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

Kobori, Yusei; Mizuta, Satoshi

doi:10.1016/j.gpb.2015.09.007

Cited by 7 publications

(6 citation statements)

References 18 publications

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“…Graphical representation is Figure 13. Phylogenetic tree of 31 mammalian species reconstructed by Kobori's method [41] using UPGMA based on the histogram intersection distance measure. The tree is generated by statistical analysis software R with package "ape".…”

Section: Resultsmentioning

confidence: 99%

“…The assignments of this type including the variations with some modifications are utilized in Refs. [5,6,10,16,20,21,40,41].…”

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

confidence: 99%

“…The author and co-workers recently published the paper about a novel two-dimensional graphical representation of DNA sequences based on binary images [41]. In this section, we introduce our method and demonstrate the notable effects of weighting for the construction of the graphical representations introduced first by the author and co-workers [40].…”

Section: Graphical Representation Based On Binary Imagesmentioning

confidence: 99%

“…The self-information is defined by Figure 9. Two-dimensional vector assignment to four bases utilized by Kobori and Mizuta [41].…”

Section: Introducing Weightingmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

“…The assignments of this type including the variations with some modifications are utilized in Refs. [5,6,10,16,20,21,40,41].…”

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

confidence: 99%

Section: Graphical Representation Based On Binary Imagesmentioning

confidence: 99%

“…The self-information is defined by Figure 9. Two-dimensional vector assignment to four bases utilized by Kobori and Mizuta [41].…”

Section: Introducing Weightingmentioning

confidence: 99%

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Graphical Representation of Biological Sequences

Mizuta¹

2018

Bioinformatics in the Era of Post Genomics and Big Data

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show abstract

“…However, it offers various variations as follows; the earlier model of DNA-walk uses four main directions (i.e. west, east, north, and south) for representing each nucleotide, and hence a DNA (or RNA) sequence is plotted by consequent unit vectors in these directions [32,35,38]. As its main drawback, overlapping and crossing of the curves, representing DNA segments, cause information loss.…”

Section: Plos Onementioning

confidence: 99%

WalkIm: Compact image-based encoding for high-performance classification of biological sequences using simple tuning-free CNNs

2022

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The classification of biological sequences is an open issue for a variety of data sets, such as viral and metagenomics sequences. Therefore, many studies utilize neural network tools, as the well-known methods in this field, and focus on designing customized network structures. However, a few works focus on more effective factors, such as input encoding method or implementation technology, to address accuracy and efficiency issues in this area. Therefore, in this work, we propose an image-based encoding method, called as WalkIm, whose adoption, even in a simple neural network, provides competitive accuracy and superior efficiency, compared to the existing classification methods (e.g. VGDC, CASTOR, and DLM-CNN) for a variety of biological sequences. Using WalkIm for classifying various data sets (i.e. viruses whole-genome data, metagenomics read data, and metabarcoding data), it achieves the same performance as the existing methods, with no enforcement of parameter initialization or network architecture adjustment for each data set. It is worth noting that even in the case of classifying high-mutant data sets, such as Coronaviruses, it achieves almost 100% accuracy for classifying its various types. In addition, WalkIm achieves high-speed convergence during network training, as well as reduction of network complexity. Therefore WalkIm method enables us to execute the classifying neural networks on a normal desktop system in a short time interval. Moreover, we addressed the compatibility of WalkIm encoding method with free-space optical processing technology. Taking advantages of optical implementation of convolutional layers, we illustrated that the training time can be reduced by up to 500 time. In addition to all aforementioned advantages, this encoding method preserves the structure of generated images in various modes of sequence transformation, such as reverse complement, complement, and reverse modes.

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Ranking and clustering of Drosophila olfactory receptors using mathematical morphology

et al. 2019

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This article introduces an alignment-free clustering method in order to cluster all the 66 DORs sequentially diverse protein sequences. Two different methods are discussed: one is utilizing twenty standard amino acids (without grouping) and another one is using chemical grouping of amino acids (with grouping). Two grayscale images (representing two protein sequences by order pair frequency matrices) are compared to find the similarity index using morphology technique. We could achieve the correlation coefficients of 0.9734 and 0.9403 for without and with grouping methods respectively with the ClustalW result in the ND5 dataset, which are much better than some of the existing alignment-free methods. Based on the similarity index, the 66 DORs are clustered into three classes - Highest, Moderate and Lowest - which are seen to be best fitted for 66 DORs protein sequences. OR83b is the distinguished olfactory receptor expressed in divergent insect population which is substantiated through our investigation.

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

Cited by 7 publications

References 18 publications

Graphical Representation of Biological Sequences

Graphical Representation of Biological Sequences

WalkIm: Compact image-based encoding for high-performance classification of biological sequences using simple tuning-free CNNs

Ranking and clustering of Drosophila olfactory receptors using mathematical morphology

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