Energy landscape analysis for regulatory RNA finding using scalable distributed cyberinfrastructure
Identifieur interne : 000181 ( Istex/Corpus ); précédent : 000180; suivant : 000182Energy landscape analysis for regulatory RNA finding using scalable distributed cyberinfrastructure
Auteurs : Joohyun Kim ; Wei Huang ; Sharath Maddineni ; Fareed Aboul-Ela ; Shantenu JhaSource :
- Concurrency and Computation: Practice and Experience [ 1532-0626 ] ; 2011-12-10.
Abstract
We investigate the folding energy landscape for a given RNA sequence through Boltzmann ensemble (BE) sampling of RNA secondary structures. The ensemble of sampled structures is used to derive distributions of energies and base‐pair distances between two configurations. We identify structural features that can be utilized for RNA gene finding. Characterization of the EL through BE sampling of secondary structures is computationally demanding and has multiple heterogeneous stages. We develop the Distributed Adaptive Runtime Environment to effectively address the computational requirements. Distributed Adaptive Runtime Environment is built upon an extensible and interoperable pilot‐job and supports the concurrent execution of a broad range of task sizes across a range of infrastructure. It is used to investigate two RNA systems of different sizes, S‐adenosyl methionine (SAM) binding RNA sequences known as SAM‐I riboswitches, and the S gene of the bovine corona virus RNA genome. We demonstrate how the implementation lowers the total time to solution for increases in RNA length, the number of sequences investigated, and the number of sampled structures. The distributions of energies and base‐pair distances reveal variations in folding dynamics and pathways among the SAM riboswitch sequences. Our results for BCoV RNA genome sequences also indicate sensitivity of folding to coding‐neutral variations in sequence. We search for a characteristic motif from within the SAM‐I consensus structure – a four‐way junction, among BE sampled structures for all 2910 SAM‐I sequences identified from Rfam (the curated ncRNA family database). We find that BE sampling provides insight into the variations in conformational distribution among sequences of the same ncRNA family. Therefore, BE sampling of secondary structures is a viable pre‐processing or post‐processing tool to complement comparative sequence analysis. The understanding gained shows how appropriately designed cyberinfrastructure can provide new insight into RNA folding and structure formation. Copyright © 2011 John Wiley & Sons, Ltd.
Url:
DOI: 10.1002/cpe.1796
Links to Exploration step
ISTEX:C06975D57FCA7300C0DE70650D16AFE0922E3B1CLe document en format XML
<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">Energy landscape analysis for regulatory RNA finding using scalable distributed cyberinfrastructure</title><author><name sortKey="Kim, Joohyun" sort="Kim, Joohyun" uniqKey="Kim J" first="Joohyun" last="Kim">Joohyun Kim</name><affiliation><mods:affiliation>Center for Computation & Technology, Louisiana State University, LA, 70803, Baton Rouge, USA</mods:affiliation></affiliation><affiliation><mods:affiliation>Joohyun Kim and Shantenu Jha, Center for Computation & Technology, Louisiana State University, Baton Rouge, LA 70803, USA.E‐mail: E‐mail:</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: jhkim@cct.lsu.edusjha@cct.lsu.edu</mods:affiliation></affiliation></author><author><name sortKey="Huang, Wei" sort="Huang, Wei" uniqKey="Huang W" first="Wei" last="Huang">Wei Huang</name><affiliation><mods:affiliation>Department of Biological Sciences, Louisiana State University, LA, 70803, Baton Rouge, USA</mods:affiliation></affiliation></author><author><name sortKey="Maddineni, Sharath" sort="Maddineni, Sharath" uniqKey="Maddineni S" first="Sharath" last="Maddineni">Sharath Maddineni</name><affiliation><mods:affiliation>Center for Computation & Technology, Louisiana State University, LA, 70803, Baton Rouge, USA</mods:affiliation></affiliation></author><author><name sortKey="Aboul La, Fareed" sort="Aboul La, Fareed" uniqKey="Aboul La F" first="Fareed" last="Aboul-Ela">Fareed Aboul-Ela</name><affiliation><mods:affiliation>Department of Biological Sciences, Louisiana State University, LA, 70803, Baton Rouge, USA</mods:affiliation></affiliation></author><author><name sortKey="Jha, Shantenu" sort="Jha, Shantenu" uniqKey="Jha S" first="Shantenu" last="Jha">Shantenu Jha</name><affiliation><mods:affiliation>Center for Computation & Technology, Louisiana State University, LA, 70803, Baton Rouge, USA</mods:affiliation></affiliation><affiliation><mods:affiliation>Joohyun Kim and Shantenu Jha, Center for Computation & Technology, Louisiana State University, Baton Rouge, LA 70803, USA.E‐mail: E‐mail:</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: jhkim@cct.lsu.edusjha@cct.lsu.edu</mods:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:C06975D57FCA7300C0DE70650D16AFE0922E3B1C</idno><date when="2011" year="2011">2011</date><idno type="doi">10.1002/cpe.1796</idno><idno type="url">https://api.istex.fr/document/C06975D57FCA7300C0DE70650D16AFE0922E3B1C/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">000181</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Energy landscape analysis for regulatory RNA finding using scalable distributed cyberinfrastructure</title><author><name sortKey="Kim, Joohyun" sort="Kim, Joohyun" uniqKey="Kim J" first="Joohyun" last="Kim">Joohyun Kim</name><affiliation><mods:affiliation>Center for Computation & Technology, Louisiana State University, LA, 70803, Baton Rouge, USA</mods:affiliation></affiliation><affiliation><mods:affiliation>Joohyun Kim and Shantenu Jha, Center for Computation & Technology, Louisiana State University, Baton Rouge, LA 70803, USA.E‐mail: E‐mail:</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: jhkim@cct.lsu.edusjha@cct.lsu.edu</mods:affiliation></affiliation></author><author><name sortKey="Huang, Wei" sort="Huang, Wei" uniqKey="Huang W" first="Wei" last="Huang">Wei Huang</name><affiliation><mods:affiliation>Department of Biological Sciences, Louisiana State University, LA, 70803, Baton Rouge, USA</mods:affiliation></affiliation></author><author><name sortKey="Maddineni, Sharath" sort="Maddineni, Sharath" uniqKey="Maddineni S" first="Sharath" last="Maddineni">Sharath Maddineni</name><affiliation><mods:affiliation>Center for Computation & Technology, Louisiana State University, LA, 70803, Baton Rouge, USA</mods:affiliation></affiliation></author><author><name sortKey="Aboul La, Fareed" sort="Aboul La, Fareed" uniqKey="Aboul La F" first="Fareed" last="Aboul-Ela">Fareed Aboul-Ela</name><affiliation><mods:affiliation>Department of Biological Sciences, Louisiana State University, LA, 70803, Baton Rouge, USA</mods:affiliation></affiliation></author><author><name sortKey="Jha, Shantenu" sort="Jha, Shantenu" uniqKey="Jha S" first="Shantenu" last="Jha">Shantenu Jha</name><affiliation><mods:affiliation>Center for Computation & Technology, Louisiana State University, LA, 70803, Baton Rouge, USA</mods:affiliation></affiliation><affiliation><mods:affiliation>Joohyun Kim and Shantenu Jha, Center for Computation & Technology, Louisiana State University, Baton Rouge, LA 70803, USA.E‐mail: E‐mail:</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: jhkim@cct.lsu.edusjha@cct.lsu.edu</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="j">Concurrency and Computation: Practice and Experience</title><title level="j" type="abbrev">Concurrency Computat.: Pract. Exper.</title><idno type="ISSN">1532-0626</idno><idno type="eISSN">1532-0634</idno><imprint><publisher>John Wiley & Sons, Ltd</publisher><pubPlace>Chichester, UK</pubPlace><date type="published" when="2011-12-10">2011-12-10</date><biblScope unit="volume">23</biblScope><biblScope unit="issue">17</biblScope><biblScope unit="page" from="2292">2292</biblScope><biblScope unit="page" to="2304">2304</biblScope></imprint><idno type="ISSN">1532-0626</idno></series><idno type="istex">C06975D57FCA7300C0DE70650D16AFE0922E3B1C</idno><idno type="DOI">10.1002/cpe.1796</idno><idno type="ArticleID">CPE1796</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">1532-0626</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract">We investigate the folding energy landscape for a given RNA sequence through Boltzmann ensemble (BE) sampling of RNA secondary structures. The ensemble of sampled structures is used to derive distributions of energies and base‐pair distances between two configurations. We identify structural features that can be utilized for RNA gene finding. Characterization of the EL through BE sampling of secondary structures is computationally demanding and has multiple heterogeneous stages. We develop the Distributed Adaptive Runtime Environment to effectively address the computational requirements. Distributed Adaptive Runtime Environment is built upon an extensible and interoperable pilot‐job and supports the concurrent execution of a broad range of task sizes across a range of infrastructure. It is used to investigate two RNA systems of different sizes, S‐adenosyl methionine (SAM) binding RNA sequences known as SAM‐I riboswitches, and the S gene of the bovine corona virus RNA genome. We demonstrate how the implementation lowers the total time to solution for increases in RNA length, the number of sequences investigated, and the number of sampled structures. The distributions of energies and base‐pair distances reveal variations in folding dynamics and pathways among the SAM riboswitch sequences. Our results for BCoV RNA genome sequences also indicate sensitivity of folding to coding‐neutral variations in sequence. We search for a characteristic motif from within the SAM‐I consensus structure – a four‐way junction, among BE sampled structures for all 2910 SAM‐I sequences identified from Rfam (the curated ncRNA family database). We find that BE sampling provides insight into the variations in conformational distribution among sequences of the same ncRNA family. Therefore, BE sampling of secondary structures is a viable pre‐processing or post‐processing tool to complement comparative sequence analysis. The understanding gained shows how appropriately designed cyberinfrastructure can provide new insight into RNA folding and structure formation. Copyright © 2011 John Wiley & Sons, Ltd.</div></front></TEI><istex><corpusName>wiley</corpusName><author><json:item><name>Joohyun Kim</name><affiliations><json:string>Center for Computation & Technology, Louisiana State University, LA, 70803, Baton Rouge, USA</json:string><json:string>Joohyun Kim and Shantenu Jha, Center for Computation & Technology, Louisiana State University, Baton Rouge, LA 70803, USA.E‐mail: E‐mail:</json:string><json:string>E-mail: jhkim@cct.lsu.edusjha@cct.lsu.edu</json:string></affiliations></json:item><json:item><name>Wei Huang</name><affiliations><json:string>Department of Biological Sciences, Louisiana State University, LA, 70803, Baton Rouge, USA</json:string></affiliations></json:item><json:item><name>Sharath Maddineni</name><affiliations><json:string>Center for Computation & Technology, Louisiana State University, LA, 70803, Baton Rouge, USA</json:string></affiliations></json:item><json:item><name>Fareed Aboul‐ela</name><affiliations><json:string>Department of Biological Sciences, Louisiana State University, LA, 70803, Baton Rouge, USA</json:string></affiliations></json:item><json:item><name>Shantenu Jha</name><affiliations><json:string>Center for Computation & Technology, Louisiana State University, LA, 70803, Baton Rouge, USA</json:string><json:string>Joohyun Kim and Shantenu Jha, Center for Computation & Technology, Louisiana State University, Baton Rouge, LA 70803, USA.E‐mail: E‐mail:</json:string><json:string>E-mail: jhkim@cct.lsu.edusjha@cct.lsu.edu</json:string></affiliations></json:item></author><subject><json:item><lang><json:string>eng</json:string></lang><value>concurrent programming and distributed computing</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Simple API for Grid Applications (SAGA)</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Pilot‐Job abstraction</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>RNA folding energy landscape</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>ncRNA gene finding</value></json:item></subject><articleId><json:string>CPE1796</json:string></articleId><language><json:string>eng</json:string></language><originalGenre><json:string>article</json:string></originalGenre><abstract>We investigate the folding energy landscape for a given RNA sequence through Boltzmann ensemble (BE) sampling of RNA secondary structures. The ensemble of sampled structures is used to derive distributions of energies and base‐pair distances between two configurations. We identify structural features that can be utilized for RNA gene finding. Characterization of the EL through BE sampling of secondary structures is computationally demanding and has multiple heterogeneous stages. We develop the Distributed Adaptive Runtime Environment to effectively address the computational requirements. Distributed Adaptive Runtime Environment is built upon an extensible and interoperable pilot‐job and supports the concurrent execution of a broad range of task sizes across a range of infrastructure. It is used to investigate two RNA systems of different sizes, S‐adenosyl methionine (SAM) binding RNA sequences known as SAM‐I riboswitches, and the S gene of the bovine corona virus RNA genome. We demonstrate how the implementation lowers the total time to solution for increases in RNA length, the number of sequences investigated, and the number of sampled structures. The distributions of energies and base‐pair distances reveal variations in folding dynamics and pathways among the SAM riboswitch sequences. Our results for BCoV RNA genome sequences also indicate sensitivity of folding to coding‐neutral variations in sequence. We search for a characteristic motif from within the SAM‐I consensus structure – a four‐way junction, among BE sampled structures for all 2910 SAM‐I sequences identified from Rfam (the curated ncRNA family database). We find that BE sampling provides insight into the variations in conformational distribution among sequences of the same ncRNA family. Therefore, BE sampling of secondary structures is a viable pre‐processing or post‐processing tool to complement comparative sequence analysis. The understanding gained shows how appropriately designed cyberinfrastructure can provide new insight into RNA folding and structure formation. Copyright © 2011 John Wiley & Sons, Ltd.</abstract><qualityIndicators><score>8</score><pdfVersion>1.3</pdfVersion><pdfPageSize>595.276 x 782.362 pts</pdfPageSize><refBibsNative>true</refBibsNative><keywordCount>5</keywordCount><abstractCharCount>2108</abstractCharCount><pdfWordCount>6751</pdfWordCount><pdfCharCount>41679</pdfCharCount><pdfPageCount>13</pdfPageCount><abstractWordCount>302</abstractWordCount></qualityIndicators><title>Energy landscape analysis for regulatory RNA finding using scalable distributed cyberinfrastructure</title><genre><json:string>article</json:string></genre><host><volume>23</volume><publisherId><json:string>CPE</json:string></publisherId><pages><total>13</total><last>2304</last><first>2292</first></pages><issn><json:string>1532-0626</json:string></issn><issue>17</issue><subject><json:item><value>Special Issue Paper</value></json:item></subject><genre><json:string>journal</json:string></genre><language><json:string>unknown</json:string></language><eissn><json:string>1532-0634</json:string></eissn><title>Concurrency and Computation: Practice and Experience</title><doi><json:string>10.1002/(ISSN)1532-0634</json:string></doi></host><publicationDate>2011</publicationDate><copyrightDate>2011</copyrightDate><doi><json:string>10.1002/cpe.1796</json:string></doi><id>C06975D57FCA7300C0DE70650D16AFE0922E3B1C</id><score>0.27877396</score><fulltext><json:item><original>true</original><mimetype>application/pdf</mimetype><extension>pdf</extension><uri>https://api.istex.fr/document/C06975D57FCA7300C0DE70650D16AFE0922E3B1C/fulltext/pdf</uri></json:item><json:item><original>false</original><mimetype>application/zip</mimetype><extension>zip</extension><uri>https://api.istex.fr/document/C06975D57FCA7300C0DE70650D16AFE0922E3B1C/fulltext/zip</uri></json:item><istex:fulltextTEI uri="https://api.istex.fr/document/C06975D57FCA7300C0DE70650D16AFE0922E3B1C/fulltext/tei"><teiHeader><fileDesc><titleStmt><title level="a" type="main" xml:lang="en">Energy landscape analysis for regulatory RNA finding using scalable distributed cyberinfrastructure</title></titleStmt><publicationStmt><authority>ISTEX</authority><publisher>John Wiley & Sons, Ltd</publisher><pubPlace>Chichester, UK</pubPlace><availability><p>Copyright © 2011 John Wiley & Sons, Ltd.Copyright © 2011 John Wiley & Sons, Ltd.</p></availability><date>2011-08</date></publicationStmt><sourceDesc><biblStruct type="inbook"><analytic><title level="a" type="main" xml:lang="en">Energy landscape analysis for regulatory RNA finding using scalable distributed cyberinfrastructure</title><author xml:id="author-1"><persName><forename type="first">Joohyun</forename><surname>Kim</surname></persName><email>jhkim@cct.lsu.edusjha@cct.lsu.edu</email><affiliation>Center for Computation & Technology, Louisiana State University, LA, 70803, Baton Rouge, USA</affiliation><affiliation>Joohyun Kim and Shantenu Jha, Center for Computation & Technology, Louisiana State University, Baton Rouge, LA 70803, USA.E‐mail: E‐mail:</affiliation></author><author xml:id="author-2"><persName><forename type="first">Wei</forename><surname>Huang</surname></persName><affiliation>Department of Biological Sciences, Louisiana State University, LA, 70803, Baton Rouge, USA</affiliation></author><author xml:id="author-3"><persName><forename type="first">Sharath</forename><surname>Maddineni</surname></persName><affiliation>Center for Computation & Technology, Louisiana State University, LA, 70803, Baton Rouge, USA</affiliation></author><author xml:id="author-4"><persName><forename type="first">Fareed</forename><surname>Aboul‐ela</surname></persName><affiliation>Department of Biological Sciences, Louisiana State University, LA, 70803, Baton Rouge, USA</affiliation></author><author xml:id="author-5"><persName><forename type="first">Shantenu</forename><surname>Jha</surname></persName><email>jhkim@cct.lsu.edusjha@cct.lsu.edu</email><affiliation>Center for Computation & Technology, Louisiana State University, LA, 70803, Baton Rouge, USA</affiliation><affiliation>Joohyun Kim and Shantenu Jha, Center for Computation & Technology, Louisiana State University, Baton Rouge, LA 70803, USA.E‐mail: E‐mail:</affiliation></author></analytic><monogr><title level="j">Concurrency and Computation: Practice and Experience</title><title level="j" type="abbrev">Concurrency Computat.: Pract. Exper.</title><idno type="pISSN">1532-0626</idno><idno type="eISSN">1532-0634</idno><idno type="DOI">10.1002/(ISSN)1532-0634</idno><imprint><publisher>John Wiley & Sons, Ltd</publisher><pubPlace>Chichester, UK</pubPlace><date type="published" when="2011-12-10"></date><biblScope unit="volume">23</biblScope><biblScope unit="issue">17</biblScope><biblScope unit="page" from="2292">2292</biblScope><biblScope unit="page" to="2304">2304</biblScope></imprint></monogr><idno type="istex">C06975D57FCA7300C0DE70650D16AFE0922E3B1C</idno><idno type="DOI">10.1002/cpe.1796</idno><idno type="ArticleID">CPE1796</idno></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>2011-08</date></creation><langUsage><language ident="en">en</language></langUsage><abstract><p>We investigate the folding energy landscape for a given RNA sequence through Boltzmann ensemble (BE) sampling of RNA secondary structures. The ensemble of sampled structures is used to derive distributions of energies and base‐pair distances between two configurations. We identify structural features that can be utilized for RNA gene finding. Characterization of the EL through BE sampling of secondary structures is computationally demanding and has multiple heterogeneous stages. We develop the Distributed Adaptive Runtime Environment to effectively address the computational requirements. Distributed Adaptive Runtime Environment is built upon an extensible and interoperable pilot‐job and supports the concurrent execution of a broad range of task sizes across a range of infrastructure. It is used to investigate two RNA systems of different sizes, S‐adenosyl methionine (SAM) binding RNA sequences known as SAM‐I riboswitches, and the S gene of the bovine corona virus RNA genome. We demonstrate how the implementation lowers the total time to solution for increases in RNA length, the number of sequences investigated, and the number of sampled structures. The distributions of energies and base‐pair distances reveal variations in folding dynamics and pathways among the SAM riboswitch sequences. Our results for BCoV RNA genome sequences also indicate sensitivity of folding to coding‐neutral variations in sequence. We search for a characteristic motif from within the SAM‐I consensus structure – a four‐way junction, among BE sampled structures for all 2910 SAM‐I sequences identified from Rfam (the curated ncRNA family database). We find that BE sampling provides insight into the variations in conformational distribution among sequences of the same ncRNA family. Therefore, BE sampling of secondary structures is a viable pre‐processing or post‐processing tool to complement comparative sequence analysis. The understanding gained shows how appropriately designed cyberinfrastructure can provide new insight into RNA folding and structure formation. Copyright © 2011 John Wiley & Sons, Ltd.</p></abstract><textClass><keywords scheme="keyword"><list><head>keywords</head><item><term>concurrent programming and distributed computing</term></item><item><term>Simple API for Grid Applications (SAGA)</term></item><item><term>Pilot‐Job abstraction</term></item><item><term>RNA folding energy landscape</term></item><item><term>ncRNA gene finding</term></item></list></keywords></textClass><textClass><keywords scheme="Journal Subject"><list><head>article-category</head><item><term>Special Issue Paper</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="2010-12-15">Received</change><change when="2011-05-14">Registration</change><change when="2011-08">Created</change><change when="2011-12-10">Published</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><original>false</original><mimetype>text/plain</mimetype><extension>txt</extension><uri>https://api.istex.fr/document/C06975D57FCA7300C0DE70650D16AFE0922E3B1C/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Wiley, elements deleted: body"><istex:xmlDeclaration>version="1.0" encoding="UTF-8" standalone="yes"</istex:xmlDeclaration><istex:document><component type="serialArticle" version="2.0" xml:lang="en" xml:id="cpe1796"><header><publicationMeta level="product"><publisherInfo><publisherName>John Wiley & Sons, Ltd</publisherName><publisherLoc>Chichester, UK</publisherLoc></publisherInfo><doi>10.1002/(ISSN)1532-0634</doi><issn type="print">1532-0626</issn><issn type="electronic">1532-0634</issn><idGroup><id type="product" value="CPE"></id></idGroup><titleGroup><title type="main" sort="CONCURRENCY AND COMPUTATION: PRACTICE AND EXPERIENCE">Concurrency and Computation: Practice and Experience</title><title type="short">Concurrency Computat.: Pract. Exper.</title></titleGroup></publicationMeta><publicationMeta level="part" position="170"><doi>10.1002/cpe.v23.17</doi><copyright ownership="publisher">Copyright © 2011 John Wiley & Sons, Ltd.</copyright><numberingGroup><numbering type="journalVolume" number="23">23</numbering><numbering type="journalIssue">17</numbering></numberingGroup><coverDate startDate="2011-12-10">10 December 2011</coverDate></publicationMeta><publicationMeta level="unit" position="220" type="article" status="forIssue"><doi>10.1002/cpe.1796</doi><idGroup><id type="unit" value="CPE1796"></id></idGroup><countGroup><count type="pageTotal" number="13"></count></countGroup><titleGroup><title type="articleCategory">Special Issue Paper</title><title type="tocHeading1">Special Issue Papers</title></titleGroup><copyright ownership="publisher">Copyright © 2011 John Wiley & Sons, Ltd.</copyright><eventGroup><event type="manuscriptReceived" date="2010-12-15"></event><event type="manuscriptRevised" date="2011-05-13"></event><event type="manuscriptAccepted" date="2011-05-14"></event><event type="xmlCreated" agent="Spi global" date="2011-08"></event><event type="publishedOnlineEarlyUnpaginated" date="2011-09-21"></event><event type="publishedOnlineFinalForm" date="2011-10-20"></event><event type="firstOnline" date="2011-09-21"></event><event type="xmlConverted" agent="Converter:WILEY_ML3G_TO_WILEY_ML3GV2 version:3.8.8" date="2014-01-16"></event><event type="xmlConverted" agent="Converter:WML3G_To_WML3G version:4.1.7 mode:FullText,remove_FC" date="2014-10-16"></event></eventGroup><numberingGroup><numbering type="pageFirst">2292</numbering><numbering type="pageLast">2304</numbering></numberingGroup><correspondenceTo><lineatedText xml:id="cpe1796-lntext-0001"><line>Joohyun Kim and Shantenu Jha, Center for Computation & Technology, Louisiana State University, Baton Rouge, LA 70803, USA.</line><line>E‐mail: <email>jhkim@cct.lsu.edu</email></line><line>E‐mail: <email>sjha@cct.lsu.edu</email></line></lineatedText></correspondenceTo><linkGroup><link type="toTypesetVersion" href="file:CPE.CPE1796.pdf"></link></linkGroup></publicationMeta><contentMeta><titleGroup><title type="main">Energy landscape analysis for regulatory RNA finding using scalable distributed cyberinfrastructure</title><title type="short">ENERGY LANDSCAPE ANALYSIS FOR REGULATORY RNA FINDING AND FOLDING PREDICTION</title><title type="shortAuthors">J. KIM <i>ET AL.</i></title></titleGroup><creators><creator xml:id="cpe1796-cr-0002" creatorRole="author" affiliationRef="#cpe1796-aff-0001" corresponding="yes"><personName><givenNames>Joohyun</givenNames><familyName>Kim</familyName></personName><contactDetails><email>jhkim@cct.lsu.edu</email></contactDetails></creator><creator xml:id="cpe1796-cr-0003" creatorRole="author" affiliationRef="#cpe1796-aff-0002"><personName><givenNames>Wei</givenNames><familyName>Huang</familyName></personName></creator><creator xml:id="cpe1796-cr-0004" creatorRole="author" affiliationRef="#cpe1796-aff-0001"><personName><givenNames>Sharath</givenNames><familyName>Maddineni</familyName></personName></creator><creator xml:id="cpe1796-cr-0005" creatorRole="author" affiliationRef="#cpe1796-aff-0002"><personName><givenNames>Fareed</givenNames><familyName>Aboul‐ela</familyName></personName></creator><creator xml:id="cpe1796-cr-0006" creatorRole="author" affiliationRef="#cpe1796-aff-0001" corresponding="yes"><personName><givenNames>Shantenu</givenNames><familyName>Jha</familyName></personName><contactDetails><email>sjha@cct.lsu.edu</email></contactDetails></creator></creators><affiliationGroup><affiliation xml:id="cpe1796-aff-0001" countryCode="US" type="organization"><orgDiv>Center for Computation & Technology</orgDiv><orgName>Louisiana State University</orgName><address><city>Baton Rouge</city><countryPart>LA</countryPart><postCode>70803</postCode><country>USA</country></address></affiliation><affiliation xml:id="cpe1796-aff-0002" countryCode="US" type="organization"><orgDiv>Department of Biological Sciences</orgDiv><orgName>Louisiana State University</orgName><address><city>Baton Rouge</city><countryPart>LA</countryPart><postCode>70803</postCode><country>USA</country></address></affiliation></affiliationGroup><keywordGroup type="author"><keyword xml:id="cpe1796-kwd-0001">concurrent programming and distributed computing</keyword><keyword xml:id="cpe1796-kwd-0002">Simple API for Grid Applications (SAGA)</keyword><keyword xml:id="cpe1796-kwd-0003">Pilot‐Job abstraction</keyword><keyword xml:id="cpe1796-kwd-0004">RNA folding energy landscape</keyword><keyword xml:id="cpe1796-kwd-0005">ncRNA gene finding</keyword></keywordGroup><abstractGroup><abstract type="main" xml:id="cpe1796-abs-0001"><title type="main">SUMMARY</title><p xml:id="cpe1796-para-0002">We investigate the folding energy landscape for a given RNA sequence through Boltzmann ensemble (BE) sampling of RNA secondary structures. The ensemble of sampled structures is used to derive distributions of energies and base‐pair distances between two configurations. We identify structural features that can be utilized for RNA gene finding. Characterization of the EL through BE sampling of secondary structures is computationally demanding and has multiple heterogeneous stages. We develop the Distributed Adaptive Runtime Environment to effectively address the computational requirements. Distributed Adaptive Runtime Environment is built upon an extensible and interoperable pilot‐job and supports the concurrent execution of a broad range of task sizes across a range of infrastructure. It is used to investigate two RNA systems of different sizes, S‐adenosyl methionine (SAM) binding RNA sequences known as SAM‐I riboswitches, and the S gene of the bovine corona virus RNA genome. We demonstrate how the implementation lowers the total time to solution for increases in RNA length, the number of sequences investigated, and the number of sampled structures. The distributions of energies and base‐pair distances reveal variations in folding dynamics and pathways among the SAM riboswitch sequences. Our results for BCoV RNA genome sequences also indicate sensitivity of folding to coding‐neutral variations in sequence. We search for a characteristic motif from within the SAM‐I consensus structure – a four‐way junction, among BE sampled structures for all 2910 SAM‐I sequences identified from Rfam (the curated ncRNA family database). We find that BE sampling provides insight into the variations in conformational distribution among sequences of the same ncRNA family. Therefore, BE sampling of secondary structures is a viable pre‐processing or post‐processing tool to complement comparative sequence analysis. The understanding gained shows how appropriately designed cyberinfrastructure can provide new insight into RNA folding and structure formation. Copyright © 2011 John Wiley & Sons, Ltd.</p></abstract></abstractGroup></contentMeta></header></component></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Energy landscape analysis for regulatory RNA finding using scalable distributed cyberinfrastructure</title></titleInfo><titleInfo type="abbreviated" lang="en"><title>ENERGY LANDSCAPE ANALYSIS FOR REGULATORY RNA FINDING AND FOLDING PREDICTION</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Energy landscape analysis for regulatory RNA finding using scalable distributed cyberinfrastructure</title></titleInfo><name type="personal"><namePart type="given">Joohyun</namePart><namePart type="family">Kim</namePart><affiliation>Center for Computation & Technology, Louisiana State University, LA, 70803, Baton Rouge, USA</affiliation><affiliation>Joohyun Kim and Shantenu Jha, Center for Computation & Technology, Louisiana State University, Baton Rouge, LA 70803, USA.E‐mail: E‐mail:</affiliation><affiliation>E-mail: jhkim@cct.lsu.edusjha@cct.lsu.edu</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Wei</namePart><namePart type="family">Huang</namePart><affiliation>Department of Biological Sciences, Louisiana State University, LA, 70803, Baton Rouge, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Sharath</namePart><namePart type="family">Maddineni</namePart><affiliation>Center for Computation & Technology, Louisiana State University, LA, 70803, Baton Rouge, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Fareed</namePart><namePart type="family">Aboul‐ela</namePart><affiliation>Department of Biological Sciences, Louisiana State University, LA, 70803, Baton Rouge, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Shantenu</namePart><namePart type="family">Jha</namePart><affiliation>Center for Computation & Technology, Louisiana State University, LA, 70803, Baton Rouge, USA</affiliation><affiliation>Joohyun Kim and Shantenu Jha, Center for Computation & Technology, Louisiana State University, Baton Rouge, LA 70803, USA.E‐mail: E‐mail:</affiliation><affiliation>E-mail: jhkim@cct.lsu.edusjha@cct.lsu.edu</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="article" displayLabel="article"></genre><originInfo><publisher>John Wiley & Sons, Ltd</publisher><place><placeTerm type="text">Chichester, UK</placeTerm></place><dateIssued encoding="w3cdtf">2011-12-10</dateIssued><dateCreated encoding="w3cdtf">2011-08</dateCreated><dateCaptured encoding="w3cdtf">2010-12-15</dateCaptured><dateValid encoding="w3cdtf">2011-05-14</dateValid><copyrightDate encoding="w3cdtf">2011</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract>We investigate the folding energy landscape for a given RNA sequence through Boltzmann ensemble (BE) sampling of RNA secondary structures. The ensemble of sampled structures is used to derive distributions of energies and base‐pair distances between two configurations. We identify structural features that can be utilized for RNA gene finding. Characterization of the EL through BE sampling of secondary structures is computationally demanding and has multiple heterogeneous stages. We develop the Distributed Adaptive Runtime Environment to effectively address the computational requirements. Distributed Adaptive Runtime Environment is built upon an extensible and interoperable pilot‐job and supports the concurrent execution of a broad range of task sizes across a range of infrastructure. It is used to investigate two RNA systems of different sizes, S‐adenosyl methionine (SAM) binding RNA sequences known as SAM‐I riboswitches, and the S gene of the bovine corona virus RNA genome. We demonstrate how the implementation lowers the total time to solution for increases in RNA length, the number of sequences investigated, and the number of sampled structures. The distributions of energies and base‐pair distances reveal variations in folding dynamics and pathways among the SAM riboswitch sequences. Our results for BCoV RNA genome sequences also indicate sensitivity of folding to coding‐neutral variations in sequence. We search for a characteristic motif from within the SAM‐I consensus structure – a four‐way junction, among BE sampled structures for all 2910 SAM‐I sequences identified from Rfam (the curated ncRNA family database). We find that BE sampling provides insight into the variations in conformational distribution among sequences of the same ncRNA family. Therefore, BE sampling of secondary structures is a viable pre‐processing or post‐processing tool to complement comparative sequence analysis. The understanding gained shows how appropriately designed cyberinfrastructure can provide new insight into RNA folding and structure formation. Copyright © 2011 John Wiley & Sons, Ltd.</abstract><subject><genre>keywords</genre><topic>concurrent programming and distributed computing</topic><topic>Simple API for Grid Applications (SAGA)</topic><topic>Pilot‐Job abstraction</topic><topic>RNA folding energy landscape</topic><topic>ncRNA gene finding</topic></subject><relatedItem type="host"><titleInfo><title>Concurrency and Computation: Practice and Experience</title></titleInfo><titleInfo type="abbreviated"><title>Concurrency Computat.: Pract. Exper.</title></titleInfo><genre type="journal">journal</genre><subject><genre>article-category</genre><topic>Special Issue Paper</topic></subject><identifier type="ISSN">1532-0626</identifier><identifier type="eISSN">1532-0634</identifier><identifier type="DOI">10.1002/(ISSN)1532-0634</identifier><identifier type="PublisherID">CPE</identifier><part><date>2011</date><detail type="volume"><caption>vol.</caption><number>23</number></detail><detail type="issue"><caption>no.</caption><number>17</number></detail><extent unit="pages"><start>2292</start><end>2304</end><total>13</total></extent></part></relatedItem><identifier type="istex">C06975D57FCA7300C0DE70650D16AFE0922E3B1C</identifier><identifier type="DOI">10.1002/cpe.1796</identifier><identifier type="ArticleID">CPE1796</identifier><accessCondition type="use and reproduction" contentType="copyright">Copyright © 2011 John Wiley & Sons, Ltd.Copyright © 2011 John Wiley & Sons, Ltd.</accessCondition><recordInfo><recordContentSource>WILEY</recordContentSource><recordOrigin>John Wiley & Sons, Ltd</recordOrigin></recordInfo></mods></metadata><enrichments><json:item><type>multicat</type><uri>https://api.istex.fr/document/C06975D57FCA7300C0DE70650D16AFE0922E3B1C/enrichments/multicat</uri></json:item></enrichments><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Ticri/CIDE/explor/CyberinfraV1/Data/Istex/Corpus
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000181 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Istex/Corpus/biblio.hfd -nk 000181 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Ticri/CIDE
|area= CyberinfraV1
|flux= Istex
|étape= Corpus
|type= RBID
|clé= ISTEX:C06975D57FCA7300C0DE70650D16AFE0922E3B1C
|texte= Energy landscape analysis for regulatory RNA finding using scalable distributed cyberinfrastructure
}}
| This area was generated with Dilib version V0.6.25. |  |