Similarity/dissimilarity calculation methods of DNA sequences: A survey

Jin, Xin; Jiang, Qian; Chen, Yanyan; Lee, Shin-Jye; Nie, Rencan; Yao, Shaowen; Zhou, Dongming; He, Kangjian

doi:10.1016/j.jmgm.2017.07.019

Cited by 28 publications

(21 citation statements)

References 49 publications

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“…Let us first consider the sequences of beta-globin gene, whose information is listed in Table 2 from GenBank, which updates the information of Table 3 in [1]. The result is shown in Table 3.…”

Section: Resultsmentioning

confidence: 99%

A New Numerical Method for DNA Sequence Analysis Based on 8-Dimensional Vector Representation

Zhang¹

2019

JAMP

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Background: The multiple sequence alignment (MSA) algorithms are the traditional ways to compare and analyze DNA sequences. However, for large DNA sequences, these algorithms require a long time computationally. Objective: Here we will propose a new numerical method to characterize and compare DNA sequences quickly. Method: Based on a new 2-dimensional (2D) graphical representation of DNA sequences, we can obtain an 8-dimensional vector using two basic concepts of probability, the mean and the variance. Results: We perform similarity/dissimilarity analyses among two real DNA data sets, the coding sequences of the first exon of beta-globin gene of 11 species and 31 mammalian mitochondrial genomes, respectively. Conclusion: Our results are in agreement with the existing analyses in our literatures. We also compare our approach with other methods and find that ours is more effective. IntroductionWith the rapid growth in biological data, how to get more information from these big data is a challenge for scientists. For this purpose, an important problem is to find a suitable way to digitize these DNA sequences so that the sequence comparison can be applied. For computational time reason, beyond the traditional multiple sequence alignment (MSA), many alignment-free sequence comparison methods were introduced, for more details, please refer to [1] [2] [3] and the references therein.

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Section: Resultsmentioning

confidence: 99%

A New Numerical Method for DNA Sequence Analysis Based on 8-Dimensional Vector Representation

Zhang¹

2019

JAMP

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“…Now we use this method to study the similarities among the coding sequences of the first exon of beta-globin genes of eleven species based on the data information listed in (JIN et al, 2017) from GenBank. The result is shown in Table 2.…”

Section: Resultsmentioning

confidence: 99%

2D graphical representation of dna sequence based on horizon lines from a probabilistic view

Liu¹

2018

Biosci. J.

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ABSTRACT:In this study, we propose a new two-dimensional graphical representation of DNA sequence based on a choice of four horizon lines. The 2D representation is constructed in a probabilistic framework. Following the new approach, we perform the similarity analysis among coding sequences of the first exon of beta-globin gene from eleven species. Our results coincide with current biological analyses. We also compare our method with some existing DNA sequence comparison algorithms and find that ours is more intuitive and effective.

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“…A similarity/dissimilarity vector is introduced instead of the regular similarity/dissimilarity matrix [10,11]. The similarity/dissimilarity matrix is a square symmetric matrix with zeros in its main diagonal.…”

Section: Similarity/dissimilarity Analysismentioning

confidence: 99%

“…Many graphical representations of DNA and protein primary sequences have been proposed. Some other approaches characterize numerically protein sequences without previous graphical representation and nongraphical representation methods [10,11].…”

Section: Introductionmentioning

confidence: 99%

A Statistical Similarity/Dissimilarity Analysis of Protein Sequences Based on a Novel Group Representative Vector

Elwahaab

Abo-Elkhier

Maaty

2019

BioMed Research International

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Similarity/dissimilarity analysis is a key way of understanding the biology of an organism by knowing the origin of the new genes/sequences. Sequence data are grouped in terms of biological relationships. The number of sequences related to any group is susceptible to be increased every day. All the present alignment-free methods approve the utility of their approaches by producing a similarity/dissimilarity matrix. Although this matrix is clear, it measures the degree of similarity among sequences individually. In our work, a representative of each of three groups of protein sequences is introduced. A similarity/dissimilarity vector is evaluated instead of the ordinary similarity/dissimilarity matrix based on the group representative. The approach is applied on three selected groups of protein sequences: beta globin, NADH dehydrogenase subunit 5 (ND5), and spike protein sequences. A cross-grouping comparison is produced to ensure the singularity of each group. A qualitative comparison between our approach, previous articles, and the phylogenetic tree of these protein sequences proved the utility of our approach.

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Similarity/dissimilarity calculation methods of DNA sequences: A survey

Cited by 28 publications

References 49 publications

A New Numerical Method for DNA Sequence Analysis Based on 8-Dimensional Vector Representation

A New Numerical Method for DNA Sequence Analysis Based on 8-Dimensional Vector Representation

2D graphical representation of dna sequence based on horizon lines from a probabilistic view

A Statistical Similarity/Dissimilarity Analysis of Protein Sequences Based on a Novel Group Representative Vector

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