GIFtS: annotation landscape analysis with GeneCards

<p>Abstract</p> <p>Background</p> <p>Gene annotation is a pivotal component in computational genomics, encompassing prediction of gene function, expression analysis, and sequence scrutiny. Hence, quantitative measures of the annotation landscape constitute a pertinent bioinformatics tool. GeneCards^®is a gene-centric compendium of rich annotative information for over 50,000 human gene entries, building upon 68 data sources, including Gene Ontology (GO), pathways, interactions, phenotypes, publications and many more.</p> <p>Results</p> <p>We present the GeneCards Inferred Functionality Score (GIFtS) which allows a quantitative assessment of a gene's annotation status, by exploiting the unique wealth and diversity of GeneCards information. The GIFtS tool, linked from the GeneCards home page, facilitates browsing the human genome by searching for the annotation level of a specified gene, retrieving a list of genes within a specified range of GIFtS value, obtaining random genes with a specific GIFtS value, and experimenting with the GIFtS weighting algorithm for a variety of annotation categories. The bimodal shape of the GIFtS distribution suggests a division of the human gene repertoire into two main groups: the high-GIFtS peak consists almost entirely of protein-coding genes; the low-GIFtS peak consists of genes from all of the categories. Cluster analysis of GIFtS annotation vectors provides the classification of gene groups by detailed positioning in the annotation arena. GIFtS also provide measures which enable the evaluation of the databases that serve as GeneCards sources. An inverse correlation is found (for GIFtS>25) between the number of genes annotated by each source, and the average GIFtS value of genes associated with that source. Three typical source prototypes are revealed by their GIFtS distribution: genome-wide sources, sources comprising mainly highly annotated genes, and sources comprising mainly poorly annotated genes. The degree of accumulated knowledge for a given gene measured by GIFtS was correlated (for GIFtS>30) with the number of publications for a gene, and with the seniority of this entry in the HGNC database.</p> <p>Conclusion</p> <p>GIFtS can be a valuable tool for computational procedures which analyze lists of large set of genes resulting from wet-lab or computational research. GIFtS may also assist the scientific community with identification of groups of uncharacterized genes for diverse applications, such as delineation of novel functions and charting unexplored areas of the human genome.</p

Detection of regulator genes and eQTLs in gene networks

Genetic differences between individuals associated to quantitative phenotypic traits, including disease states, are usually found in non-coding genomic regions. These genetic variants are often also associated to differences in expression levels of nearby genes (they are "expression quantitative trait loci" or eQTLs for short) and presumably play a gene regulatory role, affecting the status of molecular networks of interacting genes, proteins and metabolites. Computational systems biology approaches to reconstruct causal gene networks from large-scale omics data have therefore become essential to understand the structure of networks controlled by eQTLs together with other regulatory genes, and to generate detailed hypotheses about the molecular mechanisms that lead from genotype to phenotype. Here we review the main analytical methods and softwares to identify eQTLs and their associated genes, to reconstruct co-expression networks and modules, to reconstruct causal Bayesian gene and module networks, and to validate predicted networks in silico.Comment: minor revision with typos corrected; review article; 24 pages, 2 figure

Whole Genome Expression Array Profiling Highlights Differences in Mucosal Defense Genes in Barrett's Esophagus and Esophageal Adenocarcinoma

Esophageal adenocarcinoma (EAC) has become a major concern in Western countries due to rapid rises in incidence coupled with very poor survival rates. One of the key risk factors for the development of this cancer is the presence of Barrett's esophagus (BE), which is believed to form in response to repeated gastro-esophageal reflux. In this study we performed comparative, genome-wide expression profiling (using Illumina whole-genome Beadarrays) on total RNA extracted from esophageal biopsy tissues from individuals with EAC, BE (in the absence of EAC) and those with normal squamous epithelium. We combined these data with publically accessible raw data from three similar studies to investigate key gene and ontology differences between these three tissue states. The results support the deduction that BE is a tissue with enhanced glycoprotein synthesis machinery (DPP4, ATP2A3, AGR2) designed to provide strong mucosal defenses aimed at resisting gastro-esophageal reflux. EAC exhibits the enhanced extracellular matrix remodeling (collagens, IGFBP7, PLAU) effects expected in an aggressive form of cancer, as well as evidence of reduced expression of genes associated with mucosal (MUC6, CA2, TFF1) and xenobiotic (AKR1C2, AKR1B10) defenses. When our results are compared to previous whole-genome expression profiling studies keratin, mucin, annexin and trefoil factor gene groups are the most frequently represented differentially expressed gene families. Eleven genes identified here are also represented in at least 3 other profiling studies. We used these genes to discriminate between squamous epithelium, BE and EAC within the two largest cohorts using a support vector machine leave one out cross validation (LOOCV) analysis. While this method was satisfactory for discriminating squamous epithelium and BE, it demonstrates the need for more detailed investigations into profiling changes between BE and EAC

Applying Benford’s law to detect accounting data manipulation in the banking industry

We utilise Benford’s Law to test if balance sheet and income statement data broadly used to assess bank soundness were manipulated prior to and also during the global financial crisis. We find that all banks resort to loan loss provisions to manipulate earnings and income upwards. Distressed institutions that have stronger incentives to conceal their financial difficulties resort additionally to manipulating loan loss allowances and non-performing loans downwards. Moreover, manipulation is magnified during the crisis and expands to encompass regulatory capital

Calcium in health and disease.

Evolution has exploited the chemical properties of Ca(2+), which facilitate its reversible binding to the sites of irregular geometry offered by biological macromolecules, to select it as a carrier of cellular signals. A number of proteins bind Ca(2+) to specific sites: those intrinsic to membranes play the most important role in the spatial and temporal regulation of the concentration and movements of Ca(2+) inside cells. Those which are soluble, or organized in non-membranous structures, also decode the Ca(2+) message to be then transmitted to the targets of its regulation. Since Ca(2+) controls the most important processes in the life of cells, it must be very carefully controlled within the cytoplasm, where most of the targets of its signaling function reside. Membrane channels (in the plasma membrane and in the organelles) mediate the entrance of Ca(2+) into the cytoplasm, ATPases, exchangers, and the mitochondrial Ca(2+) uptake system remove Ca(2+) from it. The concentration of Ca(2+) in the external spaces, which is controlled essentially by its dynamic exchanges in the bone system, is much higher than inside cells, and can, under conditions of pathology, generate a situation of dangerous internal Ca(2+) overload. When massive and persistent, the Ca(2+) overload culminates in the death of the cell. Subtle conditions of cellular Ca(2+) dyshomeostasis that affect individual systems that control Ca(2+), generate cell disease phenotypes that are particularly severe in tissues in which the signaling function of Ca(2+) has special importance, e.g., the nervous system