Identifying Candidate Disease Genes with High-Performance Computing

Terry Braun; Todd Scheetz; Gregg Webster; Abe Clark; Edwin Stone; Val Sheffield; Thomas Casavant

doi:10.1023/A:1024417200364

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Identifying Candidate Disease Genes with High-Performance Computing

Journal article

Peer reviewed

Identifying Candidate Disease Genes with High-Performance Computing

Terry Braun, Todd Scheetz, Gregg Webster, Abe Clark, Edwin Stone, Val Sheffield and Thomas Casavant

The Journal of Supercomputing, Vol.26(1), pp.7-24

08/2003

DOI: 10.1023/A:1024417200364

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Abstract

The publicly-funded effort to read the complete nucleotide sequence of the human genome, the human genome project (HGP), is nearing completion of the approximately three billion nucleotides of the human genome. In addition, several valuable sources of information have been developed as direct and indirect results of the HGP. These include the genome sequencing of model organisms (Escherichia coli, Saccharomyces cerevisiae, the fruit fly Drosophila melanogaster, the worm Caenorhabditis elegans, and the laboratory mouse), gene discovery projects (expressed sequence tags and full-length), and new high-throughput expression analyzes. These resources are invaluable in identifying the trascriptome and proteome—the set of transcribed and translated sequences. However, the bulk of the effort still remains—to identify the functional and structural elements contained within gene sequences. Addressing these challenges requires the use of high-performance computing. There are currently hundreds of databases containing biological information that may contain data relevant to the identification of disease-causing genes. Knowledge discovery using these databases holds enormous potential, if sufficient computing resources are utilized to process the overwhelming amounts of data. We are developing a system to acquire and mine data from a subset of these databases to aid our efforts to identify disease genes. A high performance cluster of Linux of workstations is used to perform distributed sequence alignments as part of our analysis and processing. This system has been used to mine the GeneMap99 database within specific genomic intervals to identify potential candidate disease genes associated with Bardet-Biedl syndrome (BBS).

Processor Architectures

Programming Languages, Compilers, Interpreters

biological databases

Computer Science

bioinformatics

knowledge discovery

Computer Science, general

high-performance computing

disease gene discovery

Details

Title: Subtitle: Identifying Candidate Disease Genes with High-Performance Computing
Creators: Terry Braun - Coordinated Laboratory for Computational Genomics, Department of Ophthalmology, Department of Biomedical Engineering The University of Iowa Iowa City Iowa USA
Todd Scheetz - Coordinated Laboratory for Computational Genomics The University of Iowa Iowa City Iowa USA
Gregg Webster - Coordinated Laboratory for Computational Genomics The University of Iowa Iowa City Iowa USA
Abe Clark - Alcon Laboratories Fort Worth Texas
Edwin Stone - Department of Ophthalmology The University of Iowa Iowa City Iowa USA
Val Sheffield - Department of Pediatrics The University of Iowa Iowa City Iowa USA
Thomas Casavant - Coordinated Laboratory for Computational Genomics, Department of Ophthalmology, Department of Biomedical Engineering The University of Iowa Iowa City Iowa USA
Resource Type: Journal article
Publication Details: The Journal of Supercomputing, Vol.26(1), pp.7-24
DOI: 10.1023/A:1024417200364
ISSN: 0920-8542
eISSN: 1573-0484
Publisher: Kluwer Academic Publishers; Boston
Language: English
Date published: 08/2003
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Stead Family Department of Pediatrics; Iowa Neuroscience Institute; Medical Genetics and Genomics; Ophthalmology and Visual Sciences
Record Identifier: 9983980030502771

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