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Article: The current status and challenges in computational analysis of genomic big data

TitleThe current status and challenges in computational analysis of genomic big data
Authors
KeywordsGene regulatory networks
Genomic big data
Integrative data analysis
Next generation sequencing
OMICS
Issue Date2015
PublisherElsevier BV.
Citation
Big Data Research, 2015, v. 2 n. 1, p. 12-18 How to Cite?
AbstractDNA, RNA and protein are three major kinds of biological macromolecules with up to billions of basic elements in such biological organisms as human or mouse. They function at molecular, cellular and organismal levels individually and interactively. Traditional assays on such macromolecules are largely experimentally based, which are usually time consuming and laborious. In the past few years, high-throughput technologies, such as microarray and next-generation sequencing (NGS), were developed. Consequently, large genomic datasets are being generated and computational tools to analyzing these data are in urgent demand. This paper reviews several state-of-the-art high-throughput methodologies, representative projects, available databases and bioinformatics tools at different molecular levels. Finally, challenges and perspectives in processing genomic big data are discussed.
Persistent Identifierhttp://hdl.handle.net/10722/210595
ISSN
2015 SCImago Journal Rankings: 0.372

 

DC FieldValueLanguage
dc.contributor.authorQin, Y-
dc.contributor.authorYalamanchili, HK-
dc.contributor.authorQin, J-
dc.contributor.authorYan, B-
dc.contributor.authorWang, J-
dc.date.accessioned2015-06-19T03:23:36Z-
dc.date.available2015-06-19T03:23:36Z-
dc.date.issued2015-
dc.identifier.citationBig Data Research, 2015, v. 2 n. 1, p. 12-18-
dc.identifier.issn2214-5796-
dc.identifier.urihttp://hdl.handle.net/10722/210595-
dc.description.abstractDNA, RNA and protein are three major kinds of biological macromolecules with up to billions of basic elements in such biological organisms as human or mouse. They function at molecular, cellular and organismal levels individually and interactively. Traditional assays on such macromolecules are largely experimentally based, which are usually time consuming and laborious. In the past few years, high-throughput technologies, such as microarray and next-generation sequencing (NGS), were developed. Consequently, large genomic datasets are being generated and computational tools to analyzing these data are in urgent demand. This paper reviews several state-of-the-art high-throughput methodologies, representative projects, available databases and bioinformatics tools at different molecular levels. Finally, challenges and perspectives in processing genomic big data are discussed.-
dc.languageeng-
dc.publisherElsevier BV.-
dc.relation.ispartofBig Data Research-
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Big Data Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Big Data Research, [VOL 2, ISSUE 1, 2015] DOI 10.1016/j.bdr.2015.02.005-
dc.subjectGene regulatory networks-
dc.subjectGenomic big data-
dc.subjectIntegrative data analysis-
dc.subjectNext generation sequencing-
dc.subjectOMICS-
dc.titleThe current status and challenges in computational analysis of genomic big data-
dc.typeArticle-
dc.identifier.emailYan, B: yanbinai6017@gmail.com-
dc.identifier.authorityYan, B=rp01940-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.bdr.2015.02.005-
dc.identifier.scopuseid_2-s2.0-84925687964-
dc.identifier.hkuros244545-
dc.identifier.volume2-
dc.identifier.issue1-
dc.identifier.spage12-
dc.identifier.epage18-
dc.publisher.placeNetherlands-

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