Unsupervised Learning of Multiple Motifs in Biopolymers Using Expectation Maximization
Machine Learning - Special issue on applications in molecular biology
DWE: Discriminating Word Enumerator
Bioinformatics
Streaming Algorithms for Biological Sequence Alignment on GPUs
IEEE Transactions on Parallel and Distributed Systems
Accelerating motif discovery: motif matching on parallel hardware
WABI'06 Proceedings of the 6th international conference on Algorithms in Bioinformatics
Pattern Recognition Letters
A Map-Reduce Based Framework for Heterogeneous Processing Element Cluster Environments
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
A parallel cooperative team of multiobjective evolutionary algorithms for motif discovery
The Journal of Supercomputing
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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 GPU-cluster even improves the speedup to two orders of magnitude.