Distributed Bioinformatics Computing System for DNA Sequence Analysis

This paper provides an effective design of computing technique of a distributed bioinformatics computing system for analysis of DNA sequences using OPTSDNA algorithm. This system could be used for disease detection, criminal forensic analysis, gene prediction, genetic system and protein analysis. Different types of distributed algorithms for the search and identification for DNA segments and repeat pattern in a given DNA sequence are developed. The search algorithm was developed to compute the number of DNA sequence which contains the same consecutive types of DNA segments. A distributed subsequence identifications algorithm was designed and implemented to detect the segment containing DNA sequences. Sequential and distributed implementation of these algorithms was executed with different length of search segments patterns and genetic sequences. OPTSDNA algorithm is used for storing various sizes of DNA sequence into database. DNA sequences of different lengths were tested by using this algorithm. These input DNA sequences varied in size from very small to very large. The performance of search technique distributed system is compared with sequential approach

Comparative genomics allowed the identification of drug targets against human fungal pathogens

<p>Abstract</p> <p>Background</p> <p>The prevalence of invasive fungal infections (IFIs) has increased steadily worldwide in the last few decades. Particularly, there has been a global rise in the number of infections among immunosuppressed people. These patients present severe clinical forms of the infections, which are commonly fatal, and they are more susceptible to opportunistic fungal infections than non-immunocompromised people. IFIs have historically been associated with high morbidity and mortality, partly because of the limitations of available antifungal therapies, including side effects, toxicities, drug interactions and antifungal resistance. Thus, the search for alternative therapies and/or the development of more specific drugs is a challenge that needs to be met. Genomics has created new ways of examining genes, which open new strategies for drug development and control of human diseases.</p> <p>Results</p> <p><it>In silico </it>analyses and manual mining selected initially 57 potential drug targets, based on 55 genes experimentally confirmed as essential for <it>Candida albicans </it>or <it>Aspergillus fumigatus </it>and other 2 genes (<it>kre2 </it>and <it>erg6</it>) relevant for fungal survival within the host. Orthologs for those 57 potential targets were also identified in eight human fungal pathogens (<it>C. albicans</it>, <it>A. fumigatus</it>, <it>Blastomyces dermatitidis</it>, <it>Paracoccidioides brasiliensis</it>, <it>Paracoccidioides lutzii, Coccidioides immitis</it>, <it>Cryptococcus neoformans </it>and <it>Histoplasma capsulatum</it>). Of those, 10 genes were present in all pathogenic fungi analyzed and absent in the human genome. We focused on four candidates: <it>trr1 </it>that encodes for thioredoxin reductase, <it>rim8 </it>that encodes for a protein involved in the proteolytic activation of a transcriptional factor in response to alkaline pH, <it>kre2 </it>that encodes for α-1,2-mannosyltransferase and <it>erg6 </it>that encodes for Δ(24)-sterol C-methyltransferase.</p> <p>Conclusions</p> <p>Our data show that the comparative genomics analysis of eight fungal pathogens enabled the identification of four new potential drug targets. The preferred profile for fungal targets includes proteins conserved among fungi, but absent in the human genome. These characteristics potentially minimize toxic side effects exerted by pharmacological inhibition of the cellular targets. From this first step of post-genomic analysis, we obtained information relevant to future new drug development.</p

The role of parallel computing in bioinformatics

The need to intelligibly capture, manage and analyse the ever-increasing amount of publicly available genomic data is one of the challenges facing bioinformaticians today. Such analyses are in fact impractical using uniprocessor machines, which has led to an increasing reliance on clusters of commodity-priced computers. An existing network of cheap, commodity PCs was utilised as a single computational resource for parallel computing. The performance of the cluster was investigated using a whole genome-scanning program written in the Java programming language. The TSpaces framework, based on the Linda parallel programming model, was used to parallelise the application. Maximum speedup was achieved at between 30 and 50 processors, depending on the size of the genome being scanned. Together with this, the associated significant reductions in wall-clock time suggest that both parallel computing and Java have a significant role to play in the field of bioinformatics