Parallelizing exact motif finding algorithms on multi-core

Abbas, Mostafa M.; Bahig, Hazem M.; Abouelhoda, Mohamed; Mohie-Eldin, M. M.

doi:10.1007/s11227-014-1180-3

Cited by 6 publications

(1 citation statement)

References 33 publications

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“…Parallel CVoting (PCVoting) is an enhanced implementation of a voting algorithm to speed up the computational process through parallelizing the CVoting algorithm to run on multi-core machines with a high degree of parallelization efficiency (90%) (Abbas et al, 2014a). Other algorithms are also modified to run on multi-core machines and reduce computational times, such as parallelizing HEPPMSprune, PMS5 and PMS6 algorithms (Brazma et al, 1998), network motif discovery (Abbas et al, 2014b); and the ACME approach that deconstructs the searching into multiple contiguous blocks to increase CPU efficiency (Schatz et al, 2008). The high computational capabilities of GPUs make them an attractive choice for modifying several existing algorithms according to Regulatory motifs of genetic networks GPU structure to achieve suitable performance (Dasari et al, 2010;Brazma et al, 1998;Sahli et al, 2013;Al-Omari et al, 2018;Al-Omari et al, 2015;Al-Omari et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Discovering regulatory motifs of genetic networks using the indexing-tree based algorithm: a parallel implementation

Almomany

Al-Omari

Jarrah

et al. 2020

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Purpose The problem of motif discovery has become a significant challenge in the era of big data where there are hundreds of genomes requiring annotations. The importance of motifs has led many researchers to develop different tools and algorithms for finding them. The purpose of this paper is to propose a new algorithm to increase the speed and accuracy of the motif discovering process, which is the main drawback of motif discovery algorithms. Design/methodology/approach All motifs are sorted in a tree-based indexing structure where each motif is created from a combination of nucleotides: ‘A’, ‘C’, ‘T’ and ‘G’. The full motif can be discovered by extending the search around 4-mer nucleotides in both directions, left and right. Resultant motifs would be identical or degenerated with various lengths. Findings The developed implementation discovers conserved string motifs in DNA without having prior information about the motifs. Even for a large data set that contains millions of nucleotides and thousands of very long sequences, the entire process is completed in a few seconds. Originality/value Experimental results demonstrate the efficiency of the proposed implementation; as for a real-sequence of 1,270,000 nucleotides spread into 2,000 samples, it takes 5.9 s to complete the overall discovering process when the code ran on an Intel Core i7-6700 @ 3.4 GHz machine and 26.7 s when running on an Intel Xeon x5670 @ 2.93 GHz machine. In addition, the authors have improved computational performance by parallelizing the implementation to run on multi-core machines using the OpenMP framework. The speedup achieved by parallelizing the implementation is scalable and proportional to the number of processors with a high efficiency that is close to 100%.

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