The identification of regions of DNA sequences that code for proteins is one of the most fundamental applications in bioinformatics. These protein-coding regions are in contrast to other DNA regions that encode functional RNA molecules, provide structural stability of chromosomes, serve as genetic raw materials, represent molecular fossils, or have no known purpose (sometimes called “junk DNA”). A number of approaches have been suggested for differentiating between the protein-coding and non-protein-coding regions of DNA. A selection of these approaches is based on digital signal processing (DSP) techniques. These DSP techniques rely on the phenomenon that protein-coding regions have a prominent power spectrum peak at frequency f = ⅓ arising from the length of codons (three nucleic acids). This article partitions the identification of protein-coding regions into four discrete steps. Based on this partitioning, DSP techniques can be easily described and compared based on their unique implementatio...

Gene prediction based on DNA spectral analysis: a literature review.

Background: This paper compares the most common digital signal processing methods of exon prediction in eukaryotes, and also proposes a technique for noise suppression in exon prediction. The specimen used here which has relevance in medical research, has been taken from the public genomic database - GenBank. Methods: Here exon prediction has been done using the digital signal processing methods viz. binary method, EIIP (electron-ion interaction psuedopotential) method and filter methods. Under filter method two filter designs, and two approaches using these two designs have been tried. The discrete wavelet transform has been used for de-noising of the exon plots. Results: Results of exon prediction based on the methods mentioned above, which give values closest to the ones found in the NCBI database are given here. The exon plot de-noised using discrete wavelet transform is also given. Conclusion: Alterations to the proven methods as done by the authors, improves performance of exon prediction algorithms. Also it has been proven that the discrete wavelet transform is an effective tool for de-noising which can be used with exon prediction algorithms.

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Discrete wavelet transform de-noising in eukaryotic gene splicing

From DNA to protein: Why genetic code context of nucleotides for DNA signal processing? A review

Genome architecture often reflects an organism's lifestyle and can therefore provide insights into gene function, regulation, and adaptation. In several lineages of plant pathogenic fungi and oomycetes, characteristic repeat-rich and gene-sparse regions harbor pathogenicity-related genes such as effectors. In these pathogens, analysis of genome architecture has assisted the mining for novel candidate effector genes and investigations into patterns of gene regulation and evolution at the whole genome level. Here we describe a two-dimensional data binning method in R with a heatmap-style graphical output to facilitate analysis and visualization of whole genome architecture. The method is flexible, combining whole genome architecture heatmaps with scatter plots of the genomic environment of selected gene sets. This enables analysis of specific values associated with genes such as gene expression and sequence polymorphisms, according to genome architecture. This method enables the investigation of whole genome architecture and reveals local properties of genomic neighborhoods in a clear and concise manner.

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Two-dimensional data binning for the analysis of genome architecture in filamentous plant pathogens and other eukaryotes.

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

Prediction of the protein-coding regions (exons) is one of the central issues of DNA sequence analysis. Most of the existing computational methods exploit the period-3 property of the coding-regions to distinguish exons from noncoding regions (introns). However, the current Discrete Fourier Transform (DFT) based methods are inadequate in predicting short exons. In this paper, we present a model-based exon detection approach using statistically optimal null filter. The proposed method employs a model of the period-3 characteristic to maximize signal-to-noise ratio, and least-squares optimization criteria to rapidly detect the presence of exons in the input DNA sequence. Through examples, it is shown that the proposed method is highly effective as compared to the DFT technique, especially in identifying short exons and successive exons separated by short introns.

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

Accurate modeling of devices is critical to efficient computer aided design and optimization. Commonly encountered modeling techniques include empirical formulae, equivalent circuits, and black-box models (eg. neural networks). Important criteria in device modeling are model accuracy, computational simplicity, generality of the modeling approach, and so forth. In this paper, we present a new and systematic CAD algorithm to device modeling based on a concept often referred to as binning. For a given set of data either from measurements or simulations, the proposed algorithm leads to an accurate model comprising of a set of sub-models with best possible accuracy, while keeping the model structure simple. The proposed algorithm is general and can be applied in the context of any black-box modeling technique. In this paper, the algorithm is illustrated for the case of neural network modeling. Resulting models are shown to exhibit relatively better accuracies compared to those developed using a standard modeling approach. Both active and passive modeling examples are presented.

Binning algorithm for accurate computer aided device modeling

Accurate modeling of devices is critical to efficient computer aided design and optimization. Commonly encountered modeling techniques include empirical formulae, equivalent circuits, and black-box models. Important criteria in device modeling are model accuracy, computational simplicity, generality of the modeling approach, etc. In this paper, we present a multi-dimensional CAD algorithm to device modeling based on a concept often referred to as binning. For a given set of data either from measurements or simulations, the proposed algorithm leads to an accurate model comprising of a set of sub-models with best possible accuracy, while keeping the model structure simple. The algorithm is general and can be applied in the context of any black-box modeling technique; however, we demonstrate multi-dimensional neural modeling of active and passive components. Resulting models are shown to exhibit relatively better accuracies compared to those developed via standard modeling approaches.

V.K. Devabhaktuni

Papers

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

Binning algorithm for accurate computer aided device modeling

A new computer-aided multi-dimensional device modeling algorithm based on binning concepts