Eukaryotic Gene Prediction by Spectral Analysis and Pattern Recognition Techniques

Eftestøl, Trygve; Ryen, Tom; Aase, S.O.; Strässle, Christian; Boos, Moritz; Schuster, G.M.; Ruoff, Peter

doi:10.1109/norsig.2006.275214

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…Up to now, distinctive methods have been proposed to overcome these problems which in a comprehensive categorization they can be divided into two groups; Model-dependent or supervised methods and Model-independent or Filter-based methods. Model-dependent methods like Hidden Markov Model (HMM) [9], neural network [10] and pattern recognition [11], are based on some former information gathered from the available datasets, and have been successfully used to predict exons in genes. In [9] an HMM model is proposed for gene identification which resolves three basic problems; Evaluation, Decoding and Learning problems, which can be solved using Forward and Backward, Viterbi and BaumWelch algorithms, respectively.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fast Algorithm for Exon Prediction in Eukaryotic Genes using Linear Predictive Coding Model and Goertzel Algorithm based on the Z-Curve

Saberkari¹,

Shamsi²,

Heravi³

et al. 2013

IJCA

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Punctual identification of protein-coding regions in Deoxyribonucleic Acid (DNA) sequences because of their 3-base periodicity has been a challenging issue in bioinformatics. Many DSP (Digital Signal Processing) techniques have been applied for identification task and concentrated on assigning numerical values to the symbolic DNA sequence and then applying spectral analysis tools such as the short-time discrete Fourier transform (ST-DFT) to locate periodicity components. In this paper, first, the symbolic DNA sequences are converted to digital signal using the Z-curve method, which is a unique 3-D plot to illustrate DNA sequence and presents the biological behavior of DNA sequence. Then a novel fast algorithm is proposed to investigate the location of exons in DNA strand based on the combination of Linear Predictive Coding Model (LPCM) and Goertzel algorithm. The proposed algorithm leads to increase the speed of process and therefor reduce the computational complexity. Detection of small size exons in DNA sequences, exactly, is another advantage of our algorithm. The proposed algorithm ability in exon prediction is compared with several existing methods at the nucleotide level using: (i) specificitysensitivity values; (ii) Receiver Operating Curves (ROC); and (iii) area under ROC curve. Simulation results show that our algorithm increases the accuracy of exon detection relative to other methods for exon prediction. In this paper, we have also developed a useful user friendly package to analyze DNA sequences.

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Section: Introductionmentioning

confidence: 99%

A Novel Fast Algorithm for Exon Prediction in Eukaryotic Genes using Linear Predictive Coding Model and Goertzel Algorithm based on the Z-Curve

Saberkari¹,

Shamsi²,

Heravi³

et al. 2013

IJCA

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“…Now signal processing approaches have been attracting significant attentions in genomic DNA research since the hidden periodicities and features cannot be revealed easily by conventional statistics methods. As examples, [5,6] predict coding regions based on the fact that most of exon sequences have a 3-base periodicity, while intron sequences do not have this unique feature. However, current studies show many exons have no 3-base periodicity, especially for the short coding region sequences [7].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Protein Coding Regions by Support Vector Machine

Guo

Zhu

2009

2009 International Symposium on Intelligent Ubiquitous Computing and Education

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With the exponential growth of genomic sequences, there is an increasing demand to accurately identify protein coding regions from genomic sequences. Despite many progresses being made in the identification of protein coding regions by computational methods during recent years, the performances and efficiencies of the prediction methods still need to be improved. A novel method to predict the position of coding regions is proposed. First, a support vector machine is used as a classifier to recognize the first nucleotide of a codon in a coding region. Then, according to the difference of the time frequency characteristics of the output values of the classifier analyzed by Short Time Fourier Transform, the position of coding regions can be accurately determinate. The algorithm is not only can predict coding regions, but also can identify the first nucleotide of the codon in coding regions. This is very significant for accurate translation into a protein sequence. The simulation results show the proposed method is more effective for coding regions prediction than the existing coding region discovery tools.

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Identification of Protein-Coding Regions in DNA Sequences Using A Time-Frequency Filtering Approach

Sahu

Panda

2011

Genomics, Proteomics &Amp; Bioinformatics

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Accurate identification of protein-coding regions (exons) in DNA sequences has been a challenging task in bioinformatics. Particularly the coding regions have a 3-base periodicity, which forms the basis of all exon identification methods. Many signal processing tools and techniques have been applied successfully for the identification task but still improvement in this direction is needed. In this paper, we have introduced a new promising model-independent time-frequency filtering technique based on S-transform for accurate identification of the coding regions. The S-transform is a powerful linear time-frequency representation useful for filtering in time-frequency domain. The potential of the proposed technique has been assessed through simulation study and the results obtained have been compared with the existing methods using standard datasets. The comparative study demonstrates that the proposed method outperforms its counterparts in identifying the coding regions.

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Eukaryotic Gene Prediction by Spectral Analysis and Pattern Recognition Techniques

Cited by 3 publications

References 10 publications

A Novel Fast Algorithm for Exon Prediction in Eukaryotic Genes using Linear Predictive Coding Model and Goertzel Algorithm based on the Z-Curve

A Novel Fast Algorithm for Exon Prediction in Eukaryotic Genes using Linear Predictive Coding Model and Goertzel Algorithm based on the Z-Curve

Prediction of Protein Coding Regions by Support Vector Machine

Identification of Protein-Coding Regions in DNA Sequences Using A Time-Frequency Filtering Approach

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