ScalaBLAST: A Scalable Implementation of BLAST for High-Performance Data-Intensive Bioinformatics Analysis
Genes in an organism's DNA (genome) have embedded in them information about proteins, which are the molecules that do most of a cell's work. A typical bacterial genome contains on the order of 5,000 genes. Mammalian genomes can contain tens of thousands of genes. For each genome sequenced,...
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| Veröffentlicht in: | IEEE transactions on parallel and distributed systems Jg. 17; H. 8; S. 740 - 749 |
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| Sprache: | Englisch |
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IEEE
01.08.2006
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1045-9219, 1558-2183 |
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| Abstract | Genes in an organism's DNA (genome) have embedded in them information about proteins, which are the molecules that do most of a cell's work. A typical bacterial genome contains on the order of 5,000 genes. Mammalian genomes can contain tens of thousands of genes. For each genome sequenced, the challenge is to identify protein components (proteome) being actively used for a given set of conditions. Fundamentally, sequence alignment is a sequence matching problem focused on unlocking protein information embedded in the genetic code, making it possible to assemble a "tree of lifeâ⬠by comparing new sequences against all sequences from known organisms. But, the memory footprint of sequence data is growing more rapidly than per-node core memory. Despite years of research and development, high-performance sequence alignment applications either do not scale well, cannot accommodate very large databases in core, or require special hardware. We have developed a high-performance sequence alignment application, ScalaBLAST, which accommodates very large databases and which scales linearly to as many as thousands of processors on both distributed memory and shared memory architectures, representing a substantial improvement over the current state-of-the-art in high-performance sequence alignment with scaling and portability. ScalaBLAST relies on a collection of techniques-distributing the target database over available memory, multilevel parallelism to exploit concurrency, parallel I/O, and latency hiding through data prefetching-to achieve high-performance and scalability. This demonstrated approach of database sharing combined with effective task scheduling should have broad ranging applications to other informatics-driven sciences. |
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| AbstractList | Genes in an organism's DNA (genome) have embedded in them information about proteins, which are the molecules that do most of a cell's work. A typical bacterial genome contains on the order of 5,000 genes. Mammalian genomes can contain tens of thousands of genes. For each genome sequenced, the challenge is to identify protein components (proteome) being actively used for a given set of conditions. Fundamentally, sequence alignment is a sequence matching problem focused on unlocking protein information embedded in the genetic code, making it possible to assemble a "tree of lifeâ⬠by comparing new sequences against all sequences from known organisms. But, the memory footprint of sequence data is growing more rapidly than per-node core memory. Despite years of research and development, high-performance sequence alignment applications either do not scale well, cannot accommodate very large databases in core, or require special hardware. We have developed a high-performance sequence alignment application, ScalaBLAST, which accommodates very large databases and which scales linearly to as many as thousands of processors on both distributed memory and shared memory architectures, representing a substantial improvement over the current state-of-the-art in high-performance sequence alignment with scaling and portability. ScalaBLAST relies on a collection of techniques-distributing the target database over available memory, multilevel parallelism to exploit concurrency, parallel I/O, and latency hiding through data prefetching-to achieve high-performance and scalability. This demonstrated approach of database sharing combined with effective task scheduling should have broad ranging applications to other informatics-driven sciences. Genes in an organism's DNA (genome) have embedded in them information about proteins, which are the molecules that do most of a cell's work. A typical bacterial genome contains on the order of 5,000 genes. Mammalian genomes can contain tens of thousands of genes. For each genome sequenced, the challenge is to identify protein components (proteome) being actively used for a given set of conditions. Fundamentally, sequence alignment is a sequence matching problem focused on unlocking protein information embedded in the genetic code, making it possible to assemble a "tree of life" by comparing new sequences against all sequences from known organisms. But, the memory footprint of sequence data is growing more rapidly than per-node core memory. Despite years of research and development, high-performance sequence alignment applications either do not scale well, cannot accommodate very large databases in core, or require special hardware. We have developed a high-performance sequence alignment application, ScalaBLAST, which accommodates very large databases and which scales linearly to as many as thousands of processors on both distributed memory and shared memory architectures, representing a substantial improvement over the current state-of-the-art in high-performance sequence alignment with scaling and portability. ScalaBLAST relies on a collection of techniques - distributing the target database over available memory, multilevel parallelism to exploit concurrency, parallel I/O, and latency hiding through data prefetching - to achieve high-performance and scalability. This demonstrated approach of database sharing combined with effective task scheduling should have broad ranging applications to other informatics-driven sciences ScalaBLAST relies on a collection of techniques - distributing the target database over available memory, multilevel parallelism to exploit concurrency, parallel I/O, and latency hiding through data prefetching - to achieve high-performance and scalability. |
| Author | Oehmen, C. Nieplocha, J. |
| Author_xml | – sequence: 1 givenname: C. surname: Oehmen fullname: Oehmen, C. email: Christopher.Oehmen@pnl.gov organization: Computational Sciences and Mathematics Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, PO Box 999, MSIN: K7-90, Richland, WA 99352 – sequence: 2 givenname: J. surname: Nieplocha fullname: Nieplocha, J. email: Jarek.Nieplochal@pnl.gov organization: Computational Sciences and Mathematics Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, PO Box 999, MSIN: K7-90, Richland, WA 99352 |
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| SubjectTerms | Assembly Bioinformatics BLAST Data analysis DNA Genetics Genomes Genomics Global Arrays High-performance sequence alignment Microorganisms Proteins Sequences Studies |
| Title | ScalaBLAST: A Scalable Implementation of BLAST for High-Performance Data-Intensive Bioinformatics Analysis |
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