Alignment-free comparison of genome sequences by a new numerical characterization

Huang, Guohua; Zhou, Houqing; Li, Yongfan; Xu, Lixin

doi:10.1016/j.jtbi.2011.04.003

Cited by 32 publications

(27 citation statements)

References 28 publications

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“…We compared our tree constructed by HI and MD (the top panel of Fig. 7) with those given by Huang et al 21 and Yu et al 53 Overall configuration of our tree approximately agrees with their trees except for the position of hedgehog. Krettek et al…”

supporting

confidence: 68%

“…We compared our phylogenetic trees with that constructed by ClustalW for mitochondrial genomes of 31 mammalian species. Among the five distance measures, histogram intersection and Manhattan distance showed the best performance in terms of Robinson-Foulds distance between the phylogenetic trees, as well as having the more appropriate position of hedgehog on the phylogenetic tree than those given by Huang et al 21 and Yu et al 53 . The most time consuming step in our method is counting the occurrence frequencies of local patterns.…”

Section: Resultsmentioning

confidence: 90%

“…So far various methods based on the graphical representation have been introduced by many authors. The basic procedure is common to almost all the methods, which is outlined as follows: first, each type of bases in a DNA sequence is replaced by an individual vector on a certain dimensional expression space, two-dimension [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] , three-dimension [23][24][25][26][27][28][29][30][31][32][33][34][35][36] , or more [37][38][39][40] ; next, the vectors are connected successively, drawing a trajectory on the expression space; lastly, the distances between the trajectories, or graphs, are calculated based on a pre-defined distance measure.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Similarity Estimation Between DNA Sequences Based on Local Pattern Histograms of Binary Images

Kobori

Mizuta

2015

Preprint

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Graphical representation of DNA sequences is one of the most popular techniques of alignment-free sequence comparison. In this article, we propose a new method for extracting features of DNA sequences represented by binary images, in which we estimate the similarity between DNA sequences by the frequency histograms of local bitmap patterns on the images. Our method has linear time complexity for the length of DNA sequences, which is practical even for comparison of long sequences. We tested five distance measures to estimate sequence similarities and found that histogram intersection and Manhattan distance are most appropriate for our method among them.

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supporting

confidence: 68%

Section: Resultsmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Similarity Estimation Between DNA Sequences Based on Local Pattern Histograms of Binary Images

Kobori

Mizuta

2015

Preprint

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“…The assignments of Yau's type (including the variations with some modifications) are utilized in Refs. [9,12,15,18,19,[37][38][39].…”

Section: Bioinformatics In the Era Of Post Genomics And Big Datamentioning

confidence: 99%

Graphical Representation of Biological Sequences

Mizuta¹

2018

Bioinformatics in the Era of Post Genomics and Big Data

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Sequence comparison is one of the most fundamental tasks in bioinformatics. For biological sequence comparison, alignment is the most profitable method when the sequence lengths are not so large. However, as the time complexity of the alignment is the square order of the sequence length, the alignment requires a large amount of computational time for comparison of sequences of large size. Therefore, so-called alignment-free sequence comparison methods are needed for comparison between such as whole genome sequences in practical time. In this chapter, we reviewed the graphical representation of biological sequences, which is one of the major alignment-free sequence comparison methods. The notable effects of weighting during the course of the graphical representation introduced first by the author and co-workers were also mentioned.

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“…BLAST: (Basic Local Alignment Search Tool) [4] is an example of a larger scale. Most of these algorithms uses SmithWaterman algorithm for performing sequence alignment [6][7][8][9][10][21][22][23][24][25]. This algorithm which is also used in crimes' forensic investigation does not use full DNA to DNA sequence comparison.…”

Section: Introductionmentioning

confidence: 99%

Locating All Common Subsequences in Two DNA Sequences

Khalil¹

2016

IJITCS

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Abstract-Biological sequence comparison is one of the most important and basic problems in computational biology. Due to its high demands for computational power and memory, it is a very challenging task. The well-known algorithm proposed by Smith-Waterman obtains the best local alignments at the expense of very high computing power and huge memory requirements. This paper introduces a new efficient algorithm to locate the longest common subsequences (LCS) in two different DNA sequences. It is based on the convolution between the two DNA sequences: The major sequence is represented in the linked-list X while the minor one is represented in circular linked-list Y. An array of linked lists is established where each linked list is corresponding to an element of the linked-list X and a new node is added to it for each match between the two sequences. If two or more matches in different locations in string Y share the same location in string X, the corresponding nodes will construct a unique linked-list. Accordingly, by the end of processing, we obtain a group of linked-lists containing nodes that reflect all possible matches between the two sequences X and Y. The proposed algorithm has been implemented and tested using C# language. The benchmark test shows very good speedups and indicated that impressive improvements has been achieved.

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Alignment-free comparison of genome sequences by a new numerical characterization

Cited by 32 publications

References 28 publications

Similarity Estimation Between DNA Sequences Based on Local Pattern Histograms of Binary Images

Similarity Estimation Between DNA Sequences Based on Local Pattern Histograms of Binary Images

Graphical Representation of Biological Sequences

Locating All Common Subsequences in Two DNA Sequences

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