ArticleInternational journal of genomics2013
Implementation of a parallel protein structure alignment service on cloud.
Article in International journal of genomics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- pmTM-align: scalable pairwise and multiple structure alignment with Apache Spark and OpenMP.BMC bioinformatics · 2020Article
- Precise parallel volumetric comparison of molecular surfaces and electrostatic isopotentials.Algorithms for molecular biology : AMB · 2020Article
- Scalable Extraction of Big Macromolecular Data in Azure Data Lake Environment.Molecules (Basel, Switzerland) · 2019Article
- Cloud computing-based TagSNP selection algorithm for human genome data.International journal of molecular sciences · 2015Article
- Applications of the MapReduce programming framework to clinical big data analysis: current landscape and future trends.BioData mining · 2014Review
- Local alignment tool based on Hadoop framework and GPU architecture.BioMed research international · 2014Article
- A novel approach for discovering condition-specific correlations of gene expressions within biological pathways by using cloud computing technology.BioMed research international · 2014Article
- Cloud computing for protein-ligand binding site comparison.BioMed research international · 2013Article
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Authors and funding
2 authors.
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Abstract
Protein structure alignment has become an important strategy by which to identify evolutionary relationships between protein sequences. Several alignment tools are currently available for online comparison of protein structures. In this paper, we propose a parallel protein structure alignment service based on the Hadoop distribution framework. This service includes a protein structure alignment algorithm, a refinement algorithm, and a MapReduce programming model. The refinement algorithm refines the result of alignment. To process vast numbers of protein structures in parallel, the alignment and refinement algorithms are implemented using MapReduce. We analyzed and compared the structure alignments produced by different methods using a dataset randomly selected from the PDB database. The experimental results verify that the proposed algorithm refines the resulting alignments more accurately than existing algorithms. Meanwhile, the computational performance of the proposed service is proportional to the number of processors used in our cloud platform.
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Registered trials
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