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

Hou, Wenbing; Pan, Qiuhui; Peng, Qianying; He, Mingfeng

doi:10.1016/j.ygeno.2016.12.002

Cited by 30 publications

(6 citation statements)

References 38 publications

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“…Phylogenetic tree generated by our method (Fig. 5) is consistent with phylogenetic trees generated in the previous studies 42,50,51 and alignment based method ClustalW using MEGA package 25 (Fig. S5).…”

Section: Resultssupporting

confidence: 84%

Alignment-free similarity analysis for protein sequences based on fuzzy integral

Saw

Tripathy

Nandi

2019

Sci Rep

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Sequence comparison is an essential part of modern molecular biology research. In this study, we estimated the parameters of Markov chain by considering the frequencies of occurrence of the all possible amino acid pairs from each alignment-free protein sequence. These estimated Markov chain parameters were used to calculate similarity between two protein sequences based on a fuzzy integral algorithm. For validation, our result was compared with both alignment-based (ClustalW) and alignment-free methods on six benchmark datasets. The results indicate that our developed algorithm has a better clustering performance for protein sequence comparison.

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Section: Resultssupporting

confidence: 84%

Alignment-free similarity analysis for protein sequences based on fuzzy integral

Saw

Tripathy

Nandi

2019

Sci Rep

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“…The digital-signal-based representation encodes a single amino acid (AA) into a number so that a protein sequence is converted into a digital signal sequence, which is processed by digital signal analysis tools to extract the features of the protein sequence. For example, in a study performed by Hou et al (2017) , protein sequence was converted into numerical sequences with their physicochemical properties to achieve the power spectra by Discrete Fourier Transform (DFT). Furthermore, Dynamic Time Warping (DTW) was used to extend the spectra to the same length in order to calculate the distance between different sequences.…”

Section: Introductionmentioning

confidence: 99%

A novel model for protein sequence similarity analysis based on spectral radius

Gao

Marinis

et al. 2018

Journal of Theoretical Biology

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Advances in sequencing technologies led to rapid increase in the number and diversity of biological sequences, which facilitated development in the sequence research. In this paper, we present a new method for analyzing protein sequence similarity. We calculated the spectral radii of 20 amino acids (AAs) and put forward a novel 2-D graphical representation of protein sequences. To characterize protein sequences numerically, three groups of features were extracted and related to statistical, dynamics measurements and fluctuation complexity of the sequences. With the obtained feature vector, two models utilizing Gaussian Kernel similarity and Cosine similarity were built to measure the similarity between sequences. We applied our method to analyze the similarities/dissimilarities of four data sets. Both proposed models received consistent results with improvements when compared to that obtained by the ClustalW analysis. The novel approach we present in this study may therefore benefit protein research in medical and scientific fields.

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“…Their range of lengths is from 602 to 610. This sample set is applied before in [12][13][14][15][16][17][18][19][20][21][22][23][24][25]. Table 3 shows the 3 rd sample set which consists of 29 spike protein sequences.…”

Section: Dataset Technology and Toolsmentioning

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

Cited by 30 publications

References 38 publications

Alignment-free similarity analysis for protein sequences based on fuzzy integral

Alignment-free similarity analysis for protein sequences based on fuzzy integral

A novel model for protein sequence similarity analysis based on spectral radius

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

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