Current Bioinformatics - Volume 4, Issue 1, 2009

Primer Approximation Multiplex PCR (PAMP) is a recently introduced experimental technique for detecting large-scale cancer genome lesions such as inversions and deletions from heterogeneous samples containing a mixture of cancer and normal cells. In this chapter we will first review previous solutions for the problem of selecting sets of PAMP primers that minimize detection failure probability and subsequently review our approach based on integer programming formulations for inversion and deletion detections.

Olfactory receptors, in addition to being involved in first step of the physiological processes that leads to olfaction, occupy an important place in mammalian genomes. ORs constitute super families in these genomes. Elucidating olfactory receptor function at a molecular level can be aided by a computationally derived structure and an understanding of its interactions with odor molecules. Experimental functional analyses of olfactory receptors in conjunction with computational studies serve to validate findings and generate hypotheses. We present here a review of the research efforts in: creating computational models of olfactory receptors, identifying binding strategies for these receptors with odorant molecules, performing medium to long range simulation studies of odor ligands in the receptor binding region, and identifying amino acid positions within the receptor that are responsible for ligand-binding and olfactory receptor activation. Written as a primer and a teaching tool, this review will help researchers extend the methodologies described herein to other GPCRs.

Molecular genetic markers represent one of the most powerful tools for the analysis of genomes and enable the association of heritable traits with underlying genomic variation. Molecular marker technology has developed rapidly over the last decade and two forms of sequence based marker, Simple Sequence Repeats (SSRs), also known as microsatellites, and Single Nucleotide Polymorphisms (SNPs) now predominate applications in modern genetic analysis. The reducing cost of DNA sequencing has led to the availability of large sequence data sets derived from whole genome sequencing and large scale Expressed Sequence Tag (EST) discovery that enable the mining of SSRs and SNPs, which may then be applied to diversity analysis, genetic trait mapping, association studies, and marker assisted selection. These markers are inexpensive, require minimal labour to produce and can frequently be associated with annotated genes. Here we review automated methods for the discovery of SSRs and SNPs and provide an overview of the diverse applications of these markers.

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.

An original software package for determination of biological age has been offered. The package is simple for understanding and convenient in application. It is designed for the users who are not professionals in the fields of applied statistics or computer science. The problems and the algorithms realized in the package, the features and the possibilities of their application are described in brief. The package can be used both for fundamental theoretical research in which various logical-mathematical methods of determination of biological age are compared with each other and for applied work in a geriatric clinic.

Biological pathways information has accumulated along with Genomic sequence data. These metabolic pathways help us in understanding network robustness and complex reaction networks. They also provide a framework for improved understanding of microbial physiology and for antimicrobial drug discovery. This article is an attempt to understand the local and global properties of metabolic networks in P. aeruginosa and to identify potential drug targets through ‘load point’ and ‘choke point’ analyses. In this study, we identify 25 choke point enzymes in pathways unique to P. aeruginosa and 202 choke point enzymes in the common pathways between the pathogen and the host human. We also list top 10 choke point enzymes based on the load point values and number of shortest paths and propose them as putative targets. These data underscore the utility of systems analyses methods for understanding human metabolic network in drug discovery process and in-depth understanding of the mechanism of diseases.

Proteins are the molecules of life which are involved in cellular processes. The functional specificity of a protein is linked to its structure. A great section of bioinformatics deals with the prediction, analysis and visualization of protein 3D structures. High-throughput methods for the determination of protein structures provide the information needed to build structure-activity relationships. The accessibility of these structural data together with genomic and clinical data is of crucial importance for the application of bioinformatics in medical research. The experimental methods are supplemented by homology modelling, where new protein structures are predicted by exploiting structural information from known configurations. Computer visualization of protein models provide insights into biological processes which can not be adequately explained otherwise. For the analysis of protein-protein interactions, Voronoi tessellations are used to quantify the macromolecular interfaces. Details at the atomic and electronic levels of the protein molecules, needed for a deeper understanding of properties that remain unrevealed after structural elucidation, are provided by methods based on quantum theoretical calculations. Many proteins are of immediate medical and pharmacological relevance. The structural analysis is therefore of special interest to understand diseases at a molecular level, which is the prerequisite for new developments in diagnosis and therapy.