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

: 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.

Identification of CpG islands in DNA sequences using matched filters.

CpG islands (CGIs), rich in CG dinucleotides, are usually located in the promoter regions of genes in DNA sequences and are used as gene markers. Identification of CGIs plays an important role in the analysis of DNA sequences. In this paper, we propose a new digital signal processing (DSP) based approach using matched filters for the identification of CGIs. We also formulate a new/reliable CGI identification characteristic replacing the several existing probability transition tables for CGIs and non-CGIs. The peaks in matched filter output, obtained by correlating the CGI characteristic with the DNA sequence to be analyzed, accurately and reliably identify CGIs. This approach is tested on a number of DNA sequences and is proved to be capable of identifying CpG islands efficiently and reliably.

Microarray image segmentation using Chan-Vese active contour model and level set method.

Microarray technology is considered to be one of the major breakthroughs in bioinformatics for profiling gene-expressions of thousands of genes, simultaneously. Analysis of a microarray image plays an important role in the accurate depiction of gene-expression. Segmentation, the process of separating the foreground from the background, of a microarray image, is one of the key issues in microarray image analysis. Level sets have tremendous potential in the segmentation of images. In this paper, a new approach for segmentation of the microarray images is proposed. In this work, Chan-Vese approximation of the Mumford-Shah model and the level set method are employed for image segmentation. Illustrative examples of the proposed method are presented highlighting its effectiveness.

A two-stage neural network based technique for protein secondary structure prediction.

Protein secondary structure prediction is one of the most important research areas in bioinformatics. In this paper, we propose a two-stage protein secondary structure prediction technique, implemented using neural network models. The first neural network stage of the proposed technique associates the input protein sequence to a bin containing its corresponding homologues. The second stage predicts the secondary structure of the input sequence utilizing a neural prediction model specific to the bin obtained from stage one. The strategy of binning allows for simplified and accurate neural models. This technique is implemented on the RS126 dataset and its prediction accuracy is compared with that of the standard PHD approach.