RGAugury: a pipeline for genome-wide prediction of resistance gene analogs (RGAs) in plants
Identifieur interne : 000610 ( Pmc/Curation ); précédent : 000609; suivant : 000611RGAugury: a pipeline for genome-wide prediction of resistance gene analogs (RGAs) in plants
Auteurs : Pingchuan Li ; Xiande Quan ; Gaofeng Jia ; Jin Xiao [République populaire de Chine] ; Sylvie Cloutier ; Frank M. YouSource :
- BMC Genomics [ 1471-2164 ] ; 2016.
Abstract
Resistance gene analogs (RGAs), such as NBS-encoding proteins, receptor-like protein kinases (RLKs) and receptor-like proteins (RLPs), are potential
An integrative pipeline, named RGAugury, was developed to automate RGA prediction. The pipeline first identifies RGA-related protein domains and motifs, namely nucleotide binding site (NB-ARC), leucine rich repeat (LRR), transmembrane (TM), serine/threonine and tyrosine kinase (STTK), lysin motif (LysM), coiled-coil (CC) and Toll/Interleukin-1 receptor (TIR). RGA candidates are identified and classified into four major families based on the presence of combinations of these RGA domains and motifs: NBS-encoding, TM-CC, and membrane associated RLP and RLK. All time-consuming analyses of the pipeline are paralleled to improve performance. The pipeline was evaluated using the well-annotated Arabidopsis genome. A total of 98.5, 85.2, and 100 % of the reported NBS-encoding genes, membrane associated RLPs and RLKs were validated, respectively. The pipeline was also successfully applied to predict RGAs for 50 sequenced plant genomes. A user-friendly web interface was implemented to ease command line operations, facilitate visualization and simplify result management for multiple datasets.
RGAugury is an efficiently integrative bioinformatics tool for large scale genome-wide identification of RGAs. It is freely available at Bitbucket:
The online version of this article (doi:10.1186/s12864-016-3197-x) contains supplementary material, which is available to authorized users.
Url:
DOI: 10.1186/s12864-016-3197-x
PubMed: 27806688
PubMed Central: 5093994
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Pingchuan Li<affiliation><nlm:aff id="Aff1">Morden Research and Development Centre, Agriculture and Agri-Food Canada, Morden, MB R6M 1Y5 Canada</nlm:aff>
<wicri:noCountry code="subfield">MB R6M 1Y5 Canada</wicri:noCountry>
</affiliation>
<affiliation><nlm:aff id="Aff1">Morden Research and Development Centre, Agriculture and Agri-Food Canada, Morden, MB R6M 1Y5 Canada</nlm:aff>
<wicri:noCountry code="subfield">MB R6M 1Y5 Canada</wicri:noCountry>
</affiliation>
<affiliation><nlm:aff id="Aff1">Morden Research and Development Centre, Agriculture and Agri-Food Canada, Morden, MB R6M 1Y5 Canada</nlm:aff>
<wicri:noCountry code="subfield">MB R6M 1Y5 Canada</wicri:noCountry>
</affiliation>
<affiliation><nlm:aff id="Aff2">University of Saskatchewan, Saskatoon, SK S7N 5A8 Canada</nlm:aff>
<wicri:noCountry code="subfield">SK S7N 5A8 Canada</wicri:noCountry>
</affiliation>
<affiliation><nlm:aff id="Aff2">University of Saskatchewan, Saskatoon, SK S7N 5A8 Canada</nlm:aff>
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<affiliation wicri:level="1"><nlm:aff id="Aff3">National Key Laboratory of Crop Genetics and Germplasm Enhancement, Cytogenetics Institute, Nanjing Agricultural University, Nanjing, 210095 China</nlm:aff>
<country xml:lang="fr">République populaire de Chine</country>
<wicri:regionArea>National Key Laboratory of Crop Genetics and Germplasm Enhancement, Cytogenetics Institute, Nanjing Agricultural University, Nanjing</wicri:regionArea>
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<affiliation><nlm:aff id="Aff4">Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6 Canada</nlm:aff>
<wicri:noCountry code="subfield">ON K1A 0C6 Canada</wicri:noCountry>
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<series><title level="j">BMC Genomics</title>
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>Resistance gene analogs (RGAs), such as NBS-encoding proteins, receptor-like protein kinases (RLKs) and receptor-like proteins (RLPs), are potential <italic>R</italic>
-genes that contain specific conserved domains and motifs. Thus, RGAs can be predicted based on their conserved structural features using bioinformatics tools. Computer programs have been developed for the identification of individual domains and motifs from the protein sequences of RGAs but none offer a systematic assessment of the different types of RGAs. A user-friendly and efficient pipeline is needed for large-scale genome-wide RGA predictions of the growing number of sequenced plant genomes.</p>
</sec>
<sec><title>Results</title>
<p>An integrative pipeline, named RGAugury, was developed to automate RGA prediction. The pipeline first identifies RGA-related protein domains and motifs, namely nucleotide binding site (NB-ARC), leucine rich repeat (LRR), transmembrane (TM), serine/threonine and tyrosine kinase (STTK), lysin motif (LysM), coiled-coil (CC) and Toll/Interleukin-1 receptor (TIR). RGA candidates are identified and classified into four major families based on the presence of combinations of these RGA domains and motifs: NBS-encoding, TM-CC, and membrane associated RLP and RLK. All time-consuming analyses of the pipeline are paralleled to improve performance. The pipeline was evaluated using the well-annotated Arabidopsis genome. A total of 98.5, 85.2, and 100 % of the reported NBS-encoding genes, membrane associated RLPs and RLKs were validated, respectively. The pipeline was also successfully applied to predict RGAs for 50 sequenced plant genomes. A user-friendly web interface was implemented to ease command line operations, facilitate visualization and simplify result management for multiple datasets.</p>
</sec>
<sec><title>Conclusions</title>
<p>RGAugury is an efficiently integrative bioinformatics tool for large scale genome-wide identification of RGAs. It is freely available at Bitbucket: <ext-link ext-link-type="uri" xlink:href="https://bitbucket.org/yaanlpc/rgaugury">https://bitbucket.org/yaanlpc/rgaugury</ext-link>
.</p>
</sec>
<sec><title>Electronic supplementary material</title>
<p>The online version of this article (doi:10.1186/s12864-016-3197-x) contains supplementary material, which is available to authorized users.</p>
</sec>
</div>
</front>
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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">BMC Genomics</journal-id>
<journal-id journal-id-type="iso-abbrev">BMC Genomics</journal-id>
<journal-title-group><journal-title>BMC Genomics</journal-title>
</journal-title-group>
<issn pub-type="epub">1471-2164</issn>
<publisher><publisher-name>BioMed Central</publisher-name>
<publisher-loc>London</publisher-loc>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">27806688</article-id>
<article-id pub-id-type="pmc">5093994</article-id>
<article-id pub-id-type="publisher-id">3197</article-id>
<article-id pub-id-type="doi">10.1186/s12864-016-3197-x</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Software</subject>
</subj-group>
</article-categories>
<title-group><article-title>RGAugury: a pipeline for genome-wide prediction of resistance gene analogs (RGAs) in plants</article-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Li</surname>
<given-names>Pingchuan</given-names>
</name>
<address><email>Pingchuan.Li@agr.gc.ca</email>
</address>
<xref ref-type="aff" rid="Aff1">1</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Quan</surname>
<given-names>Xiande</given-names>
</name>
<address><email>xiande.quan@canada.ca</email>
</address>
<xref ref-type="aff" rid="Aff1">1</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Jia</surname>
<given-names>Gaofeng</given-names>
</name>
<address><email>gfjia@msn.com</email>
</address>
<xref ref-type="aff" rid="Aff1">1</xref>
<xref ref-type="aff" rid="Aff2">2</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Xiao</surname>
<given-names>Jin</given-names>
</name>
<address><email>xiaojin@njau.edu.cn</email>
</address>
<xref ref-type="aff" rid="Aff1">1</xref>
<xref ref-type="aff" rid="Aff3">3</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Cloutier</surname>
<given-names>Sylvie</given-names>
</name>
<address><email>Sylvie.J.Cloutier@agr.gc.ca</email>
</address>
<xref ref-type="aff" rid="Aff4">4</xref>
</contrib>
<contrib contrib-type="author" corresp="yes"><name><surname>You</surname>
<given-names>Frank M.</given-names>
</name>
<address><email>Frank.You@agr.gc.ca</email>
</address>
<xref ref-type="aff" rid="Aff1">1</xref>
</contrib>
<aff id="Aff1"><label>1</label>
Morden Research and Development Centre, Agriculture and Agri-Food Canada, Morden, MB R6M 1Y5 Canada</aff>
<aff id="Aff2"><label>2</label>
University of Saskatchewan, Saskatoon, SK S7N 5A8 Canada</aff>
<aff id="Aff3"><label>3</label>
National Key Laboratory of Crop Genetics and Germplasm Enhancement, Cytogenetics Institute, Nanjing Agricultural University, Nanjing, 210095 China</aff>
<aff id="Aff4"><label>4</label>
Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6 Canada</aff>
</contrib-group>
<pub-date pub-type="epub"><day>2</day>
<month>11</month>
<year>2016</year>
</pub-date>
<pub-date pub-type="pmc-release"><day>2</day>
<month>11</month>
<year>2016</year>
</pub-date>
<pub-date pub-type="collection"><year>2016</year>
</pub-date>
<volume>17</volume>
<elocation-id>852</elocation-id>
<history><date date-type="received"><day>9</day>
<month>8</month>
<year>2016</year>
</date>
<date date-type="accepted"><day>25</day>
<month>10</month>
<year>2016</year>
</date>
</history>
<permissions><copyright-statement>© The Author(s). 2016</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>Resistance gene analogs (RGAs), such as NBS-encoding proteins, receptor-like protein kinases (RLKs) and receptor-like proteins (RLPs), are potential <italic>R</italic>
-genes that contain specific conserved domains and motifs. Thus, RGAs can be predicted based on their conserved structural features using bioinformatics tools. Computer programs have been developed for the identification of individual domains and motifs from the protein sequences of RGAs but none offer a systematic assessment of the different types of RGAs. A user-friendly and efficient pipeline is needed for large-scale genome-wide RGA predictions of the growing number of sequenced plant genomes.</p>
</sec>
<sec><title>Results</title>
<p>An integrative pipeline, named RGAugury, was developed to automate RGA prediction. The pipeline first identifies RGA-related protein domains and motifs, namely nucleotide binding site (NB-ARC), leucine rich repeat (LRR), transmembrane (TM), serine/threonine and tyrosine kinase (STTK), lysin motif (LysM), coiled-coil (CC) and Toll/Interleukin-1 receptor (TIR). RGA candidates are identified and classified into four major families based on the presence of combinations of these RGA domains and motifs: NBS-encoding, TM-CC, and membrane associated RLP and RLK. All time-consuming analyses of the pipeline are paralleled to improve performance. The pipeline was evaluated using the well-annotated Arabidopsis genome. A total of 98.5, 85.2, and 100 % of the reported NBS-encoding genes, membrane associated RLPs and RLKs were validated, respectively. The pipeline was also successfully applied to predict RGAs for 50 sequenced plant genomes. A user-friendly web interface was implemented to ease command line operations, facilitate visualization and simplify result management for multiple datasets.</p>
</sec>
<sec><title>Conclusions</title>
<p>RGAugury is an efficiently integrative bioinformatics tool for large scale genome-wide identification of RGAs. It is freely available at Bitbucket: <ext-link ext-link-type="uri" xlink:href="https://bitbucket.org/yaanlpc/rgaugury">https://bitbucket.org/yaanlpc/rgaugury</ext-link>
.</p>
</sec>
<sec><title>Electronic supplementary material</title>
<p>The online version of this article (doi:10.1186/s12864-016-3197-x) contains supplementary material, which is available to authorized users.</p>
</sec>
</abstract>
<kwd-group xml:lang="en"><title>Keywords</title>
<kwd>Resistance gene analog (RGA)</kwd>
<kwd>Nucleotide binding site (NBS)</kwd>
<kwd>Receptor like protein (RLP)</kwd>
<kwd>Receptor like kinase (RLK)</kwd>
<kwd>Genome-wide prediction</kwd>
<kwd>Pipeline</kwd>
</kwd-group>
<funding-group><award-group><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/501100000040</institution-id>
<institution>Agriculture and Agri-Food Canada</institution>
</institution-wrap>
</funding-source>
<award-id>J-000006</award-id>
</award-group>
</funding-group>
<custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name>
<meta-value>© The Author(s) 2016</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
</pmc>
</record>
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