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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<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>
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