Feature based quality assessment of DNA sequencing chromatograms

Öz, Ersoy; Kurt, Serkan; Asyalı, Musa Hakan; Kaya, Huseyin; Yücel, Yeliz

doi:10.1016/j.asoc.2016.01.025

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…Especially, for DNA sequencing data, a very common and important challenge is discriminating the genes belonging different classes since distinguishing the signals by visually is almost impossible. ML methods present promising performances in various tasks such as recognition and categorization to overcome this challenge as shown in many important studies [6,[60][61][62][63][64][65][66][67][68].…”

Section: Discussionmentioning

confidence: 99%

Feature extraction for DNA capillary electrophesis signals based on discrete wavelet transform combined with multi-scale permutation entropy

YİĞİT¹

2022

Sigma J Eng Nat Sci - Sigma Müh Fen Bil Derg

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DNA sequence classification is an important challenge in genomic studies due to non-linear and chaotic behavior of DNA oxidation signals of Adenine, Cytosine, Guanine, and Thymine bases. To achieve genotype identification of samples derived from biological sources accurately, Machine Learning (ML) methods have been commonly preferred instead of expert-based methods due to the ability in handling such these complex-structured biological sequences. Reducing the dimension without sacrificing important information that should not be omitted during the classification process is an important task in ML applications. This st udy presents a new feature extraction method to detect two sub-types of hepatitis nucleic acid trace files. The proposed method combines both discrete wavelet transform (DWT) and entropy. The DWT decomposes the bases signals up to three levels and thus all necessary information that is hidden in both spatial and frequency domains is aimed to captured. To achieve a good summarization of DNA trace files having different length, multi-scale permutation entropy (MPE) measures are then computed from approximate and detail coefficients o f signals s tored in the s ub-bands. Different feature sets are extracted with the proposed method using real data covering 200 hepatitis DNA trace files and then fed to a simple memory-based learning classifier, k-NN. The classification performance of the proposed feature extraction method is compared against a method based on MPE features without wavelet decomposition. The results indicate, in classifying hepatitis DNA trace files, the average accuracy reaches up to nearly 99% with feature sets based on proposed method even at 30% training samples proportion. Cite this article as: Yiğit ÖE, Öz E. Feature extraction for DNA capillary electrophesis signals based on discrete wavelet transform combined with multi-scale permutation entropy. Sigma

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Section: Discussionmentioning

confidence: 99%

Feature extraction for DNA capillary electrophesis signals based on discrete wavelet transform combined with multi-scale permutation entropy

YİĞİT¹

2022

Sigma J Eng Nat Sci - Sigma Müh Fen Bil Derg

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“…Three statistical features based on descriptive statistical theory, including central tendency measures (mean and median) and a central dispersion measure (standard deviation), are used. These are frequently used statistics that reflect the property of DNA trace files [ 26 , 27 ].…”

Section: Methodsmentioning

confidence: 99%

“…However, there is no work available that uses entropy-based feature extraction methods for DNA trace files, especially for hepatitis DNA trace files. On the other hand, sophisticated classifiers within the concept of machine learning have been investigated in terms of their classification ability in the studies of DNA sequencing [ 25 , 26 , 27 , 28 ]. However, SVM [ 29 , 30 ] has been reported as a powerful classification tool compared with other supervised algorithms in recent years [ 31 ], and to the best our knowledge, none of the hepatitis DNA studies have examined SVM as a classifier.…”

Section: Introductionmentioning

confidence: 99%

Classification of Hepatitis Viruses from Sequencing Chromatograms Using Multiscale Permutation Entropy and Support Vector Machines

Öz

Aşkın

2019

Entropy

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Classifying nucleic acid trace files is an important issue in molecular biology researches. For the purpose of obtaining better classification performance, the question of which features are used and what classifier is implemented to best represent the properties of nucleic acid trace files plays a vital role. In this study, different feature extraction methods based on statistical and entropy theory are utilized to discriminate deoxyribonucleic acid chromatograms, and distinguishing their signals visually is almost impossible. Extracted features are used as the input feature set for the classifiers of Support Vector Machines (SVM) with different kernel functions. The proposed framework is applied to a total number of 200 hepatitis nucleic acid trace files which consist of Hepatitis B Virus (HBV) and Hepatitis C Virus (HCV). While the use of statistical-based feature extraction methods allows representing the properties of hepatitis nucleic acid trace files with descriptive measures such as mean, median and standard deviation, entropy-based feature extraction methods including permutation entropy and multiscale permutation entropy enable quantifying the complexity of these files. The results indicate that using statistical and entropy-based features produces exceptionally high performances in terms of accuracies (reached at nearly 99%) in classifying HBV and HCV.

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RETRACTED ARTICLE: Classification of nucleotide sequences for quality assessment using logistic regression and decision tree approaches

Kurt

Öz

Aşkın

et al. 2017

Neural Comput & Applic

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Feature based quality assessment of DNA sequencing chromatograms

Cited by 6 publications

References 20 publications

Feature extraction for DNA capillary electrophesis signals based on discrete wavelet transform combined with multi-scale permutation entropy

Feature extraction for DNA capillary electrophesis signals based on discrete wavelet transform combined with multi-scale permutation entropy

Classification of Hepatitis Viruses from Sequencing Chromatograms Using Multiscale Permutation Entropy and Support Vector Machines

RETRACTED ARTICLE: Classification of nucleotide sequences for quality assessment using logistic regression and decision tree approaches

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