Identification of protein coding regions using antinotch filters

doi:10.1016/j.dsp.2012.06.005

Digital Signal Processing

Volume 22, Issue 6, December 2012, Pages 869-877

https://doi.org/10.1016/j.dsp.2012.06.005 Get rights and content

Abstract

A major area of research in genomic sequence analysis is the identification of protein coding regions using the period-3 property. Previously antinotch filter has been used for this purpose. In this paper, three antinotch filters, namely conjugate suppression antinotch filter, antinotch filter followed by moving average filter and harmonic suppression antinotch filter are proposed to improve the identification accuracy. Conjugate suppression antinotch filter suppresses the conjugate frequency component, antinotch filter followed by moving average filter reduces the background noise and harmonic suppression antinotch filter suppresses the harmonic frequency component. Several existing DNA to numerical mapping techniques are compared for GENSCAN test set and based on the result one mapping technique is recommended so that detailed analysis can be performed using various datasets. The computational complexity of the antinotch filters is evaluated in comparison with the ST-DFT method and it is found that the computational load is reduced to a greater extent in antinotch filter. The identification accuracy of the proposed antinotch filter methods is compared with the existing antinotch filter method at the nucleotide level for benchmark datasets. The results show that proposed methods outperform the existing method, giving improved identification of the protein coding regions.

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Cited by (33)

Identification of exon locations in DNA sequences using a fractional digital anti-notch filter
2023, Biomedical Signal Processing and Control
Citation Excerpt :
To evaluate the proposed method, eukaryotic genes containing single to multiple exons were taken from the HMR195 [35] and NCBI [36], and reported in Table 1. These genes have been used in previous GSP researches based on digital filters for exon identification [11,18,3738]. The main study is focused on the F56F11.4 gene (C. Elegans) case and simulations were made on MATLAB software using 8 GB RAM with a 64-bit-6700HQ i7 CPU.
Identification of protein coding region (exon) locations in DNA sequences is a fundamental initial step in genomic signal processing (GSP). Several techniques have already been applied to achieve this challenging task. However, improvements are still needed. Transforms-based methods and digital filtering are among those techniques that have been widely used. These techniques exploit the period-3 property of protein coding regions. This paper proposes the application of a narrowband bandpass fractional digital filter to extract more selectively the single frequency component corresponding to the frequency $f = 1 / 3$ from DNA sequences. The ideal fractional digital anti-notch filter has an infinite amplitude at the central frequency and two tuning parameters which may be used to independently adjust the central frequency and the amplitude frequency response. The ideal filter has been approximated and implemented efficiently as an infinite impulse response (IIR) filter. The effectiveness of the proposed method has been assessed in terms of common performance evaluation metrics computed from the results obtained using DNA sequences taken from the National Center for Biotechnology Information (NCBI) and HMR195 datasets using different numerical transformations including Voss mapping and electron–ion potential (EIIP) representation. In addition to overcome the problem of sliding window size encountered in transform-based methods, comparison with existing state-of-the-art methods for exon location identification has demonstrated superiority of the proposed method on benchmark datasets.
Study of effectiveness of FIR and IIR filters in Exon identification: A comparative approach
2022, Materials Today: Proceedings
Citation Excerpt :
Digital filter-based algorithm employing Antinotch filter to predict the coding regions for a given gene was first proposed by Vaidyanathan and Yoon [12]. The Antinotch filter was later improved by Hota et al. [13]. They introduced three Antinotch filters, namely conjugate suppression Antinotch filter, Antinotch filter followed by moving average filter and harmonic suppression Antinotch filter to improve the identification accuracy.
In recent years, DSP has been widely used in the area of DNA sequence analysis, detection of protein coding and non-coding regions and also in finding abnormalities present in coding and non-coding regions. Using DSP tool, the detection of protein coding and non-coding regions has been executed with great accuracy and less complexity. In this paper, DNA nucleotide sequence is converted into corresponding EIIP sequence in the first step. In order to make a comparative study between different FIR and IIR filters, the sequence is passed through overall six FIR and IIR filters separately to extract the period three frequency components. Finally, Gaussian filter is used to suppress the high frequency noise present in the power spectrum. This study aims to find out most efficient FIR and IIR filters in predicting exons (coding regions) and introns (non-coding regions) based on different evaluating parameters as follows: (i) specificity-sensitivity values (ii) Matthews correlation coefficient (iii) Miss rate and wrong rate (iv) Discriminating factor (v) ROC.
SAVMD: An adaptive signal processing method for identifying protein coding regions
2021, Biomedical Signal Processing and Control
The identification of protein coding regions is a major topic of research in the field of gene prediction. A number of digital signal processing (DSP) based approaches, which exploit 3-base periodicity to detect coding regions, have been proposed. According to these previously published approaches, we summarize that an effective method or filter for identifying protein coding regions should fulfill three important properties, including the independence of the window length, an effective and adaptive frequency response, a fixed basic frequency of $1 ∕ 3 f$ . However, most of published approaches cannot simultaneously satisfy these three points, which causes that their identification accuracy is still limited. In this paper, we propose an adaptive signal processing method, called sinusoidal-assisted variational mode decomposition (SAVMD) for identifying coding regions. The adaptability of SAVMD reflects in two aspects including: (i) The proposed method analyzes numerical sequences without needing any window information; (ii) The spectrum of period-3 component can be automatically fitted by SAVMD in Fourier domain. From this, our proposed method outperforms other DSP-based methods in terms of identification accuracy, which is verified by the experimental results on five benchmark datasets. When processing the dataset where most sequences contain undetermined nucleotides (UDT), SAVMD shows more superior performance than the model-dependent method AUGUSTUS as well as other model-independent methods. In addition, we conduct a comparative analysis on different numerical conversions of DNA sequences using SAVMD. Several applicable methods for SAVMD, which are selected from this experimentation, can provide a reference to the applications of other time–frequency decomposition methods in the field of gene prediction.
Gene prediction by the noise-assisted MEMD and wavelet transform for identifying the protein coding regions
2021, Biocybernetics and Biomedical Engineering
The analysis of protein coding regions of DNA sequences is one of the most fundamental applications in bioinformatics. A number of model-independent approaches have been developed for differentiating between the protein-coding and non-protein-coding regions of DNA. However, these methods are often based on univariate analysis algorithms, which leads to the loss of joint information among four nucleotides of DNA. In this article, we introduce a method on basis of the noise-assisted multivariate empirical mode decomposition (NA-MEMD) and the modified Gabor-wavelet transform (MGWT). The NA-MEMD algorithm, as a multivariate analysis tool, is utilized to reconstruct the numerical analyzed sequence since it enables a matched-scale decomposition across all variables and eliminates the mode mixing. By virtues of NA-MEMD, the MGWT method achieves a stable improvement on the general identification performance. We compare our method with other Digital Signal Processing (DSP) methods on two representative DNA sequences and three benchmark datasets. The results reveal that our method can enhance the spectra of the analyzed sequences, and improve the robustness of MGWT to different DNA sequences, thus obtaining higher identification accuracies of protein coding regions over other applied methods. In addition, another comparative experiment with the model-dependent method (AUGUSTUS) on the recently proposed benchmark dataset G3PO verifies the superiority of model-independent methods (especially NA-MEMD-MGWT) for identifying coding regions of the poor-quality DNA sequences.
Walsh code based numerical mapping method for the identification of protein coding regions in eukaryotes
2020, Biomedical Signal Processing and Control
Citation Excerpt :
However, our objective is to identify exonic regions and it is determined by sensitivity. The performance of proposed method is compared with other DSP based analysis tools like DFT without windowing [41], non-parametric periodogram method (NPPM) [29], non-parametric Welch method (NPWM) [38], conjugate suppression anti-notch filter (CANF) [39], SVD method [40] and modified Gabor wavelet transform (MGWT) [36]. ROC curves for benchmark sequence F56F11 are illustrated in Fig. 6 for various threshold values.
The protein coding regions play a significant role for gene applications in genomic signal processing. Unlike prokaryotes, the coding regions in eukaryotes are arranged in a random manner. Owing to unequal lengths and low volume density of coding regions, the identification of coding regions makes cumbersome. In this work, a new numerical mapping method based on Walsh codes is proposed to detect the coding regions in eukaryotes. The Walsh code for each nucleotide is obtained using the statistical features of a DNA sequence. The proposed method uses static type of mapping to convert a string of DNA nucleotides into a numerical sequence. The numerical sequence is given as an input to the digital signal processing based spectrum identification tool to detect the existence of quasi-periodic components within the coding region. The advantage of our method is that it is simple to design and easy to represent. The performance of the proposed method has been tested on four benchmark databases and a random set of sequences collected from the National Center for Biological Information (NCBI) database. Furthermore, it has been compared with other state-of-the-art spectrum based numerical mapping methods for statistical features such as sensitivity, specificity and accuracy. The proposed method is efficient as it attains 94 % accuracy, 85 % sensitivity and 96 % specificity when tested on the benchmark C. Elegans gene sequence.
A dynamic representation-based, de novo method for protein-coding region prediction and biological information detection
2015, Digital Signal Processing: A Review Journal
In this paper, we propose a new method for the prediction of protein coding regions that is designed to detect novel genes that do not have known, close homologs. The proposed method uses a dynamic representation scheme to convert DNA sequences into a numerical form, and then it uses the nucleotide distribution variance to calculate the period-3 spectrum. The dynamic representation scheme assigns numerical pairs to the nucleotides to emphasize the effect of the nucleotides that have a stronger participation in the period-3 spectrum. The proposed method also uses the nucleotide distribution variance which has less computational cost than the Fourier transform to extract the period-3 spectrum. A post-processing of the period-3 spectrum signal is performed to smooth the signal, detect the period-3 spectrum peaks, and locate the boundaries of the protein-coding regions.
The analysis of the receiver operating characteristic (ROC) curves shows that the proposed method outperforms other Digital Signal Processing (DSP)-based methods. The analysis of the false positive peaks shows that these regions have a similarity with regions that have functional patterns in other DNA sequences. The method also highlights and explores the capabilities of techniques that perform better than homology-based techniques for de novo protein prediction. We believe that this is an area of research that has been underemphasized and deserves additional attention.

View all citing articles on Scopus

Malaya Kumar Hota received his M.Tech. in Electronics Engineering from Visvesvaraya National Institute of Technology, Nagpur, India, in 2002 and Ph.D. in Electronics and Communication Engineering from Motilal Nehru National Institute of Technology, Allahabad, India, in 2011. Presently, he is a Professor in the Department of Electronics and Telecommunication Engineering, Synergy Institute of Engineering and Technology, Dhenkanal, Odisha, India. He has authored or co-authored about twelve publications. His biography has been included in Marquis Whoʼs Who in Science and Engineering, 11th edition, USA. His main research interest is in genomic signal processing with special focus on DNA to numerical mapping techniques and DSP methods for the identification of protein coding regions.

Vinay Kumar Srivastava received his B.E. in ETC from GEC Rewa, MP, India, in 1989, M.Tech. in Communication from IT-BHU, Varanasi, India, in 1991 and Ph.D. in Electrical Engineering from I.I.T. Kanpur, India, in 2001. Presently, he is a Professor in the Department of ECE, MNNIT, Allahabad, India. He has about twenty years of teaching and research experience in the area of signal and image processing. He has chaired many sessions in conferences. He has authored or co-authored about thirty-five publications. His current research interest includes image compression, post-processing, digital watermarking, stability of 2D PSV system, DSP methods for the identification of protein coding regions, design and analysis of IDMA systems.

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Identification of protein coding regions using antinotch filters

Abstract

Section snippets

J. Franklin Inst.

Genomics

J. Theoret. Biol.

Digital Signal Process.

Comput. Biol. Med.

Genomic signal processing

IEEE Signal Process. Mag.

Genomics and proteomics: A signal processorʼs tour

IEEE Circuit Syst. Mag.

DNA composition, codon usage and exon prediction

Prediction of probable genes by Fourier analysis of genomic sequences

CABIOS

The study of correlation structures of DNA sequences: a critical review

Comput. Chem.

Gene prediction by spectral rotation measure: A new method for identifying protein-coding regions

Genome Res.

A digital signal processing method for gene prediction with improved noise suppression

EURASIP J. Appl. Signal Process.

Boosting approach to exon detection in DNA sequences

Electron. Lett.

A DSP approaches for finding the codon bias in DNA sequences

IEEE J. Sel. Top. Signal Process.

Identification of protein coding regions using the modified Gabor-wavelet transform

IEEE/ACM Trans. Comput. Biol. Bioinform.

Identification of protein-coding regions using modified Gabor-wavelet transform with signal boosting technique

Int. J. Comput. Biol. Drug Des.