A 3D pattern matching algorithm for DNA sequences

Hérisson, Joan; Payen, Guillaume; Gherbi, Rachid

doi:10.1093/bioinformatics/btl669

Cited by 17 publications

(11 citation statements)

References 25 publications

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“…In 2000, Randic et al [8] generalized these 2-D graphical representations to a 3-D graphical representation, in which the center of a cube is chosen as the origin of the Cartesian (x,y,z) coordinate system, and the four corners with coordinates (+1,´1,´1), (´1,+1,´1), (´1,´1,+1), and (+1,+1,+1) are assigned to the four bases. Some other graphical representations of bio-sequences and their applications in the field of biological science and technology can be found in [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Novel Graphical Representation and Numerical Characterization of DNA Sequences

Fei

Zhao

et al. 2016

Applied Sciences

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Abstract:Modern sequencing technique has provided a wealth of data on DNA sequences, which has made the analysis and comparison of sequences a very important but difficult task. In this paper, by regarding the dinucleotide as a 2-combination of the multiset t8¨A, 8¨G, 8¨C, 8¨Tu, a novel 3-D graphical representation of a DNA sequence is proposed, and its projections on planes (x,y), (y,z) and (x,z) are also discussed. In addition, based on the idea of "piecewise function", a cell-based descriptor vector is constructed to numerically characterize the DNA sequence. The utility of our approach is illustrated by the examination of phylogenetic analysis on four datasets.

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Section: Introductionmentioning

confidence: 99%

Novel Graphical Representation and Numerical Characterization of DNA Sequences

Fei

Zhao

et al. 2016

Applied Sciences

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“…[19] proposed a modified BoyerMoore (BM) approach to string matching over LZ78 and LZW compressed text, instead of decompressing and then searching it. Recent research also has addressed work on 3D pattern matching for DNA sequences [20], in which an algorithm, based on 3D trajectories, has been proposed.…”

Section: Related Workmentioning

confidence: 99%

Methodology for Evaluating DNA Pattern Searching Algorithms on Multiprocessor

Soewito

Weng

2007

2007 IEEE 7th International Symposium on BioInformatics and BioEngineering

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Pattern matching has been one of the major operations in modern bioengineering especially in Bioinformatics. Prior work on this area have focus on either pursuing mathematically efficient matching algorithms or hardwired approach. As multicore processor are becoming mainstream, developers need to determine how to take advantage of multicore technology for pattern matching. In this paper, we propose a methodology to evaluate pattern search algorithms for DNA on Multiprocessor. Our evaluation methodology is an automatic simulation framework. Starting from a uniprocessor profiling, the framework constructs task graphs for string matching algorithms. Then task graphs are mapped onto multiprocessor. The system's performance is determined by the analytical performance model. With this framework, we can evaluate the performance of different algorithms on multiprocessor. Our case studies show that finite automaton based (Aho-Corasick) is more efficient than shift table based algorithms (SFKSearch and Wu-Manber) on uniprocessor, however, Wu-Manber is 3 times efficient than Aho-Corasick on multiprocessor due to its inherent parallelism.

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“…A comprehensive example was AND-viewer, which provided a three-dimensional way to dynamically sense the big picture of a large DNA sequence in a virtual reality environment by using sensor, instead of mouse or keyboard. This pioneering effort had made fantastic progress for human to mimic 3D visions to intuitively sense genome sequences [7,8], but still there was no possibility of using the datasets created for visions to further explore real biological contexts.…”

Section: Introductionmentioning

confidence: 99%

GenomeFingerprinter: The Genome Fingerprint and the Universal Genome Fingerprint Analysis for Systematic Comparative Genomics

Meng

et al. 2013

PLoS ONE

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BackgroundNo attention has been paid on comparing a set of genome sequences crossing genetic components and biological categories with far divergence over large size range. We define it as the systematic comparative genomics and aim to develop the methodology.ResultsFirst, we create a method, GenomeFingerprinter, to unambiguously produce a set of three-dimensional coordinates from a sequence, followed by one three-dimensional plot and six two-dimensional trajectory projections, to illustrate the genome fingerprint of a given genome sequence. Second, we develop a set of concepts and tools, and thereby establish a method called the universal genome fingerprint analysis (UGFA). Particularly, we define the total genetic component configuration (TGCC) (including chromosome, plasmid, and phage) for describing a strain as a systematic unit, the universal genome fingerprint map (UGFM) of TGCC for differentiating strains as a universal system, and the systematic comparative genomics (SCG) for comparing a set of genomes crossing genetic components and biological categories. Third, we construct a method of quantitative analysis to compare two genomes by using the outcome dataset of genome fingerprint analysis. Specifically, we define the geometric center and its geometric mean for a given genome fingerprint map, followed by the Euclidean distance, the differentiate rate, and the weighted differentiate rate to quantitatively describe the difference between two genomes of comparison. Moreover, we demonstrate the applications through case studies on various genome sequences, giving tremendous insights into the critical issues in microbial genomics and taxonomy.ConclusionsWe have created a method, GenomeFingerprinter, for rapidly computing, geometrically visualizing, intuitively comparing a set of genomes at genome fingerprint level, and hence established a method called the universal genome fingerprint analysis, as well as developed a method of quantitative analysis of the outcome dataset. These have set up the methodology of systematic comparative genomics based on the genome fingerprint analysis.

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A 3D pattern matching algorithm for DNA sequences

Abstract: Available on request from the authors.

Cited by 17 publications

References 25 publications

Novel Graphical Representation and Numerical Characterization of DNA Sequences

Novel Graphical Representation and Numerical Characterization of DNA Sequences

Methodology for Evaluating DNA Pattern Searching Algorithms on Multiprocessor

GenomeFingerprinter: The Genome Fingerprint and the Universal Genome Fingerprint Analysis for Systematic Comparative Genomics

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