Gnare: Automated System For High-Throughput Genome Analysis With Grid Computational Backend

Recent progress in genomics and experimental biology has brought exponential growth of the biological information available for computational analysis in public genomics databases. However, applying the potentially enormous scientific value of this information to the understanding of biological systems requires computing and data storage technology of an unprecedented scale. The Grid, with its aggregated and distributed computational and storage infrastructure, offers an ideal platform for high-throughput bioinformatics analysis. To leverage this we have developed the Genome Analysis Research Environment (GNARE) – a scalable computational system for the high-throughput analysis of genomes, which provides an integrated database and computational backend for data-driven bioinformatics applications. GNARE efficiently automates the major steps of genome analysis including acquisition of data from multiple genomic databases; data analysis by a diverse set of bioinformatics tools; and storage of results and annotations.High-throughput computations in GNARE are performed using distributed heterogeneous Grid computing resources such as Grid2003, TeraGrid, and the DOE Science Grid. Multi-step genome analysis workflows involving massive data processing, the use of application-specific tools and algorithms and updating of an integrated database to provide interactive web access to results are all expressed and controlled by a “virtual data” model which transparently maps computational workflows to distributed Grid resources. This paper describes how Grid technologies such as Globus, Condor, and the Gryphyn Virtual Data System were applied in the development of GNARE. It focuses on our approach to Grid resource allocation and to the use of GNARE as a computational framework for the development of bioinformatics applications.

[1]  中尾 光輝,et al.  KEGG(Kyoto Encyclopedia of Genes and Genomes)〔和文〕 (特集 ゲノム医学の現在と未来--基礎と臨床) -- (データベース) , 2000 .

[2]  Yong Zhao,et al.  Chimera: a virtual data system for representing, querying, and automating data derivation , 2002, Proceedings 14th International Conference on Scientific and Statistical Database Management.

[3]  Miron Livny,et al.  Condor-a hunter of idle workstations , 1988, [1988] Proceedings. The 8th International Conference on Distributed.

[4]  Tim J. P. Hubbard,et al.  SCOP database in 2002: refinements accommodate structural genomics , 2002, Nucleic Acids Res..

[5]  Natalia Maltsev,et al.  Sentra, a database of signal transduction proteins , 2002, Nucleic Acids Res..

[6]  E. G. Shpaer,et al.  Sensitivity and selectivity in protein similarity searches: a comparison of Smith-Waterman in hardware to BLAST and FASTA. , 1996, Genomics.

[7]  S. Henikoff,et al.  Blocks database and its applications. , 1996, Methods in enzymology.

[8]  Hiroyuki Ogata,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 1999, Nucleic Acids Res..

[9]  W R Pearson,et al.  Using the FASTA program to search protein and DNA sequence databases. , 1994, Methods in molecular biology.

[10]  Costas D. Maranas,et al.  Review of the Enzymes and Metabolic Pathways (EMP) Database , 2001 .

[11]  Carl Kesselman,et al.  Performance and scalability of a replica location service , 2004, Proceedings. 13th IEEE International Symposium on High performance Distributed Computing, 2004..

[12]  James E. Bray,et al.  The CATH database: an extended protein family resource for structural and functional genomics , 2003, Nucleic Acids Res..

[13]  Jian Zhang,et al.  The Protein Information Resource: an integrated public resource of functional annotation of proteins , 2002, Nucleic Acids Res..

[14]  Ami Marowka,et al.  The GRID: Blueprint for a New Computing Infrastructure , 2000, Parallel Distributed Comput. Pract..

[15]  Steve Pettifer,et al.  Knowledge Integration , 2004, The Grid 2, 2nd Edition.

[16]  Jason Lee,et al.  The Grid2003 production grid: principles and practice , 2004, Proceedings. 13th IEEE International Symposium on High performance Distributed Computing, 2004..

[17]  Ian T. Foster,et al.  Data management and transfer in high-performance computational grid environments , 2002, Parallel Comput..

[18]  Sándor Pongor,et al.  The SBASE protein domain library, Release 4.0: a collection of annotated protein sequence segments , 1993, Nucleic Acids Res..

[19]  D. Lipman,et al.  National Center for Biotechnology Information , 2019, Springer Reference Medizin.

[20]  E. Myers,et al.  Basic local alignment search tool. , 1990, Journal of molecular biology.

[21]  T. Ideker,et al.  A new approach to decoding life: systems biology. , 2001, Annual review of genomics and human genetics.

[22]  Douglas W. Jones.  Auditing elections , 2004, CACM.

[23]  Alex Bateman,et al.  The InterPro Database, 2003 brings increased coverage and new features , 2003, Nucleic Acids Res..

[24]  Ian T. Foster,et al.  Globus: a Metacomputing Infrastructure Toolkit , 1997, Int. J. High Perform. Comput. Appl..

[25]  Rick L. Stevens,et al.  The SEED: a peer-to-peer environment for genome annotation , 2004, CACM.

[26]  Ian T. Foster,et al.  The virtual data grid: a new model and architecture for data-intensive collaboration , 2003, 15th International Conference on Scientific and Statistical Database Management, 2003..

[27]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[28]  Jo McEntyre,et al.  The NCBI Handbook , 2002 .

[29]  Shmuel Pietrokovski,et al.  Blocks+: a non-redundant database of protein alignment blocks derived from multiple compilations , 1999, Bioinform..

[30]  N. O. Manning,et al.  The protein data bank , 1999, Genetica.