Multiple Sequence Alignment using Fuzzy Logic

Nasser, Sara; Vert, Gregory; Nicolescu, Mircea; Murray, A. G.

doi:10.1109/cibcb.2007.4221237

Cited by 16 publications

(14 citation statements)

References 2 publications

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“…TIGR is a well known assembler [19]. FGS is the fuzzy sequence assembly method that is described in [4]. ClusFGS is the method described in this paper and is a modified version of FGS.…”

Section: Experiments and Resultsmentioning

confidence: 99%

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Fuzzy Classification of Genome Sequences Prior to Assembly Based on Similarity Measures

Nasser

Vert

Breland

et al. 2007

NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society

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-Nucleotide sequencing of genomic data is an important step towards building understanding of gene expression. Current limitations in sequencing limit the number of base pairs that can be processed to only several hundred at a time. Consequently, these sequenced substrings need to be assembled into the overall genome. However, the existence of insertions, deletions and substitutions can complicate the assembly of subsequences and confuse existing methods. What has been needed is an approach that deals with ambiguity in trying to match and assemble a genome from its sequenced subsequences. This research develops fuzzy similarity measures between subsequences that are then incorporated into an assembler based on fuzzy logic and fuzzy similarity measures. The research addresses the problem of extensive computation required by clustering data into meaningful groups. Preliminary evaluation of this approach in conjunction with K-Means clustering suggests that this approach is at least as good as standard approaches and in some cases better.

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“…TIGR is a well known assembler [19]. FGS is the fuzzy sequence assembly method that is described in [4]. ClusFGS is the method described in this paper and is a modified version of FGS.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…This reduces the number of comparisons and performs meaningful assembly. The fuzzy functions used in this paper are a modified version of the Fuzzy Genome Sequencing Assembler described in [4].…”

Section: Clustering and Assemblymentioning

confidence: 99%

Fuzzy Classification of Genome Sequences Prior to Assembly Based on Similarity Measures

Nasser

Vert

Breland

et al. 2007

NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society

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“…The divideand-conquer strategy uses a fuzzy membership function to divide genome sequences into groups, reducing the number of comparisons and performing meaningful assembly. The fuzzy functions used in this subsection are a modified version of the fuzzy genome sequencing assembler described in [24].…”

Section: Sequence Assemblymentioning

confidence: 99%

“…In this case, the user does not have to preprocess the data, change parameters and run the program several times. The approach starts by acquiring the LCS of given sequence fragments using dynamic programming (details of the fuzzy LCS technique can be found in [24]). The optimal subsequence can be a perfect match, or the user may choose to tolerate indels.…”

Section: Longest Common Subsequence With Fuzzy Logicmentioning

confidence: 99%

Fuzzy Genome Sequence Assembly for Single and Environmental Genomes

Nasser

Breland

Harris

et al. 2009

Fuzzy Systems in Bioinformatics and Computational Biology

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Summary. Traditional methods obtain a microorganism's DNA by culturing it individually. Recent advances in genomics have lead to the procurement of DNA of more than one organism from its natural habitat. Indeed, natural microbial communities are often very complex with tens and hundreds of species. Assembling these genomes is a crucial step irrespective of the method of obtaining the DNA. This chapter presents fuzzy methods for multiple genome sequence assembly of cultured genomes (single organism) and environmental genomes (multiple organisms).An optimal alignment of DNA genome fragments is based on several factors, such as the quality of bases and the length of overlap. Factors such as quality indicate if the data is high quality or an experimental error. We propose a sequence assembly solution based on fuzzy logic, which allows for tolerance of inexactness or errors in fragment matching and that can be used for improved assembly.We propose fuzzy classification using modified fuzzy weighted averages to classify fragments belonging to different organisms within an environmental genome population. Our proposed approach uses DNA-based signatures such as GC content and nucleotide frequencies as features for the classification. This divide-and-conquer strategy also improves performance on larger datasets. We evaluate our method on artificially created environmental genomes to test various combinations of organisms and on an environmental genome.

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“…Fuzzy logic allows tolerance of inexactness or errors in sub sequence matching. Nasser et al [75] propose fuzzy logic for approximate matching of subsequences. Fuzzy characteristic functions are derived for parameters that influence a match.…”

Section: In Multiple Sequence Alignment (Msa)mentioning

confidence: 99%

Computational Intelligence in Solving Bioinformatics Problems: Reviews, Perspectives, and Challenges

Hassanien

Milanova

Smolinski

et al.

Studies in Computational Intelligence

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Summary. This chapter presents a broad overview of Computational Intelligence (CI) techniques including Artificial Neural Networks (ANN), Particle Swarm Optimization (PSO), Genetic Algorithms (GA), Fuzzy Sets (FS), and Rough Sets (RS). We review a number of applications of computational intelligence to problems in bioinformatics and computational biology, including gene expression, gene selection, cancer classification, protein function prediction, multiple sequence alignment, and DNA fragment assembly. We discuss some representative methods to provide inspiring examples to illustrate how CI could be applied to solve bioinformatic problems and how bioinformatics could be analyzed, processed, and characterized by computational intelligence. Challenges to be addressed and future directions of research are presented. An extensive bibliography is also included.

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Multiple Sequence Alignment using Fuzzy Logic

Cited by 16 publications

References 2 publications

Fuzzy Classification of Genome Sequences Prior to Assembly Based on Similarity Measures

Fuzzy Classification of Genome Sequences Prior to Assembly Based on Similarity Measures

Fuzzy Genome Sequence Assembly for Single and Environmental Genomes

Computational Intelligence in Solving Bioinformatics Problems: Reviews, Perspectives, and Challenges

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