Energy landscape analysis for regulatory RNA finding using scalable distributed cyberinfrastructure
SUMMARY 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...
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| Vydáno v: | Concurrency and computation Ročník 23; číslo 17; s. 2292 - 2304 |
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Chichester, UK
John Wiley & Sons, Ltd
10.12.2011
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| ISSN: | 1532-0626, 1532-0634, 1532-0634 |
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| Abstract | SUMMARY
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. |
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| AbstractList | Keywords: concurrent programming and distributed computing; Simple API for Grid Applications (SAGA); Pilot-Job abstraction; RNA folding energy landscape; ncRNA gene finding SUMMARY 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. SUMMARY 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. 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. |
| Author | Kim, Joohyun Maddineni, Sharath Aboul-ela, Fareed Jha, Shantenu Huang, Wei |
| Author_xml | – sequence: 1 givenname: Joohyun surname: Kim fullname: Kim, Joohyun email: jhkim@cct.lsu.edu, Joohyun Kim and Shantenu Jha, Center for Computation & Technology, Louisiana State University, Baton Rouge, LA 70803, USA. , jhkim@cct.lsu.edusjha@cct.lsu.edu organization: Center for Computation & Technology, Louisiana State University, LA, 70803, Baton Rouge, USA – sequence: 2 givenname: Wei surname: Huang fullname: Huang, Wei organization: Department of Biological Sciences, Louisiana State University, LA, 70803, Baton Rouge, USA – sequence: 3 givenname: Sharath surname: Maddineni fullname: Maddineni, Sharath organization: Center for Computation & Technology, Louisiana State University, LA, 70803, Baton Rouge, USA – sequence: 4 givenname: Fareed surname: Aboul-ela fullname: Aboul-ela, Fareed organization: Department of Biological Sciences, Louisiana State University, LA, 70803, Baton Rouge, USA – sequence: 5 givenname: Shantenu surname: Jha fullname: Jha, Shantenu email: sjha@cct.lsu.edu, Joohyun Kim and Shantenu Jha, Center for Computation & Technology, Louisiana State University, Baton Rouge, LA 70803, USA. , jhkim@cct.lsu.edusjha@cct.lsu.edu organization: Center for Computation & Technology, Louisiana State University, LA, 70803, Baton Rouge, USA |
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| References_xml | – reference: Montange RK, Mondragón E, et al. Discrimination between closely related cellular metabolites by the SAM-I riboswitch. Journal of Molecular Biology 2009. available online. – reference: Robertus JD, Ladner JE, et al. Correlation between three-dimensional structure and chemical reactivity of transfer RNA. Nucleir Acids Research 1974; 1(7):927-932. – reference: Chen S, Dill KA. RNA folding energy landscapes. Proceedings of the National Academy of Science, USA 2000; 97(2):646-651. – reference: Ogle JM, Carter AP, et al. Insights into the decoding mechanism from recent ribosome structures. Trends in Biochemical Sciences 2005; 28:259-266. – reference: Shcherbakova I, Mitra S, et al. Energy barriers, pathways and dynamics during folding of large, multi-domain RNAs. Current Opinion in Chemical Biology 2008; 12(6):655-666. – reference: Collier AJ, Gallego J, et al. A conserved RNA structure within the HCV IRES elF3-binding site. Nature Structure and Molecular Biology 2002; 9(5):375-380. – reference: Schmeing TM, Ramakrishnan V. What recent ribosome structures have revealed about the mechanism of translation. Nature 2009; 461:1234-1242. – reference: McCaskill JS. The equilibrium partition function and base pair binding probabilities for RNA secondary structure. Biopolymers 1990; 29:1105-1119. – reference: Amaral PP, Dinger ME, et al. The eukaryotic genome as an RNA machine. Science 2008; 319:1787-1789. – reference: Nawrocki EP, Kolbe DL, et al. Infernal 1.0: inference of RNA alignments. Bioinformatics 2009; 25:1335-1337. – reference: Luckow A, Jha Set al.Adaptive distributed replica-exchange simulations. Philosophical Transactions of the Royal Society A: Mathematical,Physical and Engineering Sciences 2009; 367(1897):2595-2606. – reference: Dambach MD, Winkler WC. Expanding roles for metabolite-sensing regulatory RNAs. Current Opinion in Microbiology 2009; 12:161-169. – reference: Montange RK, Batey RT. Structure of the S-adenosylmethionine riboswitch regulatory mRNA element. Nature 2006; 441:1172-1175. – reference: Mathews DH, Turner DH. Prediction of RNA secondary structure by free energy minimization. Current Opinion in Structural Biology 2006; 16(3):270-278. – reference: Gruber AR, Lorenz R et al. The Vienna RNA websuite. Nucleir Acids Research 2008; 36: W70-W74. – reference: Berman HM, Olson WK, et al. The nucleic acid database: a comprehensive relational database of three-dimensional structures of nucleic acids. Biophysical Journal 1992; 63:751-759. – reference: Hyeon C, Thirumalai D. Multiple Probes are required to explore and control the rugged energy landscape of RNA hairpins. Journal of the American Chemical Society 2008; 130:1538-1539. – reference: Wolynes PG. Landscape, funnels, glasses, and folding: from metaphor to software. Proceedings of American Philosophical Society 2001; 145(4):555-563. – reference: Shapiro BA, Yingling YG, et al. 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We investigate the folding energy landscape for a given RNA sequence through Boltzmann ensemble (BE) sampling of RNA secondary structures. The ensemble... We investigate the folding energy landscape for a given RNA sequence through Boltzmann ensemble (BE) sampling of RNA secondary structures. The ensemble of... Keywords: concurrent programming and distributed computing; Simple API for Grid Applications (SAGA); Pilot-Job abstraction; RNA folding energy landscape; ncRNA... |
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| SubjectTerms | concurrent programming and distributed computing ncRNA gene finding Pilot-Job abstraction RNA folding energy landscape Simple API for Grid Applications (SAGA) |
| Title | Energy landscape analysis for regulatory RNA finding using scalable distributed cyberinfrastructure |
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