Big Data in Plant Science: Resources and Data Mining Tools for Plant Genomics and Proteomics.
Identifieur interne : 000003 ( PubMed/Corpus ); précédent : 000002; suivant : 000004Big Data in Plant Science: Resources and Data Mining Tools for Plant Genomics and Proteomics.
Auteurs : George V. Popescu ; Christos Noutsos ; Sorina C. PopescuSource :
- Methods in molecular biology (Clifton, N.J.) [ 1940-6029 ] ; 2016.
Abstract
In modern plant biology, progress is increasingly defined by the scientists' ability to gather and analyze data sets of high volume and complexity, otherwise known as "big data". Arguably, the largest increase in the volume of plant data sets over the last decade is a consequence of the application of the next-generation sequencing and mass-spectrometry technologies to the study of experimental model and crop plants. The increase in quantity and complexity of biological data brings challenges, mostly associated with data acquisition, processing, and sharing within the scientific community. Nonetheless, big data in plant science create unique opportunities in advancing our understanding of complex biological processes at a level of accuracy without precedence, and establish a base for the plant systems biology. In this chapter, we summarize the major drivers of big data in plant science and big data initiatives in life sciences with a focus on the scope and impact of iPlant, a representative cyberinfrastructure platform for plant science.
DOI: 10.1007/978-1-4939-3572-7_27
PubMed: 27115651
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Popescu</name><affiliation><nlm:affiliation>Department of Biochemistry, Molecular Biology, Plant Pathology and Entomology, Mississippi State University, 411 Dorman Hall, PO Box 9655, Mississippi State, MS, 39762, USA.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2016">2016</date><idno type="doi">10.1007/978-1-4939-3572-7_27</idno><idno type="RBID">pubmed:27115651</idno><idno type="pmid">27115651</idno><idno type="wicri:Area/PubMed/Corpus">000003</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Big Data in Plant Science: Resources and Data Mining Tools for Plant Genomics and Proteomics.</title><author><name sortKey="Popescu, George V" sort="Popescu, George V" uniqKey="Popescu G" first="George V" last="Popescu">George V. Popescu</name><affiliation><nlm:affiliation>National Institute for Laser, Plasma & Radiation Physics, Str. Atomistilor, Nr. 409, Magurele, 077125, Bucharest, Romania. george.popescu@acm.org.</nlm:affiliation></affiliation></author><author><name sortKey="Noutsos, Christos" sort="Noutsos, Christos" uniqKey="Noutsos C" first="Christos" last="Noutsos">Christos Noutsos</name><affiliation><nlm:affiliation>Cold Spring Harbor Laboratories, 1 Bungtown Street, Cold Spring Harbor, NY, 11731, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Popescu, Sorina C" sort="Popescu, Sorina C" uniqKey="Popescu S" first="Sorina C" last="Popescu">Sorina C. Popescu</name><affiliation><nlm:affiliation>Department of Biochemistry, Molecular Biology, Plant Pathology and Entomology, Mississippi State University, 411 Dorman Hall, PO Box 9655, Mississippi State, MS, 39762, USA.</nlm:affiliation></affiliation></author></analytic><series><title level="j">Methods in molecular biology (Clifton, N.J.)</title><idno type="eISSN">1940-6029</idno><imprint><date when="2016" type="published">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">In modern plant biology, progress is increasingly defined by the scientists' ability to gather and analyze data sets of high volume and complexity, otherwise known as "big data". Arguably, the largest increase in the volume of plant data sets over the last decade is a consequence of the application of the next-generation sequencing and mass-spectrometry technologies to the study of experimental model and crop plants. The increase in quantity and complexity of biological data brings challenges, mostly associated with data acquisition, processing, and sharing within the scientific community. Nonetheless, big data in plant science create unique opportunities in advancing our understanding of complex biological processes at a level of accuracy without precedence, and establish a base for the plant systems biology. In this chapter, we summarize the major drivers of big data in plant science and big data initiatives in life sciences with a focus on the scope and impact of iPlant, a representative cyberinfrastructure platform for plant science.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="In-Data-Review"><PMID Version="1">27115651</PMID><DateCreated><Year>2016</Year><Month>04</Month><Day>27</Day></DateCreated><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1940-6029</ISSN><JournalIssue CitedMedium="Internet"><Volume>1415</Volume><PubDate><Year>2016</Year></PubDate></JournalIssue><Title>Methods in molecular biology (Clifton, N.J.)</Title><ISOAbbreviation>Methods Mol. Biol.</ISOAbbreviation></Journal><ArticleTitle>Big Data in Plant Science: Resources and Data Mining Tools for Plant Genomics and Proteomics.</ArticleTitle><Pagination><MedlinePgn>533-47</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1007/978-1-4939-3572-7_27</ELocationID><Abstract><AbstractText>In modern plant biology, progress is increasingly defined by the scientists' ability to gather and analyze data sets of high volume and complexity, otherwise known as "big data". Arguably, the largest increase in the volume of plant data sets over the last decade is a consequence of the application of the next-generation sequencing and mass-spectrometry technologies to the study of experimental model and crop plants. The increase in quantity and complexity of biological data brings challenges, mostly associated with data acquisition, processing, and sharing within the scientific community. Nonetheless, big data in plant science create unique opportunities in advancing our understanding of complex biological processes at a level of accuracy without precedence, and establish a base for the plant systems biology. In this chapter, we summarize the major drivers of big data in plant science and big data initiatives in life sciences with a focus on the scope and impact of iPlant, a representative cyberinfrastructure platform for plant science.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Popescu</LastName><ForeName>George V</ForeName><Initials>GV</Initials><AffiliationInfo><Affiliation>National Institute for Laser, Plasma & Radiation Physics, Str. Atomistilor, Nr. 409, Magurele, 077125, Bucharest, Romania. george.popescu@acm.org.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Noutsos</LastName><ForeName>Christos</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>Cold Spring Harbor Laboratories, 1 Bungtown Street, Cold Spring Harbor, NY, 11731, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Popescu</LastName><ForeName>Sorina C</ForeName><Initials>SC</Initials><AffiliationInfo><Affiliation>Department of Biochemistry, Molecular Biology, Plant Pathology and Entomology, Mississippi State University, 411 Dorman Hall, PO Box 9655, Mississippi State, MS, 39762, USA.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Methods Mol Biol</MedlineTA><NlmUniqueID>9214969</NlmUniqueID><ISSNLinking>1064-3745</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Big data</Keyword><Keyword MajorTopicYN="N">Databases</Keyword><Keyword MajorTopicYN="N">Genomics</Keyword><Keyword MajorTopicYN="N">Mass spectrometry</Keyword><Keyword MajorTopicYN="N">Next-generation sequencing</Keyword><Keyword MajorTopicYN="N">Proteomics</Keyword><Keyword MajorTopicYN="N">iPlant</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2016</Year><Month>4</Month><Day>27</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2016</Year><Month>4</Month><Day>27</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2016</Year><Month>4</Month><Day>27</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="doi">10.1007/978-1-4939-3572-7_27</ArticleId><ArticleId IdType="pubmed">27115651</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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