A new method to cluster DNA sequences using Fourier power spectrum

Hoang, Tung T.; Yin, Changchuan; Zheng, Hui; Yu, Chenglong; He, Rong; Yau, Stephen S.-T.

doi:10.1016/j.jtbi.2015.02.026

Cited by 72 publications

(77 citation statements)

References 34 publications

(39 reference statements)

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“…Besides the graphical representations, researchers try to combine some techniques from other disciplines into the study of genes and have proposed novel methods. For example, the Discrete Fourier Transform, which is broadly applied in signal process, has been introduced into the process of genes [15,16]. It is proved effective in the analysis of DNA sequences.…”

Section: Introductionmentioning

confidence: 99%

A new method to analyze protein sequence similarity using Dynamic Time Warping

Hou

Pan

Peng³

et al. 2017

Genomics

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Sequences similarity analysis is one of the major topics in bioinformatics. It helps researchers to reveal evolution relationships of different species. In this paper, we outline a new method to analyze the similarity of proteins by Discrete Fourier Transform (DFT) and Dynamic Time Warping (DTW). The original symbol sequences are converted to numerical sequences according to their physico-chemical properties. We obtain the power spectra of sequences from DFT and extend the spectra to the same length to calculate the distance between different sequences by DTW. Our method is tested in different datasets and the results are compared with that of other software algorithms. In the comparison we find our scheme could amend some wrong classifications appear in other software. The comparison shows our approach is reasonable and effective.

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Section: Introductionmentioning

confidence: 99%

A new method to analyze protein sequence similarity using Dynamic Time Warping

Hou

Pan

Peng³

et al. 2017

Genomics

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“…Now we want to analyze 31 mammalian mitochondrial genomes and construct a phylogenetic tree. The GenBank information of these genomes can be found in [32], and the results with UPGMA are shown in Figure 2 also closing similar. Our results are also consistent with that in [28], where they considered 11 species of them.…”

Section: Resultsmentioning

confidence: 56%

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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“…Adjacency matrices are also introduced in some articles [18][19][20][21], where an exact solution is obtained to the protein alignment problem. Additional methods use discrete Fourier transform (DFT) in which DNA sequences are mapped into four binary indicator sequences, followed by the application of DFT on these indicator sequences to transform them into a frequency domain [22,23]. Dynamic representation is used to remove degeneracies in the previously mentioned approaches [24][25][26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Measuring Similarity among Protein Sequences Using a New Descriptor

Abo-Elkhier

Elwahaab

Maaty

2019

BioMed Research International

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The comparison of protein sequences according to similarity is a fundamental aspect of today's biomedical research. With the developments of sequencing technologies, a large number of protein sequences increase exponentially in the public databases. Famous sequences' comparison methods are alignment based. They generally give excellent results when the sequences under study are closely related and they are time consuming. Herein, a new alignment-free method is introduced. Our technique depends on a new graphical representation and descriptor. The graphical representation of protein sequence is a simple way to visualize protein sequences. The descriptor compresses the primary sequence into a single vector composed of only two values. Our approach gives good results with both short and long sequences within a little computation time. It is applied on nine beta globin, nine ND5 (NADH dehydrogenase subunit 5), and 24 spike protein sequences. Correlation and significance analyses are also introduced to compare our similarity/dissimilarity results with others' approaches, results, and sequence homology.

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A new method to cluster DNA sequences using Fourier power spectrum

Cited by 72 publications

References 34 publications

A new method to analyze protein sequence similarity using Dynamic Time Warping

A new method to analyze protein sequence similarity using Dynamic Time Warping

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

Measuring Similarity among Protein Sequences Using a New Descriptor

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