Service-Oriented Computational Biology Community Cloud

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Cloud computing is rising fast these years. It is making software or hardware more attractive through transform them to services. It provides a new developing opportunity for many research fields. A service-oriented Computational Biology Community Cloud (CBCC) is constructed in this paper. Computational biology related programs are collected, integrated and systematic. A four-layer cloud architecture of CBCC is presented and analyzed in this paper. The key technologies are also discussed. By using browsers, CBCC exposes users a lot of simple and easy use services. Users can easily begin their works on CBCC, process results or make their conclusions. Users no need to learn complex programming or prepare many files like before. Some typical applications on CBCC are introduced. By using CBCC, researchers of computational biology can save a lot time and enhance their working efficiency.

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1527-1532

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September 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] Lin Z, Bioinformatics basics: applications in biological science and medicine, Briefings in Bioinformatics, VOL. 9, 2008, pp.256-257.

Google Scholar

[2] Chen K, Zheng WM, Cloud Computing: System Instances and Current Research, Journal of Software, VOL. 20, 2009, pp: 1337-1348.

DOI: 10.3724/sp.j.1001.2009.03493

Google Scholar

[3] Michael Armbrust, Armando Fox, Rean Griffith, Anthony D. Joseph, Randy Katz, Andy Konwinski, Gunho Lee, David Patterson, Ariel Rabkin, Ion Stoica, Matei Zaharia, A View of Cloud Computing, Communications of the ACM, VOL. 53, 2010, pp: 50-58.

DOI: 10.1145/1721654.1721672

Google Scholar

[4] Rajkumar Buyya, Chee Shin Yeo, Srikumar Venugopal, Market-oriented cloud computing: Vision, hype, and reality for delivering it services as computing utilities, 10th IEEE International Conference on High Performance Computing and Communications, (2008).

DOI: 10.1109/hpcc.2008.172

Google Scholar

[5] Ian Gorton, Yan Liu, Jian Yin, Exploring Architecture Options for a Federated, Cloud-Based System Biology Knowledgebase, 2nd IEEE International Conference on Cloud Computing Technology and Science, (2010).

DOI: 10.1109/cloudcom.2010.79

Google Scholar

[6] Li Bo-Hu, Zhang Lin, Wang Shi-Long, TAO Fei, CAO Jun-wei, JIANG Xiao-dan, SONG Xiao, CHAI Xu-dong, Cloud manufacturing: A new service-oriented networked manufacturing model, Computer Integrated Manufacturing Systems, VOL. 16, 2010, pp: 1-7.

Google Scholar

[7] Dimitrios Zissis, Dimitrio Lekkas, Addressing cloud computing security issues, Future Generation Computer Systems. VOL. 28, 2012, pp: 583-592.

DOI: 10.1016/j.future.2010.12.006

Google Scholar

[8] Gerard Briscoe, Alexandros Marinos, Digital Ecosystems in the Clouds: Towards Community Cloud Computing, 3rd IEEE International Conference on Digital Ecosystems and Technologies, (2009).

DOI: 10.1109/dest.2009.5276725

Google Scholar

[9] Dejan Kovachev, Dominik Renzel, Ralf Klamma, Yiwei Cao, Mobile Community Cloud Computing: Emerges and Evolves, IEEE International Conference on Mobile Data Management, (2010).

DOI: 10.1109/mdm.2010.78

Google Scholar

[10] Goossens, Joël, Funk, Shelby, Baruah, Sanjoy, Priority-Driven Scheduling of Periodic Task Systems on Multiprocessors, Real-Time Systems, VOL. 25, 2003, pp: 187-205.

DOI: 10.1023/a:1025120124771

Google Scholar

[11] Kitchen D. B., Decornez H., Furr J. R., Bajorath J, Docking and scoring in virtual screening for drug discovery: Methods and applications, Drug Discovery, VOL. 3, 2004, pp: 935-949.

DOI: 10.1038/nrd1549

Google Scholar

[12] Ling Kang, Quan Guo, Xicheng Wang, A hierarchical method for molecular docking using cloud computing, Bioorganic & Medicinal Chemistry Letters, VOL. 22, 2012, pp: 6568-6572.

DOI: 10.1016/j.bmcl.2012.09.016

Google Scholar

[13] AdeockSA, McCamrnon JA, Molecular dynamics: survey of methods for simulating the activity of proteins, Chemical Reviews, VOL. 106, 2006, pp: 1589-1615.

Google Scholar

[14] Karplus M, Molecular dynamics of biological macromolecules: a brief history and perspective, Biopolymers, VOL. 68, 2003, pp: 350-358.

DOI: 10.1002/bip.10266

Google Scholar

[15] Xinli Liu, Xicheng Wang, Huangliang Jiang, A steered molecular dynamics method with direction optimization and its applications on ligand molecule dissociation, Journal of Biochemical and Biophysical Methods, VOL. 70, 2008, pp: 857-864.

DOI: 10.1016/j.jbbm.2007.10.006

Google Scholar

[16] Qiu Zhijun, Wang Xicheng, Identification of ligand-binding pockets in proteins using residue preference methods, Protein and peptide letters, VOL. 16, 2009, pp: 984-990.

DOI: 10.2174/092986609788923284

Google Scholar

[17] Junfeng Gu, Xicheng Wang, Jincheng Zhao, A novel method for multiple sequence alignment based on wavelet package transform, International Journal of Information Technology, VOL. 11, 2005, pp: 133-141.

Google Scholar

[18] Junfeng Gu, Xicheng Wang, Optimizing energy potential for protein fold recognition, 7th World Congress on Structural and Multidisciplinary Optimization, (2007).

Google Scholar

[19] Junfeng Gu, Honglin Li, Hualiang Jiang, Xicheng Wang, A simple Cα-SC potential with higher accuracy for protein fold recognition, Biochemical and Biophysical Research Communications, VOL. 379, 2009, pp: 610-615.

DOI: 10.1016/j.bbrc.2008.12.131

Google Scholar

[20] Chen Y.Z., Zhi, D. G, Ligand-protein inverse docking and its potential use in the computer search of protein targets of a small molecule, Proteins, VOL. 43, 2001, pp: 217-26.

DOI: 10.1002/1097-0134(20010501)43:2<217::aid-prot1032>3.0.co;2-g

Google Scholar