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
Hauptverfasser: Oehmen, C., Nieplocha, J.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York 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 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.
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 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.
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.
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  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
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  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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Snippet 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...
ScalaBLAST relies on a collection of techniques - distributing the target database over available memory, multilevel parallelism to exploit concurrency,...
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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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