Motif Discovery Using Evolutionary Algorithms

Shao, Linlin; Chen, Yuehui; Abraham, Ajith

doi:10.1109/socpar.2009.88

Cited by 18 publications

(2 citation statements)

References 22 publications

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“…The project comprises of two kinds of problems: the first being searching for a particular pattern present in thousands of genome sequences and the second being DE [29] algorithm which involves crossover operation with fine tuning. The searching of motifs in genome sequences requires multiple pattern matching.…”

Section: Problem Formulationmentioning

confidence: 99%

An Intuitive Wizard to Identify Pattern in Similarity Expressed Gene Set of Rice

Barrow¹,

Naryanan²

2018

IJIES

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While the genome sequences of different crops have been published and annotated, relatively little is known about the transcriptional networks that regulate gene expression. There are different ways of finding significant motifs in genome sequences, evolutionary motif finding algorithms being one of them. The work presented in this paper uses Differential Evolution (DE) Algorithm to discover new motifs in rice genome sequences and aims to provide an intuitive wizard to analyse the micro-array analysis of expression patterns (motifs) for the queried genes. The used DE algorithm involves crossover operation with fine tuning. Further, to search for already known motifs parallel version of adaptive hash based pattern matching algorithm has been implemented. The study is conducted on rice genome sequences for twelve chromosomes each consisting of approximately 70000 genome sequences. In this paper, experimental results provide a comparison of serial and parallel version of the algorithm applied for the same dataset which shows that parallel implementation improves the performance by almost 25-30% to search the patterns in genome sequences and provide a list of newly discovered significant motifs.

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Section: Problem Formulationmentioning

confidence: 99%

An Intuitive Wizard to Identify Pattern in Similarity Expressed Gene Set of Rice

Barrow¹,

Naryanan²

2018

IJIES

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“…Some examples of evolutionary techniques are Finding Motifs by Genetic Algorithm (FMGA) [25], which is a genetic algorithm (GA) based on the SAGA operators [28]; Structured Genetic Algorithm (St-GA) [37]; motif discovery using a genetic algorithm (MDGA) [3]; and GAME [42], a GA used for detecting cis-regulatory elements. Although there are other proposals such as TS-BFO [33], which integrates bacterial foraging optimization (BFO) and tabu search (TS), the EDA/DE algorithm proposed by the same authors [34], or the population clustering evolutionary algorithm (PCEA) [26]; we may note that many of them are based on GAs [20]. Furthermore, almost all the proposals listed employ a single objective and the motif length is given beforehand.…”

Section: Related Workmentioning

confidence: 99%

Multiobjective optimization algorithms for motif discovery in DNA sequences

González-Álvarez

Vega‐Rodríguez

Rubio-Largo

2014

Genet Program Evolvable Mach

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Optimization techniques have become powerful tools for approaching multiple NP-hard optimization problems. In this kind of problem it is practically impossible to obtain optimal solutions, thus we must apply approximation strategies such as metaheuristics. In this paper, seven metaheuristics have been used to address an important biological problem known as the motif discovery problem. As it is defined as a multiobjective optimization problem, we have adapted the proposed algorithms to this optimization context. We evaluate the proposed metaheuristics on 54 sequence datasets that belong to four organisms with different numbers of sequences and sizes. The results have been analysed in order to discover which algorithm performs best in each case. The algorithms implemented and the results achieved can assist biological researchers in the complicated task of finding DNA patterns with an important biological relevance.

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