Developing eThread pipeline using SAGA-pilot abstraction for large-scale structural bioinformatics.
Identifieur interne : 000019 ( PubMed/Curation ); précédent : 000018; suivant : 000020Developing eThread pipeline using SAGA-pilot abstraction for large-scale structural bioinformatics.
Auteurs : Anjani Ragothaman [États-Unis] ; Sairam Chowdary Boddu [États-Unis] ; Nayong Kim [États-Unis] ; Wei Feinstein [États-Unis] ; Michal Brylinski [États-Unis] ; Shantenu Jha [États-Unis] ; Joohyun Kim [États-Unis]Source :
- BioMed research international [ 2314-6141 ] ; 2014.
English descriptors
- KwdEn :
- MESH :
- chemical , chemistry : Proteins.
- methods : Computational Biology.
- Algorithms, Amino Acid Sequence, Genome, Internet, Molecular Sequence Annotation, Protein Conformation, Software, User-Computer Interface.
Abstract
While most of computational annotation approaches are sequence-based, threading methods are becoming increasingly attractive because of predicted structural information that could uncover the underlying function. However, threading tools are generally compute-intensive and the number of protein sequences from even small genomes such as prokaryotes is large typically containing many thousands, prohibiting their application as a genome-wide structural systems biology tool. To leverage its utility, we have developed a pipeline for eThread--a meta-threading protein structure modeling tool, that can use computational resources efficiently and effectively. We employ a pilot-based approach that supports seamless data and task-level parallelism and manages large variation in workload and computational requirements. Our scalable pipeline is deployed on Amazon EC2 and can efficiently select resources based upon task requirements. We present runtime analysis to characterize computational complexity of eThread and EC2 infrastructure. Based on results, we suggest a pathway to an optimized solution with respect to metrics such as time-to-solution or cost-to-solution. Our eThread pipeline can scale to support a large number of sequences and is expected to be a viable solution for genome-scale structural bioinformatics and structure-based annotation, particularly, amenable for small genomes such as prokaryotes. The developed pipeline is easily extensible to other types of distributed cyberinfrastructure.
DOI: 10.1155/2014/348725
PubMed: 24995285
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Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803, USA ; Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803</wicri:regionArea></affiliation></author><author><name sortKey="Jha, Shantenu" sort="Jha, Shantenu" uniqKey="Jha S" first="Shantenu" last="Jha">Shantenu Jha</name><affiliation wicri:level="1"><nlm:affiliation>RADICAL, ECE, Rutgers University, New Brunswick, NJ 08901, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>RADICAL, ECE, Rutgers University, New Brunswick, NJ 08901</wicri:regionArea></affiliation></author><author><name sortKey="Kim, Joohyun" sort="Kim, Joohyun" uniqKey="Kim J" first="Joohyun" last="Kim">Joohyun Kim</name><affiliation wicri:level="1"><nlm:affiliation>Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803</wicri:regionArea></affiliation></author></analytic><series><title level="j">BioMed research international</title><idno type="eISSN">2314-6141</idno><imprint><date when="2014" type="published">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Amino Acid Sequence</term><term>Computational Biology (methods)</term><term>Genome</term><term>Internet</term><term>Molecular Sequence Annotation</term><term>Protein Conformation</term><term>Proteins (chemistry)</term><term>Software</term><term>User-Computer Interface</term></keywords><keywords scheme="MESH" type="chemical" qualifier="chemistry" xml:lang="en"><term>Proteins</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Computational Biology</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Amino Acid Sequence</term><term>Genome</term><term>Internet</term><term>Molecular Sequence Annotation</term><term>Protein Conformation</term><term>Software</term><term>User-Computer Interface</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">While most of computational annotation approaches are sequence-based, threading methods are becoming increasingly attractive because of predicted structural information that could uncover the underlying function. However, threading tools are generally compute-intensive and the number of protein sequences from even small genomes such as prokaryotes is large typically containing many thousands, prohibiting their application as a genome-wide structural systems biology tool. To leverage its utility, we have developed a pipeline for eThread--a meta-threading protein structure modeling tool, that can use computational resources efficiently and effectively. We employ a pilot-based approach that supports seamless data and task-level parallelism and manages large variation in workload and computational requirements. Our scalable pipeline is deployed on Amazon EC2 and can efficiently select resources based upon task requirements. We present runtime analysis to characterize computational complexity of eThread and EC2 infrastructure. Based on results, we suggest a pathway to an optimized solution with respect to metrics such as time-to-solution or cost-to-solution. Our eThread pipeline can scale to support a large number of sequences and is expected to be a viable solution for genome-scale structural bioinformatics and structure-based annotation, particularly, amenable for small genomes such as prokaryotes. The developed pipeline is easily extensible to other types of distributed cyberinfrastructure.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="MEDLINE"><PMID Version="1">24995285</PMID><DateCreated><Year>2014</Year><Month>07</Month><Day>04</Day></DateCreated><DateCompleted><Year>2015</Year><Month>02</Month><Day>24</Day></DateCompleted><DateRevised><Year>2015</Year><Month>08</Month><Day>05</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">2314-6141</ISSN><JournalIssue CitedMedium="Internet"><Volume>2014</Volume><PubDate><Year>2014</Year></PubDate></JournalIssue><Title>BioMed research international</Title><ISOAbbreviation>Biomed Res Int</ISOAbbreviation></Journal><ArticleTitle>Developing eThread pipeline using SAGA-pilot abstraction for large-scale structural bioinformatics.</ArticleTitle><Pagination><MedlinePgn>348725</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1155/2014/348725</ELocationID><Abstract><AbstractText>While most of computational annotation approaches are sequence-based, threading methods are becoming increasingly attractive because of predicted structural information that could uncover the underlying function. However, threading tools are generally compute-intensive and the number of protein sequences from even small genomes such as prokaryotes is large typically containing many thousands, prohibiting their application as a genome-wide structural systems biology tool. To leverage its utility, we have developed a pipeline for eThread--a meta-threading protein structure modeling tool, that can use computational resources efficiently and effectively. We employ a pilot-based approach that supports seamless data and task-level parallelism and manages large variation in workload and computational requirements. Our scalable pipeline is deployed on Amazon EC2 and can efficiently select resources based upon task requirements. We present runtime analysis to characterize computational complexity of eThread and EC2 infrastructure. Based on results, we suggest a pathway to an optimized solution with respect to metrics such as time-to-solution or cost-to-solution. Our eThread pipeline can scale to support a large number of sequences and is expected to be a viable solution for genome-scale structural bioinformatics and structure-based annotation, particularly, amenable for small genomes such as prokaryotes. The developed pipeline is easily extensible to other types of distributed cyberinfrastructure.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Ragothaman</LastName><ForeName>Anjani</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>RADICAL, ECE, Rutgers University, New Brunswick, NJ 08901, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Boddu</LastName><ForeName>Sairam Chowdary</ForeName><Initials>SC</Initials><Identifier Source="ORCID">0000-0003-0990-3321</Identifier><AffiliationInfo><Affiliation>Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kim</LastName><ForeName>Nayong</ForeName><Initials>N</Initials><Identifier Source="ORCID">0000-0003-3412-9956</Identifier><AffiliationInfo><Affiliation>Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Feinstein</LastName><ForeName>Wei</ForeName><Initials>W</Initials><AffiliationInfo><Affiliation>Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Brylinski</LastName><ForeName>Michal</ForeName><Initials>M</Initials><Identifier Source="ORCID">0000-0002-6204-2869</Identifier><AffiliationInfo><Affiliation>Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803, USA ; Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Jha</LastName><ForeName>Shantenu</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>RADICAL, ECE, Rutgers University, New Brunswick, NJ 08901, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kim</LastName><ForeName>Joohyun</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803, USA.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>P20 GM103458-10</GrantID><Acronym>GM</Acronym><Agency>NIGMS NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2014</Year><Month>06</Month><Day>09</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Biomed Res Int</MedlineTA><NlmUniqueID>101600173</NlmUniqueID></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D011506">Proteins</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>Nat Methods. 2009 Nov;6(11 Suppl):S2-5</RefSource><PMID Version="1">19844227</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Brief Bioinform. 2009 Jul;10(4):378-91</RefSource><PMID Version="1">19324930</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>PLoS Comput Biol. 2009 Dec;5(12):e1000605</RefSource><PMID Version="1">20011109</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Genome Biol. 2009;10(2):206</RefSource><PMID Version="1">19226438</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Genome Biol. 2009;10(2):207</RefSource><PMID 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Nov-Dec;27(6):635-60</RefSource><PMID Version="1">18636545</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Nat Rev Genet. 2009 Jan;10(1):57-63</RefSource><PMID Version="1">19015660</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Bioinformatics. 2009 Jun 1;25(11):1363-9</RefSource><PMID Version="1">19357099</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>PLoS Comput Biol. 2009 Dec;5(12):e1000585</RefSource><PMID Version="1">19997483</PMID></CommentsCorrections></CommentsCorrectionsList><MeshHeadingList><MeshHeading><DescriptorName MajorTopicYN="N" UI="D000465">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D000595">Amino Acid Sequence</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D019295">Computational Biology</DescriptorName><QualifierName MajorTopicYN="Y" 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