A novel numerical mapping method based on entropy for digitizing DNA sequences

Daş, Bihter; Türkoğlu, İ̇brahim

doi:10.1007/s00521-017-2871-5

Cited by 31 publications

(24 citation statements)

References 31 publications

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“…It consists of 86 exons and 86 introns which are 297 bases length. First of all, we used the Entropy-based Numerical Mapping Technique to digitize DNA sequences [16,17]. The performance of this technique was compared with other two existing mapping techniques (e.g., EIIP, Integer)…”

Section: Datasetmentioning

confidence: 99%

“…In this technique, fractional Shannon entropy is used to convert DNA sequences into numerical values. The entropy of codons' distribution in a DNA sequence is computed [16,18]. There are 64 possible codon states in a DNA sequence.…”

Section: Digitization Of the Datamentioning

confidence: 99%

“…Spectrum images were obtained to extract features from each of 297 bases length exon and intron sequences. While obtaining these spectrum images, Hamming window width as 16…”

Section: Spectrum Images Composingmentioning

confidence: 99%

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A novel genome analysis method with the entropy-based numerical technique using pretrained convolutional neural networks

Daş¹,

Toraman²,

Türkoğlu³

2020

Turk J Elec Eng & Comp Sci

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The identification of DNA sequences as exon and intron is a common problem in genome analysis. The methods used for feature extraction and mapping techniques for the digitization of sequences affect directly the solution of this problem. The existing mapping techniques are not enough to detect coding and noncoding regions in some genomes because the digital representation of each base in a DNA sequence with an integer does not fully reflect the structure of an original DNA sequence. In the entropy-based mapping technique, we could overcome this problem because the technique deepens distinction rates of exon regions, and better reflects the complexity of DNA sequences. Moreover, in the literature, features are extracted by using various statistical techniques. The statistical features to be extracted are chosen by a system designer's experience. The other proposed approach in this study is to carry out the feature extraction using the transfer learning method. Transfer learning and feature extraction are performed automatically by convolutional neural network models as independent of the data set. In this study, we propose a new method to classify DNA sequences as exon and intron using two approaches. In the first approach, the entropy-based numerical technique was used for the numerical representation of DNA sequences. In the second approach, transfer learning was used to extract features. Then, the obtained features were classified by support vector machine and k -nearest neighbors algorithm. As a result of the classification, accurate performance with 97.8% was achieved. The performance of the current method was compared with the other numerical mapping techniques and feature extraction methods. The results showed that the developed method was much more successful than other methods.

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Section: Datasetmentioning

confidence: 99%

Section: Digitization Of the Datamentioning

confidence: 99%

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A novel genome analysis method with the entropy-based numerical technique using pretrained convolutional neural networks

Daş¹,

Toraman²,

Türkoğlu³

2020

Turk J Elec Eng & Comp Sci

Self Cite

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“…Furthermore, there are representations suitable for DNA and RNA [ 11 ], codons [ 3 , 5 , 12 ], and protein [ [13] , [14] , [15] ] sequences. In addition, many numerical representations utilize only a part of the genetic or biochemical information carried by the sequence [ 12 , [16] , [17] , [18] , [19] ] and thus their classification is ambiguous. In general, the standardization of numerical representations requirements is missing, e.g.…”

Section: Introductionmentioning

confidence: 99%

A degeneration-reducing criterion for optimal digital mapping of genetic codes

Škutková

Maderankova

Sedlář

et al. 2019

Computational and Structural Biotechnology Journal

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Bioinformatics may seem to be a scientific field processing primarily large string datasets, as nucleotides and amino acids are represented with dedicated characters. On the other hand, many computational tasks that bioinformatics challenges are mathematical problems understandable as operations with digits. In fact, many computational tasks are solved this way in the background. One of the most widely used digital representations is mapping of nucleotides and amino acids with integers 0–3 and 0–20, respectively. The limitation of this mapping occurs when the digital signal of nucleotides has to be translated into a digital signal of amino acids as the genetic code is degenerated. This causes non-monotonies in a mapping function. Although map for reducing this undesirable effect has already been proposed, it is defined theoretically and for standard genetic codes only. In this study, we derived a novel optimal criterion for reducing the influence of degeneration by utilizing a large dataset of real sequences with various genetic codes. As a result, we proposed a new robust global optimal map suitable for any genetic code as well as specialized optimal maps for particular genetic codes.

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“…GSP methods require the transformation or mapping of the biological sequences, usually represented as a string of characters (i.e., A, T, G and C) to a numeric representation (i.e., a signal) that can be processed using mathematical functions (Kwan and Arniker, 2009). Examples of the use of GSP methods include the identification of protein-coding regions in DNA sequences (Das and Turkoglu, 2017;Mabrouk, 2017;Das and Turkoglu, 2015;Inbamalar and Sivakumar, 2012;Marhon and Kremer, 2011;Akhtar et al, 2008Akhtar et al, , 2007Rushdi and Tuqan, 2006;Yin and Yau, 2005;Kotlar, 2003;Anastassiou, 2000), finding for genomic repeats (Sharma et al, 2004), determining the structural, thermodynamic, and bending properties of DNA (Gabrielian and Pongor, 1996), biological sequence querying (Ravichandran et al, 2010), estimating of DNA sequence similarity (Mendizabal-Ruiz et al, 2017;Hoang et al, 2016;Yin et al, 2014;Borrayo et al, 2014;Cheever et al, 1989), and sequence alignment (Skutkova et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Peer Review #1 of "Genomic signal processing for DNA sequence clustering (v0.2)"

2018

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Genomic signal processing (GSP) methods which convert DNA data to numerical values have recently been proposed, which would offer the opportunity of employing existing digital signal processing methods for genomic data. One of the most used methods for exploring data is cluster analysis which refers to the unsupervised classification of patterns in data. In this paper, we propose a novel approach for performing cluster analysis of DNA sequences that is based on the use of GSP methods and the K-means algorithm. We also propose a visualization method that facilitates the easy inspection and analysis of the results and possible hidden behaviors. Our results support the feasibility of employing the proposed method to find and easily visualize interesting features of sets of DNA data.

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A novel numerical mapping method based on entropy for digitizing DNA sequences

Cited by 31 publications

References 31 publications

A novel genome analysis method with the entropy-based numerical technique using pretrained convolutional neural networks

A novel genome analysis method with the entropy-based numerical technique using pretrained convolutional neural networks

A degeneration-reducing criterion for optimal digital mapping of genetic codes

Peer Review #1 of "Genomic signal processing for DNA sequence clustering (v0.2)"

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