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Heterogeneous computing architecture for fast detection of SNP-SNP interactions.

Sluga D, Curk T, Zupan B, Lotric U - BMC Bioinformatics (2014)

Bottom Line: Their utility resulted in an order of magnitude shorter execution times when compared to the single-threaded CPU implementation.GPU implementation on a single Nvidia Tesla K20 runs twice as fast as that for the MIC architecture-based Xeon Phi P5110 coprocessor, but also requires considerably more programming effort.On the other hand the new MIC architecture, albeit lacking in performance reduces the programming effort and makes it up with a more general architecture suitable for a wider range of problems.

View Article: PubMed Central - HTML - PubMed

Affiliation: Faculty of Computer and Information Science, University of Ljubljana, Trzaska 25, SI 1000 Ljubljana, SI, Slovenia. uros.lotric@fri.uni-lj.si.

ABSTRACT

Background: The extent of data in a typical genome-wide association study (GWAS) poses considerable computational challenges to software tools for gene-gene interaction discovery. Exhaustive evaluation of all interactions among hundreds of thousands to millions of single nucleotide polymorphisms (SNPs) may require weeks or even months of computation. Massively parallel hardware within a modern Graphic Processing Unit (GPU) and Many Integrated Core (MIC) coprocessors can shorten the run time considerably. While the utility of GPU-based implementations in bioinformatics has been well studied, MIC architecture has been introduced only recently and may provide a number of comparative advantages that have yet to be explored and tested.

Results: We have developed a heterogeneous, GPU and Intel MIC-accelerated software module for SNP-SNP interaction discovery to replace the previously single-threaded computational core in the interactive web-based data exploration program SNPsyn. We report on differences between these two modern massively parallel architectures and their software environments. Their utility resulted in an order of magnitude shorter execution times when compared to the single-threaded CPU implementation. GPU implementation on a single Nvidia Tesla K20 runs twice as fast as that for the MIC architecture-based Xeon Phi P5110 coprocessor, but also requires considerably more programming effort.

Conclusions: General purpose GPUs are a mature platform with large amounts of computing power capable of tackling inherently parallel problems, but can prove demanding for the programmer. On the other hand the new MIC architecture, albeit lacking in performance reduces the programming effort and makes it up with a more general architecture suitable for a wider range of problems.

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SNPsyn graphical user interface.a) A synergy versus information gain plot is used to select SNP-SNP pairs. b) Gene Ontology enrichment analysis for genes overlapping with selected SNP-SNP pairs. c) Synergy network of selected SNPs.
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Figure 1: SNPsyn graphical user interface.a) A synergy versus information gain plot is used to select SNP-SNP pairs. b) Gene Ontology enrichment analysis for genes overlapping with selected SNP-SNP pairs. c) Synergy network of selected SNPs.

Mentions: SNPsyn [13] (Figure 1) was developed as an interactive software tool for efficient exploration and discovery of interactions among single nucleotide polymorphisms (SNPs) in case-control genome-wide association study (GWAS) data. It uses an information-theoretic approach to evaluate SNP-SNP interactions [14]. Information gain is computed for every individual SNP, which allows the user to identify SNPs that are most associated with the disease under study. When searching for interesting pairs of SNPs, SNPsyn estimates the synergy between a pair of SNPs by computing the interaction gain. Information gain can identify SNP pairs with non-additive effects. Results are presented in an interactive graphical user interface that allows the user to select the most synergistic pairs, perform Gene Ontology enrichment analysis and visualize the synergy network among the selected SNP-SNP pairs.


Heterogeneous computing architecture for fast detection of SNP-SNP interactions.

Sluga D, Curk T, Zupan B, Lotric U - BMC Bioinformatics (2014)

SNPsyn graphical user interface.a) A synergy versus information gain plot is used to select SNP-SNP pairs. b) Gene Ontology enrichment analysis for genes overlapping with selected SNP-SNP pairs. c) Synergy network of selected SNPs.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4230497&req=5

Figure 1: SNPsyn graphical user interface.a) A synergy versus information gain plot is used to select SNP-SNP pairs. b) Gene Ontology enrichment analysis for genes overlapping with selected SNP-SNP pairs. c) Synergy network of selected SNPs.
Mentions: SNPsyn [13] (Figure 1) was developed as an interactive software tool for efficient exploration and discovery of interactions among single nucleotide polymorphisms (SNPs) in case-control genome-wide association study (GWAS) data. It uses an information-theoretic approach to evaluate SNP-SNP interactions [14]. Information gain is computed for every individual SNP, which allows the user to identify SNPs that are most associated with the disease under study. When searching for interesting pairs of SNPs, SNPsyn estimates the synergy between a pair of SNPs by computing the interaction gain. Information gain can identify SNP pairs with non-additive effects. Results are presented in an interactive graphical user interface that allows the user to select the most synergistic pairs, perform Gene Ontology enrichment analysis and visualize the synergy network among the selected SNP-SNP pairs.

Bottom Line: Their utility resulted in an order of magnitude shorter execution times when compared to the single-threaded CPU implementation.GPU implementation on a single Nvidia Tesla K20 runs twice as fast as that for the MIC architecture-based Xeon Phi P5110 coprocessor, but also requires considerably more programming effort.On the other hand the new MIC architecture, albeit lacking in performance reduces the programming effort and makes it up with a more general architecture suitable for a wider range of problems.

View Article: PubMed Central - HTML - PubMed

Affiliation: Faculty of Computer and Information Science, University of Ljubljana, Trzaska 25, SI 1000 Ljubljana, SI, Slovenia. uros.lotric@fri.uni-lj.si.

ABSTRACT

Background: The extent of data in a typical genome-wide association study (GWAS) poses considerable computational challenges to software tools for gene-gene interaction discovery. Exhaustive evaluation of all interactions among hundreds of thousands to millions of single nucleotide polymorphisms (SNPs) may require weeks or even months of computation. Massively parallel hardware within a modern Graphic Processing Unit (GPU) and Many Integrated Core (MIC) coprocessors can shorten the run time considerably. While the utility of GPU-based implementations in bioinformatics has been well studied, MIC architecture has been introduced only recently and may provide a number of comparative advantages that have yet to be explored and tested.

Results: We have developed a heterogeneous, GPU and Intel MIC-accelerated software module for SNP-SNP interaction discovery to replace the previously single-threaded computational core in the interactive web-based data exploration program SNPsyn. We report on differences between these two modern massively parallel architectures and their software environments. Their utility resulted in an order of magnitude shorter execution times when compared to the single-threaded CPU implementation. GPU implementation on a single Nvidia Tesla K20 runs twice as fast as that for the MIC architecture-based Xeon Phi P5110 coprocessor, but also requires considerably more programming effort.

Conclusions: General purpose GPUs are a mature platform with large amounts of computing power capable of tackling inherently parallel problems, but can prove demanding for the programmer. On the other hand the new MIC architecture, albeit lacking in performance reduces the programming effort and makes it up with a more general architecture suitable for a wider range of problems.

Show MeSH