Improved algorithms for finding edit distance based motifs

Pal, Soumitra; Rajasekaran, Sanguthevar

doi:10.1109/bibm.2015.7359740

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…Discovering all exact matched repeats can be perfectly solved by Suffix Tree [25]. For those approximate repeats algorithms [18, 26], although they are efficient when σ = 2, they are specially designed for “at least twice”. For example, the extension techniques in REPuter [18] require mismatches are at identical positions across the support set.…”

Section: Related Workmentioning

confidence: 99%

“…REPuter [18] is the closest effort toward mining frequent approximate consecutive patterns under Hamming Distance . Recently, some novel and special rules and Trie-based data structure is also proposed to further improve the efficiency [26]. However, they can only discover patterns with two occurrences and mismatches at identical positions across the support set.…”

Section: Introductionmentioning

confidence: 99%

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MACFP: Maximal Approximate Consecutive Frequent Pattern Mining under Edit Distance

Shang

Peng

2016

Proceedings of the 2016 SIAM International Conference on Data Mining

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Consecutive pattern mining aiming at finding sequential patterns substrings, is a special case of frequent pattern mining and has been played a crucial role in many real world applications, especially in biological sequence analysis, time series analysis, and network log mining. Approximations, including insertions, deletions, and substitutions, between strings are widely used in biological sequence comparisons. However, most existing string pattern mining methods only consider hamming distance without insertions/deletions (indels). Little attention has been paid to the general approximate consecutive frequent pattern mining under edit distance, potentially due to the high computational complexity, particularly on DNA sequences with billions of base pairs. In this paper, we introduce an efficient solution to this problem. We first formulate the Maximal Approximate Consecutive Frequent Pattern Mining (MACFP) problem that identifies substring patterns under edit distance in a long query sequence. Then, we propose a novel algorithm with linear time complexity to check whether the support of a substring pattern is above a predefined threshold in the query sequence, thus greatly reducing the computational complexity of MACFP. With this fast decision algorithm, we can efficiently solve the original pattern discovery problem with several indexing and searching techniques. Comprehensive experiments on sequence pattern analysis and a study on cancer genomics application demonstrate the effectiveness and efficiency of our algorithm, compared to several existing methods.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MACFP: Maximal Approximate Consecutive Frequent Pattern Mining under Edit Distance

Shang

Peng

2016

Proceedings of the 2016 SIAM International Conference on Data Mining

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“…Issues in motif discovery can be categorized into 3 types, namely Simple Motif Search (SMS), Edit distance based (EMS), and Planted Motif Search (PMS) [3]. The purpose of SMS is to find all the motifs from lengths 1 to the specified length in all sequences of [4] while the purpose of the EMS is to find all the motifs on the desired number of sequences [5]. PMS aims to find the motive that appears in every sequence that exists [6].…”

Section: Introductionmentioning

confidence: 99%

Parallel random projection using R high performance computing for planted motif search

Riza¹,

Dhiba²,

Setiawan³

et al. 2019

TELKOMNIKA

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Motif discovery in DNA sequences is one of the most important issues in bioinformatics. Thus, algorithms for dealing with the problem accurately and quickly have always been the goal of research in bioinformatics. Therefore, this study is intended to modify the random projection algorithm to be implemented on R high performance computing (i.e., the R package pbdMPI). Some steps are needed to achieve this objective, ie preprocessing data, splitting data according to number of batches, modifying and implementing random projection in the pbdMPI package, and then aggregating the results. To validate the proposed approach, some experiments have been conducted. Several benchmarking data were used in this study by sensitivity analysis on number of cores and batches. Experimental results show that computational cost can be reduced, which is that the computation cost of 6 cores is faster around 34 times compared with the standalone mode. Thus, the proposed approach can be used for motif discovery effectively and efficiently.

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“…Issues in motifdiscovery can be categorizedinto 3 types, namely Simple Motif Search (SMS), Edit distance based (EMS), and Planted Motif Search (PMS) [3]. The purpose of SMS is to find all the motifs from lengths 1 to the specified length in all sequences of [4] while the purpose of the EMS is tofind all the motifs on the desired number of sequences [5]. PMS aims to find the motive that appears in every sequence that exists [6].…”

Section: …………………………………………………………………………………………………… Introduction:-mentioning

confidence: 99%

On the High Performance Computing for Motif Discovery in Dna Sequences.

Arslan¹

2018

IJAR

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Motif discovery, R programming language, high performance computing, random projection.In bioinformatics, one of the most important research problems is the Motif discovery in DNA sequences. The algorithm having accuracy and speed has always been the goal of research in bioinformatics, for solving this problem. Therefore, the idea of this research study is to modify the random projection algorithm to be implemented using high performance computing technique (i.e., the R package pbdMPI). The steps that are needed to achieve this objective are the main focus of this study, i.e. preprocessing data, splitting data according to number of batches, modifying and implementing random projection in the pbdMPI package, and then aggregating the results. To validate this approach, some experiments have been conducted. Several benchmarking data were used in this study by sensitivity analysis on number of cores and batches. Experimental results show that computational cost can be reduced. Thus, the proposed approach can be used for the motif discovery effectively and efficiently.

show abstract

Improved algorithms for finding edit distance based motifs

Cited by 5 publications

References 11 publications

MACFP: Maximal Approximate Consecutive Frequent Pattern Mining under Edit Distance

MACFP: Maximal Approximate Consecutive Frequent Pattern Mining under Edit Distance

Parallel random projection using R high performance computing for planted motif search

On the High Performance Computing for Motif Discovery in Dna Sequences.

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