Coding Region Prediction in Genomic Sequences Using a Combination of Digital Signal Processing Approaches

Fuentes, Aníbal Rodríguez; Ginori, Juan V. Lorenzo; Abalo, Ricardo Grau

doi:10.1007/978-3-540-76725-1_66

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…The computational recognition is a basic step in the understanding of a genome and it is one of the challenges in the analysis of newly sequenced genomes. For analyzing genomic sequences and for interpreting genes, precise and fast tools are necessary [2]. In such situation, conventional and modern signal processing methods play a significant role in these fields [1].…”

Section: Introductionmentioning

confidence: 99%

“…Genes serve as the pattern for proteins and some additional products, and mRNA is the main intermediary that translates gene information in the production of genetically encoded molecules [4]. The strands of DNA molecules usually contain the genomic information represented by sequences of nucleotide symbols, symbolic codons (triplets of nucleotides), or symbolic sequences of amino acids in the corresponding polypeptide chains [2]. Simultaneously monitoring of the expression levels of tens of thousands of genes under diverse experimental conditions has been enabled by gene expression microchip, which is perhaps the most rapidly expanding tool of genome analysis.…”

Section: Introductionmentioning

confidence: 99%

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Evolutionary Computational Algorithm by Blending of PPCA and EP-Enhanced Supervised Classifier for Microarray Gene Expression Data

Pradhan

2018

IJ-AI

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In DNA microarray technology, gene classification is considered to be difficult because the attributes of the data, are characterized by high dimensionality and small sample size. Classification of tissue samples in such high dimensional problems is a complicated task. Furthermore, there is a high redundancy in microarray data and several genes comprise inappropriate information for accurate classification of diseases or phenotypes. Consequently, an efficient classification technique is necessary to retrieve the gene information from the microarray experimental data. In this paper, a classification technique is proposed that classifies the microarray gene expression data well. In the proposed technique, the dimensionality of the gene expression dataset is reduced by Probabilistic PCA. Then, an Artificial Neural Network (ANN) is selected as the supervised classifier and it is enhanced using Evolutionary programming (EP) technique. The enhancement of the classifier is accomplished by optimizing the dimension of the ANN. The enhanced classifier is trained using the Back Propagation (BP) algorithm and so the BP error gets minimized. The well-trained ANN has the capacity of classifying the gene expression data to the associated classes. The proposed technique is evaluated by classification performance over the cancer classes, Acute myeloid leukemia (AML) and Acute Lymphoblastic Leukemia (ALL). The classification performance of the enhanced ANN classifier is compared over the existing ANN classifier and SVM classifier.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evolutionary Computational Algorithm by Blending of PPCA and EP-Enhanced Supervised Classifier for Microarray Gene Expression Data

Pradhan

2018

IJ-AI

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“…Short time Fourier transform (STFT) with entropy based methods is incorporated to increase its efficacy to identify the homogeneous regions. [10]. Identification of protein coding regions was developed using modified Gabor-Wavelet transform [11] for the having advantage of being independent of the window length.…”

Section: Introductionmentioning

confidence: 99%

A reduced computational load protein coding predictor using equivalent amino acid sequence of DNA string with period-3 based time and frequency domain analysis

Meher¹,

Dash²,

Meher³

et al. 2011

AJMB

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Development of efficient gene prediction algorithms is one of the fundamental efforts in gene prediction study in the area of genomics. In genomic signal processing the basic step of the identification of protein coding regions in DNA sequences is based on the period-3 property exhibited by nucleotides in exons. Several approaches based on signal processing tools and numerical representations have been applied to solve this problem, trying to achieve more accurate predictions. This paper presents a new indicator sequence based on amino acid sequence, called as aminoacid indicator sequence, derived from DNA string that uses the existing signal processing based timedomain and frequency domain methods to predict these regions within the billions long DNA sequence of eukaryotic cells which reduces the computational load by one-third. It is known that each triplet of bases, called as codon, instructs the cell machinery to synthesize an amino acid. The codon sequence therefore uniquely identifies an amino acid sequence which defines a protein. Thus the protein coding region is attributed by the codons in amino acid sequence. This property is used for detection of period-3 regions using amino acid sequence. Physico-chemical properties of amino acids are used for numerical representation. Various accuracy measures such as exonic peaks, discriminating factor, sensitivity, specificity, miss rate, wrong rate and approximate correlation are used to demonstrate the efficacy of the proposed predictor. The proposed method is validated on various organisms using the standard dataset HMR195, Burset and Guigo and KEGG. The simulation result shows that the proposed method is an effective approach for protein coding prediction.

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Digital Signal Processing in the Analysis of Genomic Sequences

Lorenzo-Ginori

Rodriguez-Fuentes

Abalo

et al. 2009

CBIO

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Digital Signal Processing (DSP) applications in Bioinformatics have received great attention in recent years, where new effective methods for genomic sequence analysis, such as the detection of coding regions, have been developed. The use of DSP principles to analyze genomic sequences requires defining an adequate representation of the nucleotide bases by numerical values, converting the nucleotide sequences into time series. Once this has been done, all the mathematical tools usually employed in DSP are used in solving tasks such as identification of protein coding DNA regions, identification of reading frames, and others. In this article we present an overview of the most relevant applications of DSP algorithms in the analysis of genomic sequences, showing the main results obtained by using these techniques, analyzing their relative advantages and drawbacks, and providing relevant examples. We finally analyze some perspectives of DSP in Bioinformatics, considering recent research results on algebraic structures of the genetic code, which suggest other new DSP applications in this field, as well as the new field of Genomic Signal Processing.

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Coding Region Prediction in Genomic Sequences Using a Combination of Digital Signal Processing Approaches

Cited by 4 publications

References 11 publications

Evolutionary Computational Algorithm by Blending of PPCA and EP-Enhanced Supervised Classifier for Microarray Gene Expression Data

Evolutionary Computational Algorithm by Blending of PPCA and EP-Enhanced Supervised Classifier for Microarray Gene Expression Data

A reduced computational load protein coding predictor using equivalent amino acid sequence of DNA string with period-3 based time and frequency domain analysis

Digital Signal Processing in the Analysis of Genomic Sequences

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