Prediction of protein-coding regions in DNA sequences using a model-based approach

Kakumani, Rajasekhar; Devabhaktuni, Vijay; Ahmad, Muneer

doi:10.1109/iscas.2008.4541818

Cited by 24 publications

(12 citation statements)

References 6 publications

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“…An algorithm based on individual periodicity analysis of each nucleotide followed by their combination [21] to recognize the accurate and inaccurate repeat patterns in DNA sequences has been proposed. A method to identify protein coding regions in DNA sequences using statistically optimal null filters (SONF) [22] has been described. Voss representation is used for binary conversion and then processed by a separate SONF producing improved efficiency in predicting short exons.…”

Section: Review Of Related Workmentioning

confidence: 99%

Improved Algorithm for Analysis of DNA Sequences Using Multiresolution Transformation

Inbamalar¹,

Sivakumar²

2015

The Scientific World Journal

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Bioinformatics and genomic signal processing use computational techniques to solve various biological problems. They aim to study the information allied with genetic materials such as the deoxyribonucleic acid (DNA), the ribonucleic acid (RNA), and the proteins. Fast and precise identification of the protein coding regions in DNA sequence is one of the most important tasks in analysis. Existing digital signal processing (DSP) methods provide less accurate and computationally complex solution with greater background noise. Hence, improvements in accuracy, computational complexity, and reduction in background noise are essential in identification of the protein coding regions in the DNA sequences. In this paper, a new DSP based method is introduced to detect the protein coding regions in DNA sequences. Here, the DNA sequences are converted into numeric sequences using electron ion interaction potential (EIIP) representation. Then discrete wavelet transformation is taken. Absolute value of the energy is found followed by proper threshold. The test is conducted using the data bases available in the National Centre for Biotechnology Information (NCBI) site. The comparative analysis is done and it ensures the efficiency of the proposed system.

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Section: Review Of Related Workmentioning

confidence: 99%

Improved Algorithm for Analysis of DNA Sequences Using Multiresolution Transformation

Inbamalar¹,

Sivakumar²

2015

The Scientific World Journal

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“…Gene prediction which refers to locating the proteincoding regions (exons) of genes in a long DNA sequence is the main problem in genomic field. Many different approaches [16] - [20] have been developed to address this open problem but still a better optimized solution is essential. Kakumani and Devabhaktuni [16] , presented a model-based exon detection approach using statistically optimal null filter.…”

Section: Gene Prediction Using Existing Mapping Techniquesmentioning

confidence: 99%

“…Many different approaches [16] - [20] have been developed to address this open problem but still a better optimized solution is essential. Kakumani and Devabhaktuni [16] , presented a model-based exon detection approach using statistically optimal null filter. In [15] a model of the period-3 characteristic is employed to maximize signalto-noise ratio, using least-squares optimization criteria to rapidly detect the presence of exons in DNA sequences.…”

Section: Gene Prediction Using Existing Mapping Techniquesmentioning

confidence: 99%

A Novel Circular Mapping Technique for Spectral Classification of Exons and Introns in Human DNA Sequences

Abo‐Zahhad¹,

Ahmed²,

Abd-Elrahman³

2014

IJITCS

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Signals that represent information may be classified into two forms: numeric and symbolic. Symbolic signals such as DNA symbolic sequences cannot be directly processed with digital signal processing (DSP) techniques. The only way to apply DSP in genomic field is the mapping of DNA symbolic sequences to numerical sequences. Hence, biological properties are reflected in a numerical domain. This opens a field to present a set of tools for solving genomic problems. In literature many techniques have been developed for numerical representation of DNA sequences. The main drawback of these techniques is that each nucleotide is represented by a numerical value depending on nucleotide type only ignoring its position in codon and DNA sequence. In this paper a new approach for DNA symbolic to numeric representation called Circular Mapping (CM) is introduced. It's based on graphical representation of DNA sequence that maps each nucleotide by a complex numerical value depending not only on nucleotide type but also on its position in codons. The main applications of this method are the gene prediction that aims to locate the protein-coding regions and the classification of exons and introns in DNA sequences. The proposed approach showed significant improvement in exons and introns classification as compared with the existing techniques. The efficiency of this method in classification depends on the right choice of the mapping angle () as indicated by the power spectral analysis results over the sequences of the human genome (GRch37/hg19).

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“…It has been shown that the SONF is equivalent to a Kalman filter with a much simpler implementation [35]. Also, SONF has the ability to track rapidly changing signals leading to more practical processing of short-duration signals [36,37]. Therefore, the proposed scheme is expected to perform better in situations even if the CGIs are of very short length of the order of 200 bp.…”

Section: Proposed Schemementioning

confidence: 99%

Identification of CpG islands in DNA sequences using statistically optimal null filters

Kakumani

Ahmad

Devabhaktuni

2012

J Bioinform Sys Biology

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CpG dinucleotide clusters also referred to as CpG islands (CGIs) are usually located in the promoter regions of genes in a deoxyribonucleic acid (DNA) sequence. CGIs play a crucial role in gene expression and cell differentiation, as such, they are normally used as gene markers. The earlier CGI identification methods used the rich CpG dinucleotide content in CGIs, as a characteristic measure to identify the locations of CGIs. The fact, that the probability of nucleotide G following nucleotide C in a CGI is greater as compared to a non-CGI, is employed by some of the recent methods. These methods use the difference in transition probabilities between subsequent nucleotides to distinguish between a CGI from a non-CGI. These transition probabilities vary with the data being analyzed and several of them have been reported in the literature sometimes leading to contradictory results. In this article, we propose a new and efficient scheme for identification of CGIs using statistically optimal null filters. We formulate a new CGI identification characteristic to reliably and efficiently identify CGIs in a given DNA sequence which is devoid of any ambiguities. Our proposed scheme combines maximum signal-to-noise ratio and least squares optimization criteria to estimate the CGI identification characteristic in the DNA sequence. The proposed scheme is tested on a number of DNA sequences taken from human chromosomes 21 and 22, and proved to be highly reliable as well as efficient in identifying the CGIs.

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Prediction of protein-coding regions in DNA sequences using a model-based approach

Cited by 24 publications

References 6 publications

Improved Algorithm for Analysis of DNA Sequences Using Multiresolution Transformation

Improved Algorithm for Analysis of DNA Sequences Using Multiresolution Transformation

A Novel Circular Mapping Technique for Spectral Classification of Exons and Introns in Human DNA Sequences

Identification of CpG islands in DNA sequences using statistically optimal null filters

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