The use of rrs (16S rRNA) gene is widely regarded as the ''gold standard'' for identifying bacteria and determining their phylogenetic relationships. Nevertheless, multiple copies of this gene in a genome is likely to give an overestimation of the bacterial diversity. In each of the 50 Streptococcus genomes (16 species, 50 strains), 4-7 copies of rrs are present. The nucleotide sequences of these rrs genes show high similarity within and among genomes, which did not allow unambiguous identification. A genome-wide search revealed the presence of 27 gene sequences common to all the Streptococcus species. Digestion of these 27 gene sequences with 10 type II restriction endonucleases (REs) showed that unique RE digestion in purH gene is sufficient for clear cut identification of 30 genomes belonging to 16 species. Additional gene-RE combinations allowed identification of another 15 strains belonging to S. pneumoniae, S. pyogenes, and S. suis. For the rest 5 strains, a combination of 2 genes was required for identifying them. The proposed strategy is likely to prove helpful in proper detection of pathogens like Streptococcus.

The highly conserved 16S rRNA (rrs) gene is generally used for bacterial identification. In organisms possessing multiple copies of rrs, high intra-genomic heterogeneity does not allow easy distinction among different species. In order to identify Vibrio species, a wide range of genes have been employed. There is an urgent requirement of a consensus gene, which can be used as biomarker for rapid identification. Eight sequenced genomes of Vibrio species were screened for selecting genes which were common among all the genomes. Out of 108 common genes, 24 genes of sizes varying from 0.11 to 3.94 kb were subjected to in silico digestion with 10 type II restriction endonucleases (RE). A few unique genes-dapF, fadA, hisD, ilvH, lpxC, recF, recR, rph and ruvB in combination with certain REs provided unique digestion patterns, which can be used as biomarkers. This protocol can be exploited for rapid diagnosis of Vibrio species.

Bacterial identification using rrs (16S rRNA) gene is widely reported. Bacteria possessing multiple copies of rrs lead to overestimation of its diversity. Staphylococcus genomes carries 5-6 copies of rrs showing high similarity in their nucleotide sequences, which lead to ambiguous results. The genomes of 31 strains of Staphylococcus representing 7 species were searched for the presence of common genes. In silico digestion of 34 common genes using 10 restriction endonucleases (REs) lead to select gene-RE combinations, which could be used as biomarkers. RE digestion of recA allowed unambiguous identification of 13 genomes representing all the 7 species. In addition, a few more genes (argH, argR, cysS, gyrB, purH, and pyrE) and RE combinations permitted further identification of 12 strains. By employing additional RE and genes unique to a particular strain, it was possible to identify the rest 6 Staphylococcus aureus strains. This approach has the potential to be utilized for rapid detection of Staphylococcus strains.

Problem statement: DNA microarray technique is one of the latest advances in the field of molecular biology and medicine. It is a multiplex technique used in combination of bioinformatics and statistical data analysis. Since, 1995, the technique offers the possibility of conducting tens or hundreds of thousands of simultaneous hybridizations. Approach: This increased experimental efficiency permits high throughput and whole genome expression profiling of pathogens and hosts. Results: Microarray technologies are rapidly advancing with numerous applications in gene expression, genotyping, pharmaco-genomics, proteomics and cell biology, in infectious diseases recognizing the causative agent, molecular typing, in studying the interactions between causative agents and host cells, cancer biology, genetics, determining the presence of a pathogen in food samples, to characterize microbial isolates, identifying the presence of virulence factors or microbial resistance genes, to detect microbial mutations associated with resistance to antiretroviral drugs, simultaneously detect and discriminate several viruses, to study the contaminants in cultures, such as mycoplasma, yeasts, fungi, exogenous and endogenous viruses and prions from both animal and human origin so and so forth. Conclusion: This article reviewed the applicability of this technique elaborately in some of the important areas of biological sciences.

We report the first complete genome sequence of a classical swine fever (CSF) virus of subgenotype 2.2. The virus (CSFV/IND/UK/LAL-290) was isolated from the Uttarakhand state of India from a backyard pig suspected of having CSF. This genome sequence will give useful insight for future molecular epidemiological studies and the development of an effective vaccine in India.

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The importance of Software Reliability Growth Models to control the testing process and for quantitative assessment of software reliability is a well established fact. However, difficulties created by their underlying assumptions, their relevance and validity to real testing environment have made the selection of appropriate model an uphill task. Recently, new dimensions have been added to software reliability engineering with the development of unified modeling schemes. These schemes have proved seminal in the development of the general theory, partially because of their simplicity and mathematical tractability. In this paper, we propose a unified scheme for discrete software reliability growth modeling using the fault detection/correction rate function. Standard probability distributions have been used to model the fault correction and detection times. Initially, we have formulated the unified scheme when the fault correction is immediate to the failure observation and later we extend it to the cases where removal is a two stage process namely failure observation followed by fault detection/correction. The use of fault detection/correction rate or else known as hazard rate to represent stage wise removal of fault during testing highlights the importance of the proposed framework. Convolution of probability distribution functions has been used to represent Stage-wise removal of fault i.e. failure observation, fault detection/fault correction. Few Discrete models have been derived using the proposed methodology. Parameter estimation on two real software failure datasets has been worked out. The results obtained are fairly accurate and quite encouraging.