xref ref-type="aff" rid="Aff1"></xref>
</contrib>
<aff id="Aff1">Division of Basic Sciences and Computational Biology Program, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, 98109 WA USA</aff>
</contrib-group>
<pub-date pub-type="epub"><day>17</day>
<month>1</month>
<year>2017</year>
</pub-date>
<pub-date pub-type="pmc-release"><day>17</day>
<month>1</month>
<year>2017</year>
</pub-date>
<pub-date pub-type="collection"><year>2017</year>
</pub-date>
<volume>12</volume>
<elocation-id>1</elocation-id>
<history><date date-type="received"><day>15</day>
<month>8</month>
<year>2016</year>
</date>
<date date-type="accepted"><day>14</day>
<month>12</month>
<year>2016</year>
</date>
</history>
<permissions><copyright-statement>© The Author(s) 2017</copyright-statement>
<license license-type="OpenAccess"><license-p><bold>Open Access</bold>
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>
), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/publicdomain/zero/1.0/">http://creativecommons.org/publicdomain/zero/1.0/</ext-link>
) applies to the data made available in this article, unless otherwise stated.</license-p>
</license>
</permissions>
<abstract id="Abs1"><sec><title>Background</title>
<p>Sites of positive selection are identified by comparing observed evolutionary patterns to those expected under a null model for evolution in the absence of such selection. For protein-coding genes, the most common null model is that nonsynonymous and synonymous mutations fix at equal rates; this unrealistic model has limited power to detect many interesting forms of selection.</p>
</sec>
<sec><title>Results</title>
<p>I describe a new approach that uses a null model based on experimental measurements of a gene’s site-specific amino-acid preferences generated by deep mutational scanning in the lab. This null model makes it possible to identify both diversifying selection for repeated amino-acid change and differential selection for mutations to amino acids that are unexpected given the measurements made in the lab. I show that this approach identifies sites of adaptive substitutions in four genes (lactamase, Gal4, influenza nucleoprotein, and influenza hemagglutinin) far better than a comparable method that simply compares the rates of nonsynonymous and synonymous substitutions.</p>
</sec>
<sec><title>Conclusions</title>
<p>As rapid increases in biological data enable increasingly nuanced descriptions of the constraints on individual protein sites, approaches like the one here can improve our ability to identify many interesting forms of selection in natural sequences.</p>
</sec>
<sec><title>Reviewers</title>
<p>This article was reviewed by Sebastian Maurer-Stroh, Olivier Tenaillon, and Tal Pupko. All three reviewers are members of the <italic>Biology Direct</italic>
editorial board.</p>
</sec>
<sec><title>Electronic supplementary material</title>
<p>The online version of this article (doi:10.1186/s13062-016-0172-z) contains supplementary material, which is available to authorized users.</p>
</sec>
</abstract>
<kwd-group xml:lang="en"><title>Keywords</title>
<kwd>Deep mutational scanning</kwd>
<kwd>Phylogenetics</kwd>
<kwd>Substitution model</kwd>
<kwd>Diversifying selection</kwd>
<kwd>dN/dS</kwd>
</kwd-group>
<funding-group><award-group><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/100000057</institution-id>
<institution>National Institute of General Medical Sciences</institution>
</institution-wrap>
</funding-source>
<award-id>R01 GM102198</award-id>
<principal-award-recipient><name><surname>Bloom</surname>
<given-names>Jesse D.</given-names>
</name>
</principal-award-recipient>
</award-group>
<award-group><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/100000875</institution-id>
<institution>Pew Charitable Trusts</institution>
</institution-wrap>
</funding-source>
<award-id>Pew Scholars Grant</award-id>
<principal-award-recipient><name><surname>Bloom</surname>
<given-names>Jesse D.</given-names>
</name>
</principal-award-recipient>
</award-group>
</funding-group>
<custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name>
<meta-value>© The Author(s) 2017</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
</pmc>
<affiliations><list><country><li>États-Unis</li>
</country>
<region><li>Washington (État)</li>
</region>
</list>
<tree><country name="États-Unis"><region name="Washington (État)"><name sortKey="Bloom, Jesse D" sort="Bloom, Jesse D" uniqKey="Bloom J" first="Jesse D." last="Bloom">Jesse D. Bloom</name>
</region>
</country>
</tree>
</affiliations>
</record>
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