An introduction to Graph Data Management

A graph database is a database where the data structures for the schema and/or instances are modeled as a (labeled)(directed) graph or generalizations of it, and where querying is expressed by graph-oriented operations and type constructors. In this article we present the basic notions of graph databases, give an historical overview of its main development, and study the main current systems that implement them

Integration of gene expression data with prior knowledge for network analysis and validation

<p>Abstract</p> <p>Background</p> <p>Reconstruction of protein-protein interaction or metabolic networks based on expression data often involves in silico predictions, while on the other hand, there are unspecific networks of in vivo interactions derived from knowledge bases.</p> <p>We analyze networks designed to come as close as possible to data measured in vivo, both with respect to the set of nodes which were taken to be expressed in experiment as well as with respect to the interactions between them which were taken from manually curated databases</p> <p>Results</p> <p>A signaling network derived from the TRANSPATH database and a metabolic network derived from KEGG LIGAND are each filtered onto expression data from breast cancer (SAGE) considering different levels of restrictiveness in edge and vertex selection.</p> <p>We perform several validation steps, in particular we define pathway over-representation tests based on refined null models to recover functional modules. The prominent role of the spindle checkpoint-related pathways in breast cancer is exhibited. High-ranking key nodes cluster in functional groups retrieved from literature. Results are consistent between several functional and topological analyses and between signaling and metabolic aspects.</p> <p>Conclusions</p> <p>This construction involved as a crucial step the passage to a mammalian protein identifier format as well as to a reaction-based semantics of metabolism. This yielded good connectivity but also led to the need to perform benchmark tests to exclude loss of essential information. Such validation, albeit tedious due to limitations of existing methods, turned out to be informative, and in particular provided biological insights as well as information on the degrees of coherence of the networks despite fragmentation of experimental data.</p> <p>Key node analysis exploited the networks for potentially interesting proteins in view of drug target prediction.</p

Chondroitin sulfates and their binding molecules in the central nervous system

Chondroitin sulfate (CS) is the most abundant glycosaminoglycan (GAG) in the central nervous system (CNS) matrix. Its sulfation and epimerization patterns give rise to different forms of CS, which enables it to interact specifically and with a significant affinity with various signalling molecules in the matrix including growth factors, receptors and guidance molecules. These interactions control numerous biological and pathological processes, during development and in adulthood. In this review, we describe the specific interactions of different families of proteins involved in various physiological and cognitive mechanisms with CSs in CNS matrix. A better understanding of these interactions could promote a development of inhibitors to treat neurodegenerative diseases

Sequencing technologies and genome sequencing

The high-throughput - next generation sequencing (HT-NGS) technologies are currently the hottest topic in the field of human and animals genomics researches, which can produce over 100 times more data compared to the most sophisticated capillary sequencers based on the Sanger method. With the ongoing developments of high throughput sequencing machines and advancement of modern bioinformatics tools at unprecedented pace, the target goal of sequencing individual genomes of living organism at a cost of $1,000 each is seemed to be realistically feasible in the near future. In the relatively short time frame since 2005, the HT-NGS technologies are revolutionizing the human and animal genome researches by analysis of chromatin immunoprecipitation coupled to DNA microarray (ChIP-chip) or sequencing (ChIP-seq), RNA sequencing (RNA-seq), whole genome genotyping, genome wide structural variation, de novo assembling and re-assembling of genome, mutation detection and carrier screening, detection of inherited disorders and complex human diseases, DNA library preparation, paired ends and genomic captures, sequencing of mitochondrial genome and personal genomics. In this review, we addressed the important features of HT-NGS like, first generation DNA sequencers, birth of HT-NGS, second generation HT-NGS platforms, third generation HT-NGS platforms: including single molecule Heliscope™, SMRT™ and RNAP sequencers, Nanopore, Archon Genomics X PRIZE foundation, comparison of second and third HT-NGS platforms, applications, advances and future perspectives of sequencing technologies on human and animal genome research

The distribution of immunoglobulins and the C3 component of complement in multiple biopsies from the uninvolved and perilesional skin in pemphigus.

The distribution of immunoglobulins and the C3 component of complement (C3) in the skin of 29 patients with various forms of pemphigus was studied. Biopsies, from clinically uninvolved skin, were taken from three different sites and perilesional skin was taken when lesions were present. Thirteen patients (45%) who were studied had intercellular deposits of IgG and C3. There was no significant variation between sites. Ten patients (34%), in cutaneous remission, were negative at all sites. We conclude that in pemphigus, a biopsy for direct immunofluorescence can be taken from any suitable site on the skin surface

Graph Database for Collaborative Communities

Data manipulated in an enterprise context are structured data as well as unstructured data such as emails, documents, and social networks. Graphs are a natural way of representing and modeling such data (structured, semistructured, and unstructured ones) in a unified manner. The main advantage of such a structure relies on the dynamic aspect and the capability to represent relations, even multiple ones, between objects. Recent database research work shows a growing interest in the definition of graph models and languages to allow a natural way of presenting data. In this chapter, we provide a survey of the main graph database models and the associated graph query languages. We then present an application using a graph database to extract social networks