Pattern recognition analysis on long noncoding RNAs: a tool for prediction in plants.
Identifieur interne : 000801 ( Main/Exploration ); précédent : 000800; suivant : 000802Pattern recognition analysis on long noncoding RNAs: a tool for prediction in plants.
Auteurs : Tatianne Da Costa Negri [Brésil] ; Wonder Alexandre Luz Alves [Brésil] ; Pedro Henrique Bugatti [Brésil] ; Priscila Tiemi Maeda Saito [Brésil] ; Douglas Silva Domingues [Brésil] ; Alexandre Rossi Paschoal [Brésil]Source :
- Briefings in bioinformatics [ 1477-4054 ] ; 2019.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- chemical , genetics : RNA, Long Noncoding, RNA, Plant.
- classification : Plants.
- genetics : Plants.
- methods : Computational Biology.
- Gene Expression Regulation, Plant, Machine Learning, Species Specificity.
Abstract
MOTIVATION
Long noncoding RNAs (lncRNAs) correspond to a eukaryotic noncoding RNA class that gained great attention in the past years as a higher layer of regulation for gene expression in cells. There is, however, a lack of specific computational approaches to reliably predict lncRNA in plants, which contrast the variety of prediction tools available for mammalian lncRNAs. This distinction is not that obvious, given that biological features and mechanisms generating lncRNAs in the cell are likely different between animals and plants. Considering this, we present a machine learning analysis and a classifier approach called RNAplonc (https://github.com/TatianneNegri/RNAplonc/) to identify lncRNAs in plants.
RESULTS
Our feature selection analysis considered 5468 features, and it used only 16 features to robustly identify lncRNA with the REPTree algorithm. That was the base to create the model and train it with lncRNA and mRNA data from five plant species (thale cress, cucumber, soybean, poplar and Asian rice). After an extensive comparison with other tools largely used in plants (CPC, CPC2, CPAT and PLncPRO), we found that RNAplonc produced more reliable lncRNA predictions from plant transcripts with 87.5% of the best result in eight tests in eight species from the GreeNC database and four independent studies in monocotyledonous (Brachypodium) and eudicotyledonous (Populus and Gossypium) species.
DOI: 10.1093/bib/bby034
PubMed: 29697740
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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Paraná, UTFPR, Campus Cornélio, Procópio, Brazil and Informatics and Knowledge Management Graduate Program, Universidade Nove de Julho, São Paulo, Brazil.</nlm:affiliation><country xml:lang="fr">Brésil</country><wicri:regionArea>Department of Computer Science, Bioinformatics Graduate Program (PPGBIOINFO), Federal University of Technology - Paraná, UTFPR, Campus Cornélio, Procópio, Brazil and Informatics and Knowledge Management Graduate Program, Universidade Nove de Julho, São Paulo</wicri:regionArea><placeName><settlement type="city">São Paulo</settlement><region type="state">État de São Paulo</region></placeName></affiliation></author><author><name sortKey="Alves, Wonder Alexandre Luz" sort="Alves, Wonder Alexandre Luz" uniqKey="Alves W" first="Wonder Alexandre Luz" last="Alves">Wonder Alexandre Luz Alves</name><affiliation wicri:level="3"><nlm:affiliation>Informatics and Knowledge Management Graduate Program, Universidade Nove de Julho, São Paulo, Brazil.</nlm:affiliation><country xml:lang="fr">Brésil</country><wicri:regionArea>Informatics and Knowledge Management Graduate Program, Universidade Nove de Julho, São Paulo</wicri:regionArea><placeName><settlement type="city">São Paulo</settlement><region type="state">État de São Paulo</region></placeName></affiliation></author><author><name sortKey="Bugatti, Pedro Henrique" sort="Bugatti, Pedro Henrique" uniqKey="Bugatti P" first="Pedro Henrique" last="Bugatti">Pedro Henrique Bugatti</name><affiliation wicri:level="1"><nlm:affiliation>Department of Computer Science, Bioinformatics Graduate Program (PPGBIOINFO), Federal University of Technology - 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Paraná, UTFPR, Campus Cornélio, Procópio</wicri:regionArea><wicri:noRegion>Procópio</wicri:noRegion></affiliation></author></analytic><series><title level="j">Briefings in bioinformatics</title><idno type="eISSN">1477-4054</idno><imprint><date when="2019" type="published">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Computational Biology (methods)</term><term>Gene Expression Regulation, Plant (MeSH)</term><term>Machine Learning (MeSH)</term><term>Plants (classification)</term><term>Plants (genetics)</term><term>RNA, Long Noncoding (genetics)</term><term>RNA, Plant (genetics)</term><term>Species Specificity (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>ARN des plantes (génétique)</term><term>ARN long non codant (génétique)</term><term>Apprentissage machine (MeSH)</term><term>Biologie informatique (méthodes)</term><term>Plantes (classification)</term><term>Plantes (génétique)</term><term>Régulation de l'expression des gènes végétaux (MeSH)</term><term>Spécificité d'espèce (MeSH)</term></keywords><keywords scheme="MESH" type="chemical" qualifier="genetics" xml:lang="en"><term>RNA, Long Noncoding</term><term>RNA, Plant</term></keywords><keywords scheme="MESH" qualifier="classification" xml:lang="en"><term>Plants</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Plants</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>ARN des plantes</term><term>ARN long non codant</term><term>Plantes</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Computational Biology</term></keywords><keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Biologie informatique</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Gene Expression Regulation, Plant</term><term>Machine Learning</term><term>Species Specificity</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Apprentissage machine</term><term>Régulation de l'expression des gènes végétaux</term><term>Spécificité d'espèce</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>MOTIVATION</b></p><p>Long noncoding RNAs (lncRNAs) correspond to a eukaryotic noncoding RNA class that gained great attention in the past years as a higher layer of regulation for gene expression in cells. There is, however, a lack of specific computational approaches to reliably predict lncRNA in plants, which contrast the variety of prediction tools available for mammalian lncRNAs. This distinction is not that obvious, given that biological features and mechanisms generating lncRNAs in the cell are likely different between animals and plants. Considering this, we present a machine learning analysis and a classifier approach called RNAplonc (https://github.com/TatianneNegri/RNAplonc/) to identify lncRNAs in plants.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>Our feature selection analysis considered 5468 features, and it used only 16 features to robustly identify lncRNA with the REPTree algorithm. That was the base to create the model and train it with lncRNA and mRNA data from five plant species (thale cress, cucumber, soybean, poplar and Asian rice). After an extensive comparison with other tools largely used in plants (CPC, CPC2, CPAT and PLncPRO), we found that RNAplonc produced more reliable lncRNA predictions from plant transcripts with 87.5% of the best result in eight tests in eight species from the GreeNC database and four independent studies in monocotyledonous (Brachypodium) and eudicotyledonous (Populus and Gossypium) species.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">29697740</PMID><DateCompleted><Year>2020</Year><Month>02</Month><Day>17</Day></DateCompleted><DateRevised><Year>2020</Year><Month>02</Month><Day>17</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1477-4054</ISSN><JournalIssue CitedMedium="Internet"><Volume>20</Volume><Issue>2</Issue><PubDate><Year>2019</Year><Month>03</Month><Day>25</Day></PubDate></JournalIssue><Title>Briefings in bioinformatics</Title><ISOAbbreviation>Brief Bioinform</ISOAbbreviation></Journal><ArticleTitle>Pattern recognition analysis on long noncoding RNAs: a tool for prediction in plants.</ArticleTitle><Pagination><MedlinePgn>682-689</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1093/bib/bby034</ELocationID><Abstract><AbstractText Label="MOTIVATION">Long noncoding RNAs (lncRNAs) correspond to a eukaryotic noncoding RNA class that gained great attention in the past years as a higher layer of regulation for gene expression in cells. There is, however, a lack of specific computational approaches to reliably predict lncRNA in plants, which contrast the variety of prediction tools available for mammalian lncRNAs. This distinction is not that obvious, given that biological features and mechanisms generating lncRNAs in the cell are likely different between animals and plants. Considering this, we present a machine learning analysis and a classifier approach called RNAplonc (https://github.com/TatianneNegri/RNAplonc/) to identify lncRNAs in plants.</AbstractText><AbstractText Label="RESULTS">Our feature selection analysis considered 5468 features, and it used only 16 features to robustly identify lncRNA with the REPTree algorithm. That was the base to create the model and train it with lncRNA and mRNA data from five plant species (thale cress, cucumber, soybean, poplar and Asian rice). After an extensive comparison with other tools largely used in plants (CPC, CPC2, CPAT and PLncPRO), we found that RNAplonc produced more reliable lncRNA predictions from plant transcripts with 87.5% of the best result in eight tests in eight species from the GreeNC database and four independent studies in monocotyledonous (Brachypodium) and eudicotyledonous (Populus and Gossypium) species.</AbstractText><CopyrightInformation>© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Negri</LastName><ForeName>Tatianne da Costa</ForeName><Initials>TDC</Initials><AffiliationInfo><Affiliation>Department of Computer Science, Bioinformatics Graduate Program (PPGBIOINFO), Federal University of Technology - Paraná, UTFPR, Campus Cornélio, Procópio, Brazil and Informatics and Knowledge Management Graduate Program, Universidade Nove de Julho, São Paulo, Brazil.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Alves</LastName><ForeName>Wonder Alexandre Luz</ForeName><Initials>WAL</Initials><AffiliationInfo><Affiliation>Informatics and Knowledge Management Graduate Program, Universidade Nove de Julho, São Paulo, Brazil.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Bugatti</LastName><ForeName>Pedro Henrique</ForeName><Initials>PH</Initials><AffiliationInfo><Affiliation>Department of Computer Science, Bioinformatics Graduate Program (PPGBIOINFO), Federal University of Technology - Paraná, UTFPR, Campus Cornélio, Procópio, Brazil.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Saito</LastName><ForeName>Priscila Tiemi Maeda</ForeName><Initials>PTM</Initials><AffiliationInfo><Affiliation>Department of Computer Science, Bioinformatics Graduate Program (PPGBIOINFO), Federal University of Technology - Paraná, UTFPR, Campus Cornélio, Procópio, Brazil.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Domingues</LastName><ForeName>Douglas Silva</ForeName><Initials>DS</Initials><AffiliationInfo><Affiliation>Department of Computer Science, Bioinformatics Graduate Program (PPGBIOINFO), Federal University of Technology - Paraná, UTFPR, Campus Cornélio, Procópio, Brazil and Department of Botany, Institute of Biosciences, São Paulo State University, UNESP, Rio Claro, SP, Brazil.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Paschoal</LastName><ForeName>Alexandre Rossi</ForeName><Initials>AR</Initials><AffiliationInfo><Affiliation>Department of Computer Science, Bioinformatics Graduate Program (PPGBIOINFO), Federal University of Technology - Paraná, UTFPR, Campus Cornélio, Procópio, Brazil.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType><PublicationType UI="D016454">Review</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Brief Bioinform</MedlineTA><NlmUniqueID>100912837</NlmUniqueID><ISSNLinking>1467-5463</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D062085">RNA, Long Noncoding</NameOfSubstance></Chemical><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D018749">RNA, Plant</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D019295" MajorTopicYN="N">Computational Biology</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D018506" MajorTopicYN="N">Gene Expression Regulation, Plant</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000069550" MajorTopicYN="N">Machine Learning</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D010944" MajorTopicYN="N">Plants</DescriptorName><QualifierName UI="Q000145" MajorTopicYN="N">classification</QualifierName><QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D062085" MajorTopicYN="N">RNA, Long Noncoding</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D018749" MajorTopicYN="N">RNA, Plant</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D013045" MajorTopicYN="N">Species Specificity</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">bioinformatics</Keyword><Keyword MajorTopicYN="Y">features</Keyword><Keyword MajorTopicYN="Y">long RNAs</Keyword><Keyword MajorTopicYN="Y">machine learning</Keyword><Keyword MajorTopicYN="Y">pattern recognition</Keyword><Keyword MajorTopicYN="Y">tool</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2017</Year><Month>12</Month><Day>27</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2018</Year><Month>03</Month><Day>30</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2018</Year><Month>4</Month><Day>27</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>2</Month><Day>18</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2018</Year><Month>4</Month><Day>27</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">29697740</ArticleId><ArticleId IdType="pii">4985385</ArticleId><ArticleId IdType="doi">10.1093/bib/bby034</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>Brésil</li></country><region><li>État de São Paulo</li></region><settlement><li>São Paulo</li></settlement></list><tree><country name="Brésil"><region name="État de São Paulo"><name sortKey="Negri, Tatianne Da Costa" sort="Negri, Tatianne Da Costa" uniqKey="Negri T" first="Tatianne Da Costa" last="Negri">Tatianne Da Costa Negri</name></region><name sortKey="Alves, Wonder Alexandre Luz" sort="Alves, Wonder Alexandre Luz" uniqKey="Alves W" first="Wonder Alexandre Luz" last="Alves">Wonder Alexandre Luz Alves</name><name sortKey="Bugatti, Pedro Henrique" sort="Bugatti, Pedro Henrique" uniqKey="Bugatti P" first="Pedro Henrique" last="Bugatti">Pedro Henrique Bugatti</name><name sortKey="Domingues, Douglas Silva" sort="Domingues, Douglas Silva" uniqKey="Domingues D" first="Douglas Silva" last="Domingues">Douglas Silva Domingues</name><name sortKey="Paschoal, Alexandre Rossi" sort="Paschoal, Alexandre Rossi" uniqKey="Paschoal A" first="Alexandre Rossi" last="Paschoal">Alexandre Rossi Paschoal</name><name sortKey="Saito, Priscila Tiemi Maeda" sort="Saito, Priscila Tiemi Maeda" uniqKey="Saito P" first="Priscila Tiemi Maeda" last="Saito">Priscila Tiemi Maeda Saito</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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