Noncoding RNAs and their annotation using metagenomics algorithms
Identifieur interne : 000B72 ( Istex/Corpus ); précédent : 000B71; suivant : 000B73Noncoding RNAs and their annotation using metagenomics algorithms
Auteurs : Shubhra Sankar Ray ; Sonam MaitiSource :
- Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery [ 1942-4787 ] ; 2015-01.
Abstract
This article provides an overview of noncoding RNAs (ncRNA) involving their structure, function, computational methods for structure prediction and the algorithms for analyzing ncRNAs from metagenome samples. Different techniques for ncRNA structure prediction such as dynamic programming (DP), genetic algorithm (GA), artificial neural network (ANN) and stochastic context‐free grammar (SCFG) are discussed. The basic concepts of metagenomics along with their biological basis are mentioned and the relevance of ncRNAs in metagenomics is also explored. Similarity and composition based computational methods for analyzing noncoding sequences in metagenomes are then mentioned along with their biological findings. An extensive bibliography is included. WIREs Data Mining Knowl Discov 2015, 5:1–20. doi: 10.1002/widm.1142 For further resources related to this article, please visit the WIREs website. Conflict of interest: The authors have declared no conflicts of interest for this article.
Url:
DOI: 10.1002/widm.1142
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<familyName>Maiti</familyName></personName></creator></creators><affiliationGroup xml:id="widm1142-affgp-0001"><affiliation countryCode="IN" type="organization" xml:id="widm1142-aff-0001"><orgDiv>Machine Intelligence Unit</orgDiv><orgName>Indian Statistical Institute</orgName><address><city>Kolkata</city><countryPart>West Bengal</countryPart><country>India</country></address></affiliation><affiliation countryCode="IN" type="organization" xml:id="widm1142-aff-0002"><orgDiv>Center for Soft Computing Research: A National Facility</orgDiv><orgName>Indian Statistical Institute</orgName><address><city>Kolkata</city><countryPart>West Bengal</countryPart><country>India</country></address></affiliation></affiliationGroup><abstractGroup><abstract type="main" xml:id="widm1142-abs-0001"><p xml:id="widm1142-para-0001">This article provides an overview of noncoding <fc>RNAs</fc> (<fc>ncRNA</fc>) involving their structure, function, computational methods for structure prediction and the algorithms for analyzing <fc>ncRNAs</fc> from metagenome samples. Different techniques for ncRNA structure prediction such as dynamic programming (<fc>DP</fc>), genetic algorithm (<fc>GA</fc>), artificial neural network (<fc>ANN</fc>) and stochastic context‐free grammar (<fc>SCFG</fc>) are discussed. The basic concepts of metagenomics along with their biological basis are mentioned and the relevance of <fc>ncRNAs</fc> in metagenomics is also explored. Similarity and composition based computational methods for analyzing noncoding sequences in metagenomes are then mentioned along with their biological findings. An extensive bibliography is included. <i>WIREs Data Mining Knowl Discov</i> 2015, 5:1–20. doi: 10.1002/widm.1142</p><p>For further resources related to this article, please visit the <url href="http://wires.wiley.com/remdoi.cgi?doi=10.1002/widm.1142">WIREs website</url>.</p><p>Conflict of interest: The authors have declared no conflicts of interest for this article.</p></abstract></abstractGroup></contentMeta></header></component></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Noncoding RNAs and their annotation using metagenomics algorithms</title></titleInfo><titleInfo type="abbreviated" lang="en"><title>Noncoding RNAs and their annotation</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Noncoding RNAs and their annotation using metagenomics algorithms</title></titleInfo><name type="personal"><namePart type="given">Shubhra Sankar</namePart><namePart type="family">Ray</namePart><affiliation>Machine Intelligence Unit, Indian Statistical Institute, Kolkata, West Bengal, India</affiliation><affiliation>Center for Soft Computing Research: A National Facility, Indian Statistical Institute, West Bengal, Kolkata, India</affiliation><affiliation>E-mail: shubhra@isical.ac.in</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Sonam</namePart><namePart type="family">Maiti</namePart><affiliation>Machine Intelligence Unit, Indian Statistical Institute, West Bengal, Kolkata, India</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="review-article" displayLabel="reviewArticle" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-L5L7X3NF-P">review-article</genre><originInfo><publisher>Wiley Periodicals, Inc</publisher><place><placeTerm type="text">Hoboken, USA</placeTerm></place><dateIssued encoding="w3cdtf">2015-01</dateIssued><dateCreated encoding="w3cdtf">2015-01-13</dateCreated><dateCaptured encoding="w3cdtf">2013-09-12</dateCaptured><dateValid encoding="w3cdtf">2015-01-05</dateValid><copyrightDate encoding="w3cdtf">2015</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><abstract>This article provides an overview of noncoding RNAs (ncRNA) involving their structure, function, computational methods for structure prediction and the algorithms for analyzing ncRNAs from metagenome samples. Different techniques for ncRNA structure prediction such as dynamic programming (DP), genetic algorithm (GA), artificial neural network (ANN) and stochastic context‐free grammar (SCFG) are discussed. The basic concepts of metagenomics along with their biological basis are mentioned and the relevance of ncRNAs in metagenomics is also explored. Similarity and composition based computational methods for analyzing noncoding sequences in metagenomes are then mentioned along with their biological findings. An extensive bibliography is included. WIREs Data Mining Knowl Discov 2015, 5:1–20. doi: 10.1002/widm.1142 For further resources related to this article, please visit the WIREs website. 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