A k-mer grammar analysis to uncover maize regulatory architecture.
Identifieur interne : 000611 ( PubMed/Curation ); précédent : 000610; suivant : 000612A k-mer grammar analysis to uncover maize regulatory architecture.
Auteurs : María Katherine Mejía-Guerra [États-Unis] ; Edward S. Buckler [États-Unis]Source :
- BMC plant biology [ 1471-2229 ] ; 2019.
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Abstract
Only a small percentage of the genome sequence is involved in regulation of gene expression, but to biochemically identify this portion is expensive and laborious. In species like maize, with diverse intergenic regions and lots of repetitive elements, this is an especially challenging problem that limits the use of the data from one line to the other. While regulatory regions are rare, they do have characteristic chromatin contexts and sequence organization (the grammar) with which they can be identified.
DOI: 10.1186/s12870-019-1693-2
PubMed: 30876396
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">A k-mer grammar analysis to uncover maize regulatory architecture.</title><author><name sortKey="Mejia Guerra, Maria Katherine" sort="Mejia Guerra, Maria Katherine" uniqKey="Mejia Guerra M" first="María Katherine" last="Mejía-Guerra">María Katherine Mejía-Guerra</name><affiliation wicri:level="1"><nlm:affiliation>Institute for Genomic Diversity, Cornell University, 175 Biotechnology Building, Ithaca, 14853, NY, USA. mm2842@cornell.edu.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Institute for Genomic Diversity, Cornell University, 175 Biotechnology Building, Ithaca, 14853, NY</wicri:regionArea></affiliation></author><author><name sortKey="Buckler, Edward S" sort="Buckler, Edward S" uniqKey="Buckler E" first="Edward S" last="Buckler">Edward S. Buckler</name><affiliation wicri:level="1"><nlm:affiliation>Institute for Genomic Diversity, Cornell University, 175 Biotechnology Building, Ithaca, 14853, NY, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Institute for Genomic Diversity, Cornell University, 175 Biotechnology Building, Ithaca, 14853, NY</wicri:regionArea></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2019">2019</date><idno type="RBID">pubmed:30876396</idno><idno type="pmid">30876396</idno><idno type="doi">10.1186/s12870-019-1693-2</idno><idno type="wicri:Area/PubMed/Corpus">000611</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000611</idno><idno type="wicri:Area/PubMed/Curation">000611</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">000611</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">A k-mer grammar analysis to uncover maize regulatory architecture.</title><author><name sortKey="Mejia Guerra, Maria Katherine" sort="Mejia Guerra, Maria Katherine" uniqKey="Mejia Guerra M" first="María Katherine" last="Mejía-Guerra">María Katherine Mejía-Guerra</name><affiliation wicri:level="1"><nlm:affiliation>Institute for Genomic Diversity, Cornell University, 175 Biotechnology Building, Ithaca, 14853, NY, USA. mm2842@cornell.edu.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Institute for Genomic Diversity, Cornell University, 175 Biotechnology Building, Ithaca, 14853, NY</wicri:regionArea></affiliation></author><author><name sortKey="Buckler, Edward S" sort="Buckler, Edward S" uniqKey="Buckler E" first="Edward S" last="Buckler">Edward S. Buckler</name><affiliation wicri:level="1"><nlm:affiliation>Institute for Genomic Diversity, Cornell University, 175 Biotechnology Building, Ithaca, 14853, NY, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Institute for Genomic Diversity, Cornell University, 175 Biotechnology Building, Ithaca, 14853, NY</wicri:regionArea></affiliation></author></analytic><series><title level="j">BMC plant biology</title><idno type="eISSN">1471-2229</idno><imprint><date when="2019" type="published">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Genome, Plant</term><term>Machine Learning</term><term>Models, Genetic</term><term>Regulatory Sequences, Nucleic Acid</term><term>Software</term><term>Zea mays (genetics)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Apprentissage machine</term><term>Génome végétal</term><term>Logiciel</term><term>Modèles génétiques</term><term>Séquences d'acides nucléiques régulatrices</term><term>Zea mays (génétique)</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Zea mays</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Zea mays</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Genome, Plant</term><term>Machine Learning</term><term>Models, Genetic</term><term>Regulatory Sequences, Nucleic Acid</term><term>Software</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Apprentissage machine</term><term>Génome végétal</term><term>Logiciel</term><term>Modèles génétiques</term><term>Séquences d'acides nucléiques régulatrices</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Only a small percentage of the genome sequence is involved in regulation of gene expression, but to biochemically identify this portion is expensive and laborious. In species like maize, with diverse intergenic regions and lots of repetitive elements, this is an especially challenging problem that limits the use of the data from one line to the other. While regulatory regions are rare, they do have characteristic chromatin contexts and sequence organization (the grammar) with which they can be identified.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">30876396</PMID><DateCompleted><Year>2019</Year><Month>03</Month><Day>29</Day></DateCompleted><DateRevised><Year>2020</Year><Month>02</Month><Day>25</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1471-2229</ISSN><JournalIssue CitedMedium="Internet"><Volume>19</Volume><Issue>1</Issue><PubDate><Year>2019</Year><Month>Mar</Month><Day>15</Day></PubDate></JournalIssue><Title>BMC plant biology</Title><ISOAbbreviation>BMC Plant Biol.</ISOAbbreviation></Journal><ArticleTitle>A k-mer grammar analysis to uncover maize regulatory architecture.</ArticleTitle><Pagination><MedlinePgn>103</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/s12870-019-1693-2</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Only a small percentage of the genome sequence is involved in regulation of gene expression, but to biochemically identify this portion is expensive and laborious. In species like maize, with diverse intergenic regions and lots of repetitive elements, this is an especially challenging problem that limits the use of the data from one line to the other. While regulatory regions are rare, they do have characteristic chromatin contexts and sequence organization (the grammar) with which they can be identified.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">We developed a computational framework to exploit this sequence arrangement. The models learn to classify regulatory regions based on sequence features - k-mers. To do this, we borrowed two approaches from the field of natural language processing: (1) "bag-of-words" which is commonly used for differentially weighting key words in tasks like sentiment analyses, and (2) a vector-space model using word2vec (vector-k-mers), that captures semantic and linguistic relationships between words. We built "bag-of-k-mers" and "vector-k-mers" models that distinguish between regulatory and non-regulatory regions with an average accuracy above 90%. Our "bag-of-k-mers" achieved higher overall accuracy, while the "vector-k-mers" models were more useful in highlighting key groups of sequences within the regulatory regions.</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">These models now provide powerful tools to annotate regulatory regions in other maize lines beyond the reference, at low cost and with high accuracy.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Mejía-Guerra</LastName><ForeName>María Katherine</ForeName><Initials>MK</Initials><Identifier Source="ORCID">http://orcid.org/0000-0002-2791-182X</Identifier><AffiliationInfo><Affiliation>Institute for Genomic Diversity, Cornell University, 175 Biotechnology Building, Ithaca, 14853, NY, USA. mm2842@cornell.edu.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Buckler</LastName><ForeName>Edward S</ForeName><Initials>ES</Initials><AffiliationInfo><Affiliation>Institute for Genomic Diversity, Cornell University, 175 Biotechnology Building, Ithaca, 14853, NY, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>USDA-ARS, Research Geneticist, USDA ARS Robert Holley Center, Ithaca, 14853, NY, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Plant Breeding and Genetics, Cornell University, 159 Biotechnology Building, Ithaca, 14853, NY, USA.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>IOS #1238014</GrantID><Agency>National Science Foundation</Agency><Country></Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2019</Year><Month>03</Month><Day>15</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>BMC Plant Biol</MedlineTA><NlmUniqueID>100967807</NlmUniqueID><ISSNLinking>1471-2229</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D018745" MajorTopicYN="Y">Genome, Plant</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000069550" MajorTopicYN="N">Machine Learning</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008957" MajorTopicYN="Y">Models, Genetic</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012045" MajorTopicYN="Y">Regulatory Sequences, Nucleic Acid</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012984" MajorTopicYN="Y">Software</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D003313" MajorTopicYN="N">Zea mays</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Crops genomics</Keyword><Keyword MajorTopicYN="N">Gene regulatory regions</Keyword><Keyword MajorTopicYN="N">Machine learning models</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2018</Year><Month>04</Month><Day>25</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2019</Year><Month>02</Month><Day>21</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2019</Year><Month>3</Month><Day>17</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2019</Year><Month>3</Month><Day>17</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2019</Year><Month>3</Month><Day>30</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">30876396</ArticleId><ArticleId IdType="doi">10.1186/s12870-019-1693-2</ArticleId><ArticleId IdType="pii">10.1186/s12870-019-1693-2</ArticleId><ArticleId 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