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The goal of the Human Genome Project and the subsequent HapMap Project was to accelerate the pace at which genes for complex human traits were discovered. Elated by the early successes from cloning disease genes for monogenic disorders, the architects of the projects reasoned that complex human diseases were tractable to positional cloning methods. However, a schism emerged in the field, with hot debates regarding two competing hypotheses being publicly waged. These opposing hypotheses pertained to the anticipated allelic spectrum and frequency of disease variants associated with common, complex disease. The common disease, common variant hypothesis (CD/CV) stated that a few common allelic variants could account for the genetic variance in disease susceptibility, whereas the rare variant (CD/RV) hypothesis stated that DNA sequence variation at any gene causing disease could encompass a wide range of possibilities, with the most extreme being that each mutation is only found once in the population. The practical consequence of the debate can be broken into two parts. If the CD/CV hypothesis is true, then application of the positional cloning paradigm to map disease genes would be eminently more feasible, as a common allele would be easier to locate. Conversely, if rare variants cause common disease, then identifying these genetic susceptibility variants would be challenging. Whether a disease is caused by rare or common alleles will have an impact on clinical applications, such as designing prognostic assays, or planning therapeutic interventions; fewer susceptibility alleles will simplify assay design, and the associated reduction in costs would amortize if a universally applicable therapy can be deployed. A current review of the literature suggests that both these hypotheses are correct, depending on the gene and disease examined. Although the controversial debate is revived with the identification of each new disease gene, the time has come to integrate both hypotheses in a manner that best explains biological variation in natural populations. The allelic spectrum of variation in a particular gene may be better explained by one of the two hypotheses but, for a multifactorial trait, a composite encompassing all influential genes needs to be constructed. [ABSTRACT FROM AUTHOR]

Advertisements(Ads) are the main revenue earner for Television (TV) broadcasters. As TV reaches a large audience, it acts as the best media for advertisements of products and services. With the emergence of digital TV, it is important for the broadcasters to provide an intelligent service according to the various dimensions like program features, ad features, viewers' interest and sponsors' preference. We present an automatic ad recommendation algorithm that selects a set of ads by considering these dimensions and semantically match them with programs. Features of the ad video are captured interms of annotations and they are grouped into number of predefined semantic categories by using a categorization technique. Fuzzy categorical data clustering technique is applied on categorized data for selecting better suited ads for a particular program. Since the same ad can be recommended for more than one program depending upon multiple parameters, fuzzy clustering acts as the best suited method for ad recommendation. The relative fuzzy score called "degree of membership" calculated for each ad indicates the membership of a particular ad to different program clusters. Subjective evaluation of the algorithm is done by 10 different people and rated with a high success score. [ABSTRACT FROM AUTHOR]

Helicases are ubiquitous enzymes that disrupt complementary strands of duplex nucleic acid in a reaction dependent on nucleoside-5′-triphosphate hydrolysis. Helicases are implicated in the metabolism of DNA structures that are generated during replication, recombination, and DNA repair. Furthermore, an increasing number of helicases have been linked to genomic instability and human disease. With the growing interest in helicase mechanism and function, we have set out to describe some basic protocols for biochemical characterization of DNA helicases. Protocols for measuring ATP hydrolysis, DNA binding, and catalytic unwinding activity of DNA helicases are provided. Application of these procedures should enable the researcher to address fundamental questions regarding the biochemical properties of a given helicase, which would serve as a platform for further investigation of its molecular and cellular functions. [ABSTRACT FROM AUTHOR]

The post Human Genome Project era calls for reliable, integrated, flexible, and convenient data management techniques to facilitate research activities. Querying biological data that is large in volume and complex in structure such as 3D proteins requires expressive models to explicitly support and capture the semantics of the complex data. Protein 3D structure search and comparison not only enable us to predict unknown structures, but can also reveal distant evolutionary relationships that are otherwise undetectable, and perhaps suggest unsuspected functional properties. In this work, we model 3D protein structures by adding spatial semantics and constructs to represent the contributing forces such as hydrogen bonds and high-level structures such as protein secondary structures. This paper makes a contribution to modeling the specialty of life science data and develops methods to meet the novel challenges posed by such data. [ABSTRACT FROM AUTHOR]

Molecular medical research has traditionally required hypothesis-driven strategies, often focusing on identification of linear signaling pathways that mediate disease pathogenesis. This approach does not identify novel pathways, or genes that act together to produce pathology or a disease-permissive milieu. To generate new hypotheses that require no a priori assumptions about disease pathogenesis, gene-expression profiles of normal and diseased tissues or cell types can be compared. Although determining protein-expression patterns would be preferable, necessary methodologies are not readily available, and protein diversity generated by post-translation modifications adds technical complexities to this strategy. On the other hand, techniques for the study of RNA-expression patterns are available, and represent a reasonable "data mining" alternative. Complex relationships between multiple molecules that regulate disease pathogenesis should be identified, at least in part, by analyzing transcript profiles generated from normal and diseased states. Differential display (1,2), subtractive hybridization (3), and subtraction libraries (4) are semiquantitative, comparative tools that have been utilized previously. However, molecular pathway discovery has been revolutionized by development of high-throughput, quantitative techniques, including serial analysis of gene expression (SAGE) and hybridization array (5,6). Because both of these methods generate transcript libraries or transcriptomes, which catalog thousands of simultaneously expressed genes, a small laboratory can comprehensively determine differential gene-expression profiles within months. [ABSTRACT FROM AUTHOR]

One of the most powerful movements in the information technology community today is the widespread adoption of open-source software. Using the right software is increasingly critical to project success, but the choices keep getting wider and more confusing. Open source software (OSS) has entered the mix, leaving the traditional confines of the hacker community and entering large-scale, well-publicized applications. However, although some argue that it is ready for wide-scale commercial adaptation and deployment, the myriad number of Open Source Software packages makes actual adoption a real challenge. This paper presents a straightforward and practical roadmap to analyze Open Source Software and Proprietary software using performance testing. In this paper the website application was developed in various software applications including open source software and proprietary software. All of these applications are tested using OpenSTA performance testing tool. From this paper one can conclude the performance like elapsed time, timer values and the response of software to the number of active virtual user of Open source software when compared to the Proprietary software. [ABSTRACT FROM AUTHOR]

Genomic researchers use a number of heterogeneous data sources including nucleotides, protein sequences, 3-D Protein structures, taxonomies, and research publications such as MEDLINE. This research aims to discover as much biological knowledge as possible about the properties and functions of the structures such as DNA sequences and protein structures and to explore the connections among all the data, so that the knowledge can be used to improve human lives. Currently it is very difficult to connect all of these data sources seamlessly unless all the data is transformed into a common format with an id connecting all of them. The state-of-the-art facilities for searching these data sources provide interfaces through which scientists can access multiple databases. Most of these searches are primarily text based, requiring users to specify keywords using which the systems search through each individual data source and returns results. The user is then required to create the connections between the results from each source. This is a major problem because researchers do not always know how to create these connections. To solve this problem we propose a semantics-based mechanism for automatically linking and connecting the various data sources. Our approach is based on a model that explicitly captures the semantics of the heterogeneous data sources and makes them available for searching. In this talk I will discuss issues related to capturing the semantics of biological data and using these semantics to automate the integration of diverse heterogeneous sources. [ABSTRACT FROM AUTHOR]