Using domain-specific data to enhance scientific workflow steering queries
IPAW'12 Proceedings of the 4th international conference on Provenance and Annotation of Data and Processes
E-Clouds: A SaaS Marketplace for Scientific Computing
UCC '12 Proceedings of the 2012 IEEE/ACM Fifth International Conference on Utility and Cloud Computing
User-steering of HPC workflows: state-of-the-art and future directions
Proceedings of the 2nd ACM SIGMOD Workshop on Scalable Workflow Execution Engines and Technologies
Designing a parallel cloud based comparative genomics workflow to improve phylogenetic analyses
Future Generation Computer Systems
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Phylogenetic analysis and multiple sequence alignment (MSA) are closely related bioinformatics fields. Phylogenetic analysis makes extensive use of MSA in the construction of phylogenetic trees, which are used to infer the evolutionary relationships between homologous genes. These bioinformatics experiments are usually modeled as scientific workflows. There are many alternative workflows that use different MSA methods to conduct phylogenetic analysis and each one can produce MSA with different quality. Scientists have to explore which MSA method is the most suitable for their experiments. However, workflows for phylogenetic analysis are both computational and data intensive and they may run sequentially during weeks. Although there any many approaches that parallelize these workflows, exploring all MSA methods many become a burden and expensive task. If scientists know the most adequate MSA method a priori, it would spare time and money. To optimize the phylogenetic analysis workflow, we propose in this paper SciHmm, a bioinformatics scientific workflow based in profile hidden Markov models (pHMMs) that aims at determining the most suitable MSA method for a phylogenetic analysis prior than executing the phylogenetic workflow. SciHmm is also executed in parallel in a cloud environment using SciCumulus middleware. The results demonstrated that optimizing a phylogenetic analysis using SciHmm considerably reduce the total execution time of phylogenetic analysis (up to 80%). This optimization also demonstrates that the biological results presented more quality. In addition, the parallel execution of SciHmm demonstrates that this kind of bioinformatics workflow is suitable to be executed in the cloud.