High performance implementation of planted motif problem using suffix trees

Dasari, Naga Shailaja; Ranjan, Desh; Zubair, Mohammad

doi:10.1109/hpcsim.2011.5999825

Cited by 10 publications

(16 citation statements)

References 9 publications

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“…We estimate that our multicore algorithm is faster than other parallel algorithms for the motif search problem on large challenging instances. For example, we estimate that PMS6MC can solve (19,7) instances 3.6 times faster than mSPELLER-16 using the 16-core CPU of [8] and about 2 times faster than gSPELLER-4 using 4 GPU devices. Run times for PMS6 and PMS6MC…”

Section: Discussionmentioning

confidence: 99%

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PMS6MC: A multicore algorithm for motif discovery

Bandyopadhyay

Sahni

Rajasekaran

2013

2013 IEEE 3rd International Conference on Computational Advances in Bio and Medical Sciences (ICCABS)

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We develop an efficient multicore algorithm, PMS6MC, for the (l, d)-motif discovery problem in which we are to find all strings of length l that appear in every string of a given set of strings with at most d mismatches. PMS6MC is based on PMS6, which is currently the fastest single-core algorithm for motif discovery in large instances. The speedup, relative to PMS6, attained by our multicore algorithm ranges from a high of 6.62 for the (17,6) challenging instances to a low of 2.75 for the (13,4) challenging instances on an Intel 6-core system. We estimate that PMS6MC is 2 to 4 times faster than other parallel algorithms for motif search on large instances.

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Section: Discussionmentioning

confidence: 99%

“…PMS6MC, on the other hand, takes 8 seconds on an average to solve (15,5) instances and 3.5 hours on an average to solve (23,9) instances. The speedup achieved by PMS6MC over PMS6 varies from a low of 2.75 for (13,4) instances to a high of 6.62 for (21,8) instances. For (19,7) and larger instances PMS6MC achieves a speedup of over 5.…”

Section: B Pms6 and Pms6mcmentioning

confidence: 99%

PMS6MC: A multicore algorithm for motif discovery

Bandyopadhyay

Sahni

Rajasekaran

2013

2013 IEEE 3rd International Conference on Computational Advances in Bio and Medical Sciences (ICCABS)

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“…Parallelizing motif extraction attracted a lot of research efforts, especially in bioinformatics [3,4,6,7,16,19]. Challa and Thulasiraman [4] handle a dataset of 15,000 protein sequences with the longest sequence being 577 symbols only; their method does not manage to scale to more than 64 cores.…”

Section: Related Workmentioning

confidence: 99%

“…S[i] is the ith symbol in S, where 0 ≤ i < |S|. A subsequence of S that starts at position i and ends at position j is denoted by S [i, j] or simply by its position pair (i, j); for example, (7,11) represents GGTGC in Fig. 1.…”

Section: Motifsmentioning

confidence: 99%

“…1). An occurrence is denoted as a pair of start and end positions in S. The set of occurrences for m is L(m) = { (1,5), (4,8), (7,11), (12,16), (18,22)}, and the frequency of m is |L(m)| = 5. Compared to the well-studied frequent itemset mining problem in transactional data, repeated motif extraction has three differences: (i) Order is important.…”

Section: Introductionmentioning

confidence: 99%

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ACME: A scalable parallel system for extracting frequent patterns from a very long sequence

2014

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Modern applications, including bioinformatics, time series, and web log analysis, require the extraction of frequent patterns, called motifs, from one very long (i.e., several gigabytes) sequence. Existing approaches are either heuristics that are error-prone, or exact (also called combinatorial) methods that are extremely slow, therefore, applicable only to very small sequences (i.e., in the order of megabytes). This paper presents ACME, a combinatorial approach that scales to gigabyte-long sequences and is the first to support supermaximal motifs. ACME is a versatile parallel system that can be deployed on desktop multi-core systems, or on thousands of CPUs in the cloud. However, merely using more compute nodes does not guarantee efficiency, because of the related overheads. To this end, ACME introduces an automatic tuning mechanism that suggests the appropriate number of CPUs to utilize, in order to meet the user constraints in terms of run time, while minimizing the financial cost of cloud resources. Our experiments show that, compared to the state of the art, ACME supports three orders of magnitude longer sequences (e.g., DNA for the entire human genome); handles large alphabets (e.g., English alphabet for Wikipedia); scales out to 16,384 CPUs on a supercomputer; and supports elastic deployment in the cloud.

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