Use of image texture analysis to find DNA sequence similarities

Chen, Weiyang; Liao, Bo; Li, Weiwei

doi:10.1016/j.jtbi.2018.07.001

Cited by 15 publications

(5 citation statements)

References 27 publications

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“…A pyrimidine replaced by a purine, or a purine replaced by a pyrimidine is a transversion mutation. Transition mutations are far more prevalent than transversion mutations [37].…”

Section: Methodsmentioning

confidence: 99%

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A new feature vector model for alignment-free DNA sequence similarity analysis

DELIBAŞ¹

2022

Sigma J Eng Nat Sci - Sigma Müh Fen Bil Derg

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“…A pyrimidine replaced by a purine, or a purine replaced by a pyrimidine is a transversion mutation. Transition mutations are far more prevalent than transversion mutations [37].…”

Section: Methodsmentioning

confidence: 99%

“…The sequence lengths between 893 to 896 base pairs. They were previously investigated and reported by Hayasaka et al [16] and subsequently used by Zhang [32,39], Qi et al [25], Chen et al [37], and Delibas et al [40].…”

Section: Nadh Dehydrogenase Subunit 4 Genesmentioning

confidence: 99%

A new feature vector model for alignment-free DNA sequence similarity analysis

DELIBAŞ¹

2022

Sigma J Eng Nat Sci - Sigma Müh Fen Bil Derg

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“…Chen et al [ 7 ] developed a method for phylogeny analysis where they converted a DNA sequence to a digital vector by assigning ( ) and combined it with index information. After that, a gray level co-occurrence matrix (GLCM) was calculated from the vector.…”

Section: Background Studymentioning

confidence: 99%

“…Different AF-based researches have been conducted on sequence similarity analysis. Among them, pattern histogram [23], suffix tree count [18], k − mer encoding-based image analysis [6][7][8], chaos game representation (CGR) approach [2,30,38], convolutional neural network (CNN) approach using CGR image [30] are extensively used in different studies. We discuss different AF models, their strengths and limitations in the second section.…”

Section: Introductionmentioning

confidence: 99%

A fast and efficient algorithm for DNA sequence similarity identification

Uddin

Islam

Hassan

et al. 2022

Complex Intell. Syst.

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DNA sequence similarity analysis is necessary for enormous purposes including genome analysis, extracting biological information, finding the evolutionary relationship of species. There are two types of sequence analysis which are alignment-based (AB) and alignment-free (AF). AB is effective for small homologous sequences but becomes NP-hard problem for long sequences. However, AF algorithms can solve the major limitations of AB. But most of the existing AF methods show high time complexity and memory consumption, less precision, and less performance on benchmark datasets. To minimize these limitations, we develop an AF algorithm using a 2D $$k-mer$$ k - m e r count matrix inspired by the CGR approach. Then we shrink the matrix by analyzing the neighbors and then measure similarities using the best combinations of pairwise distance (PD) and phylogenetic tree methods. We also dynamically choose the value of k for $$k-mer$$ k - m e r . We develop an efficient system for finding the positions of $$k-mer$$ k - m e r in the count matrix. We apply our system in six different datasets. We achieve the top rank for two benchmark datasets from AFproject, 100% accuracy for two datasets (16 S Ribosomal, 18 Eutherian), and achieve a milestone for time complexity and memory consumption in comparison to the existing study datasets (HEV, HIV-1). Therefore, the comparative results of the benchmark datasets and existing studies demonstrate that our method is highly effective, efficient, and accurate. Thus, our method can be used with the top level of authenticity for DNA sequence similarity measurement.

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“…Specifically, DNA/RNA sequences are converted into realvalued vectors, and then the similarity score between these vectors is calculated for sequence similarity measurement. At present, graphics-based methods [26]–[28], chaos theory-based methods [29], [30], word frequency-based methods [9]–[12], and etc, have been used for sequence vector representations. Although the alignment-free approaches above sacrifice accuracy in exchange for speed improvement, their disadvantages are also obvious that their accuracy of sequence similarity comparison drops significantly in the big data scenario.…”

Section: Related Workmentioning

confidence: 99%

Kmer-Node2Vec: a Fast and Efficient Method for Kmer Embedding from the Kmer Co-occurrence Graph, with Applications to DNA Sequences

Yang

Lan

et al. 2022

Preprint

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Current alignment-free DNA sequence comparison tools, such as DNA2Vec and Seq2Vec, have become ubiquitous and are widely used in the computational biology. However, these approaches do not widely apply to the large-scale DNA sequential retrieval due to their extremely long training time. This paper proposes a graph-based DNA embedding algorithm, called KMer-Node2Vec, which converts the large DNA corpus into a k-mer co-occurrence graph, then takes the k-mer sequence samples from this graph by randomly traveling and finally trains the k-mer embedding on this sampling corpus. We posit that because of the stable size of k-mers, the size and density of the k-mer co-occurrence graph change slightly with the increase in the training corpus. Therefore, KMer-Node2Vec has a stable runtime on the large-scale data set, and its performance advantage becomes more and more obvious with the growth of the training corpus. Extensive experiments conducted on real world data sets demonstrate that KMer-Node2Vec outperforms DNA2Vec by 29 times in terms of efficiency in 4G data set, while the error generated by the random walk sampling is small.

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Use of image texture analysis to find DNA sequence similarities

Cited by 15 publications

References 27 publications

A new feature vector model for alignment-free DNA sequence similarity analysis

A new feature vector model for alignment-free DNA sequence similarity analysis

A fast and efficient algorithm for DNA sequence similarity identification

Kmer-Node2Vec: a Fast and Efficient Method for Kmer Embedding from the Kmer Co-occurrence Graph, with Applications to DNA Sequences

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