Abstract

Identifying tandem repeats in the proteome of any organism is important not only for understanding the structure and function of the proteins but also for analyzing the association of abnormal expansion of repeat regions with disorders We have developed an efficient tool for identifying Peptide Periodic Repeats (PEPPER) in protein sequences. The tool has three in-built modules: In the first module, initial screening of the protein sequence by a uni-dimensional vector equivalent to the dot-matrix is used to identify all possible repeat regions. In the second module, a sliding window analysis of the putative repeat region using a K-tuple identifies the period and the possible pattern of the repeat. In the third module, wraparound dynamic programming (WDP) algorithm is implemented to obtain the exact repeat boundaries. If the protein sequence does not contain tandem repeat region, then the second and third steps of the algorithm are skipped making it computationally efficient for database search compared to the other existing algorithms. We report up to three possible multiple-periodicities in a given repeat region. The tool reports the consensus repeat pattern, the complete repeat region, the score and alignment of the consensus with the repeat region along with percentage mismatch/indels. A preliminary comparison of our results with the TRIPS database of protein tandem repeats showed that our program not only identifies all the repeats in the TRIPS database, but we were also able to re-annotate a few incorrectly annotated repeats and found few new repeats no reported in the TRIPS database. PEPPER provides the user the facility to change parameter values for identifying small or approximate tandem repeats. A database of repeats can be built by the user for his set of protein sequences on the fly with PEPPER.

Abstract

Prokaryotic Genomes undergo various mutational events during the course of genome evolution through a variety of processes including Horizontal Gene Transfer. These regions of about 10 - 100 Kb in length are called Genomic Islands and presence of such regions offers selective advantage to the organism and may aid in the organism's growth, survival (saprophytic or symbiotic mode) or display of pathogenicity. Earlier analysis on horizontally transferred regions has shown significant variations from the average genomic values in their GC content, codon preferences and dinucleotide bias. We have developed a web-based tool identifying genomic islands in prokaryotic genomes (GIPro) based on various measures, viz., GC content (total and at individual codon positions), dinculeotide biases, and biases in codon and amino acid usage, carried out in a sliding window analysis along the length of the genome. The regions that deviate by n standard deviations (n - user defined, default value 2) from the average genome values for at least two measures are identified as probable genomic islands by the tool. GIPro can also be used for comparison of user defined regions / gene sets to confirm whether both belong to the same genome or have differences in their dinucleotide or codon usage biases as a result of horizontal transfer.

Abstract

The ultimate goal of molecular cell biology is to understand the physiology of living cells in terms of the information that is encoded in the genome of the cell and we would like to address the question how computational biology can help in achieving this goal. Genes coding for proteins is a very important pattern recognition problem in bioinformatics and this lecture focuses on some computational issues in gene identification.

Abstract

Most natural spatiotemporal systems are an organized ensemble of dynamical subsystems whose behaviour is regulated by nonlinearly coupled multi-variable processes. The response of each of these variables and/or combinations of them to any single or composite external perturbation can influence its spatiotemporal behaviour in a complex and non-intuitive manner. This paper attempts to study the dynamical response of two coupled map lattice systems having local dynamics described by (a) coupled discrete maps, and (b) coupled differential equations, to external perturbation (or “pinning”) applied to each variable individually or simultaneously. We show that the response of different variables to external pinning is quite different. Our results indicate that, though this pinning approach is useful in controlling complex dynamics both globally and locally, enhancing complexity in dynamics (“anti-control”) is …