NDLI: Optimizing Phylogenetic Analysis Using SciHmm Cloud-based Scientific Workflow

Content Provider	IEEE Xplore Digital Library
Author	Ocana, K.A.C.S. de Oliveira, D. Dias, J. Ogasawara, E. Mattoso, M.
Copyright Year	2011
Abstract	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.
Starting Page	62
Ending Page	69
File Size	314720
Page Count	8
File Format	PDF
ISBN	9781457721632
DOI	10.1109/eScience.2011.17
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-05
Publisher Place	Sweden
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proteins Databases Hidden Markov models Genomics Muscles Phylogeny cloud computing Bioinformatics profile hidden Markov models scientific workflows
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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