Abstract
Cloud computing opens new possibilities for computational biologists. Given the pay-as-you-go model and the commodity hardware base, new tools for extensive parallelism are needed to make experimentation in the cloud an attractive option. In this paper, we present EasyProt, a parallel message-passing architecture designed for developing experimental workflows in computational biology while harnessing the power of cloud resources. The system exploits parallelism in two ways: by multithreading modular components on virtual machines while respecting data dependencies and by allowing expansion across multiple virtual machines. Components of the system, called elements, are easily configured for efficient modification and testing of workflows during ever-changing experimentation. Though EasyProt, as an abstract cloud programming model, can be extended beyond computational biology, current development brings cloud computing to experimenters in this important discipline who are facing unprecedented data-processing challenges, with a type system designed for proteomics, interactomics and comparative genomics data, and a suite of elements that perform useful analysis tasks on biological data using cloud resources.
Availability: EasyProt is available as a public abstract machine image (AMI) on Amazon EC2 cloud service, with an open source license, registered with manifest easyprot-ami/easyprot.img.manifest.xml.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Stein, L.D.: The case for cloud computing in genome informatics. Genome Biology 11(5), 207 (2010)
Khalidi, Y.A.: Building a cloud computing platform for new possibilities. Computer 44(3), 29–34 (2011)
Gil, Y., Deelman, E., Ellisman, M., Fahringer, T., Fox, G., et al.: Examining the challenges of scientific workflows. Computer 40(12), 24–32 (2007)
Lord, H.D.: Improving the application development process with modular visualization environments. Computer Graphics 29(2), 10–12 (1995)
Kohler, E., Morris, R., Chen, B., Jannotti, J., Kaashoek, F.: The Click modular router. ACM Trans. on Computer Systems 18(3), 263–297 (2000)
Welsh, M., Culler, D., Brewer, E.: SEDA: an architecture for well-conditioned, scalable internet services. In: Proc. of the 18th Symposium on Operating Systems Principles, SOSP 2001 (2001)
Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R.H., et al.: Above the clouds: a Berkeley view of cloud computing. EECS Department, University of California, Berkeley UCB/EECS-2009-28 (2009)
Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M. (eds.): Workflows for e-Science: Scientific Workflows for Grids. Springer, Heidelberg (2006)
Deelman, E., Singh, G., Su, M., Blythe, J., Gil, Y.: Pegasus: a framework for mapping complex scientific workflows onto distributed systems. Scientific Programming 13, 219–237 (2005)
Juve, G., Deelman, E.: Scientific workflows in the cloud. In: Cafaro, M., Aloisio, G. (eds.) Grids, Clouds and Virtualization, pp. 71–91. Springer, Heidelberg (2010)
Hull, D., Wolstencroft, K., Stevens, R., Goble, C., Pocock, M.R., et al.: Taverna: a tool for building and running workflows of services. Nucleic Acids Research 34(Web Server issue), W729–W732 (2006)
Ludäscher, B., Altintas, I., Berkley, C., Higgins, D., Jaeger, E., et al.: Scientific workflow management and the Kepler system. Concurrency and Computation: Practice and Experience 18, 1039–1065 (2006)
Linke, B., Giegerich, R., Goesmann, A.: Conveyor: a workflow engine for bioinformatic analyses. Bioinformatics 27(7), 903–911 (2011)
Dudley, J.T., Butte, A.J.: In silico research in the era of cloud computing. Nature Biotechnology 28(11), 1181–1185 (2010)
Donoho, D.L., Maleki, A., Rahman, I.U., Shahram, M., Stodden, V.: Reproducible research in computational harmonic analysis. Computing in Science and Engineering 11(1), 8–18 (2009)
Parr, T.J., Quong, R.W.: ANTLR: a predicated-LL(k) parser generator. Software-Practice and Experience 25(7), 789–810 (1995)
Klipp, E., Liebermeister, W., Wierling, C., Kowald, A., Lehrach, H., Herwig, R.: Systems Biology: A Textbook. Wiley-VCH, Weinheim (2009)
Hodgkinson, L., Karp, R.M.: Algorithms to detect multiprotein modularity conserved during evolution. IEEE/ACM Trans. on Computational Biology and Bioinformatics (September 27, 2011), IEEE Computer Society Digital Library. IEEE Computer Society, http://doi.ieeecomputersociety.org/10.1109/TCBB.2011.125
Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Communications of the ACM 51(1), 107–113 (2008)
Bialecki, A., Cafarella, M., Cutting, D., OMalley, O.: Hadoop: a framework for running applications on large clusters built of commodity hardware, Wiki at, http://lucene.apache.org/hadoop
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hodgkinson, L., Rosa, J., Brewer, E.A. (2012). Parallel Software Architecture for Experimental Workflows in Computational Biology on Clouds. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Waśniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2011. Lecture Notes in Computer Science, vol 7204. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31500-8_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-31500-8_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31499-5
Online ISBN: 978-3-642-31500-8
eBook Packages: Computer ScienceComputer Science (R0)