Accelerating Motif Discovery: Motif Matching on Parallel Hardware

Sandve, Geir Kjetil; Nedland, Magnar; Syrstad, Øyvind Bø; Eidsheim, Lars Andreas; Abul, Osman; Drabløs, Finn

doi:10.1007/11851561_19

Cited by 10 publications

(4 citation statements)

References 8 publications

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“…An approach to get results in a shorter time is to use high performance computing. Previous approaches to accelerate the motif finding process are based on expensive compute clusters [3] and specialized hardware [9]. This paper presents a proof-of-concept parallelization of motif discovery with MEME on commodity graphics hardware (GPUs) to achieve high performance at low cost.…”

Section: ⋅Lmentioning

confidence: 99%

GPU-MEME: Using Graphics Hardware to Accelerate Motif Finding in DNA Sequences

Chen

Schmidt

Liu

et al. 2008

Pattern Recognition in Bioinformatics

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Abstract. Discovery of motifs that are repeated in groups of biological sequences is a major task in bioinformatics. Iterative methods such as expectation maximization (EM) are used as a common approach to find such patterns. However, corresponding algorithms are highly compute-intensive due to the small size and degenerate nature of biological motifs. Runtime requirements are likely to become even more severe due to the rapid growth of available gene transcription data. In this paper we present a novel approach to accelerate motif discovery based on commodity graphics hardware (GPUs). To derive an efficient mapping onto this type of architecture, we have formulated the compute-intensive parts of the popular MEME tool as streaming algorithms. Our experimental results show that a single GPU allows speedups of one order of magnitude with respect to the sequential MEME implementation. Furthermore, parallelization on a GPUcluster even improves the speedup to two orders of magnitude.

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Section: ⋅Lmentioning

confidence: 99%

GPU-MEME: Using Graphics Hardware to Accelerate Motif Finding in DNA Sequences

Chen

Schmidt

Liu

et al. 2008

Pattern Recognition in Bioinformatics

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“…However, STEME is only practical for finding motifs of up to width 8 on large datasets because its efficiency tails off quickly as the motif width increases. Other strategies for accelerating MEME involve specialized hardware such as parallel pattern matching chips on PCI cards (Sandve et al, 2006). However, these implementations require hardware not available to most researchers.…”

Section: Introductionmentioning

confidence: 99%

EXTREME: an online EM algorithm for motif discovery

Quang

Xie

2014

Bioinformatics

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“…The authors of MEME have implemented a parallel version of MEME, ParaMEME ( 22 ). Other approaches use specialized hardware such as parallel pattern matching chips on PCI cards ( 23 ) or off-loading the computations onto powerful GPUs ( 24 ). All these techniques require hardware that is not commonly available to the typical researcher.…”

Section: Introductionmentioning

confidence: 99%

STEME: efficient EM to find motifs in large data sets

Reid¹,

Wernisch²

2011

Nucleic Acids Research

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MEME and many other popular motif finders use the expectation–maximization (EM) algorithm to optimize their parameters. Unfortunately, the running time of EM is linear in the length of the input sequences. This can prohibit its application to data sets of the size commonly generated by high-throughput biological techniques. A suffix tree is a data structure that can efficiently index a set of sequences. We describe an algorithm, Suffix Tree EM for Motif Elicitation (STEME), that approximates EM using suffix trees. To the best of our knowledge, this is the first application of suffix trees to EM. We provide an analysis of the expected running time of the algorithm and demonstrate that STEME runs an order of magnitude more quickly than the implementation of EM used by MEME. We give theoretical bounds for the quality of the approximation and show that, in practice, the approximation has a negligible effect on the outcome. We provide an open source implementation of the algorithm that we hope will be used to speed up existing and future motif search algorithms.

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Accelerating Motif Discovery: Motif Matching on Parallel Hardware

Cited by 10 publications

References 8 publications

GPU-MEME: Using Graphics Hardware to Accelerate Motif Finding in DNA Sequences

GPU-MEME: Using Graphics Hardware to Accelerate Motif Finding in DNA Sequences

EXTREME: an online EM algorithm for motif discovery

STEME: efficient EM to find motifs in large data sets

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