The History section lists all sets of isoforms generated or retrieved during the current session, and provides links to display their contents or to download them to local files.

<p>The History section lists all sets of isoforms generated or retrieved during the current session, and provides links to display their contents or to download them to local files.</p

The input form for the <i>Generate</i> section of the website.

<p>The user may specify the isoform structure(s) in a variety of different ways, including pasting a specially-formatted FASTA sequence or uploading it from a file.</p

The output of the <i>Lookup</i> command.

<p>The system displays the list of retrieved isoforms matching the supplied query terms, providing information about their signatures and their structures. All the results displayed in this page can be downloaded to a local file.</p

The input form for the <i>Lookup</i> section of the website.

<p>The user may enter an isoform signature, a signature string, or a list of gene names as the query term. It is also possible to specify what kind of search to perform (exact or approximate, with or without coding sequence information) and the list of isoform databases to search.</p

A Knowledge-Based Method for Association Studies on Complex Diseases

<div><p>Complex disorders are a class of diseases whose phenotypic variance is caused by the interplay of multiple genetic and environmental factors. Analyzing the complexity underlying the genetic architecture of such traits may help develop more efficient diagnostic tests and therapeutic protocols. Despite the continuous advances in revealing the genetic basis of many of complex diseases using genome-wide association studies (GWAS), a major proportion of their genetic variance has remained unexplained, in part because GWAS are unable to reliably detect small individual risk contributions and to capture the underlying genetic heterogeneity. In this paper we describe a hypothesis-based method to analyze the association between multiple genetic factors and a complex phenotype. Starting from sets of markers selected based on preexisting biomedical knowledge, our method generates multi-marker models relevant to the biological process underlying a complex trait for which genotype data is available. We tested the applicability of our method using the WTCCC case-control dataset. Analyzing a number of biological pathways, the method was able to identify several immune system related multi-SNP models significantly associated with Rheumatoid Arthritis (RA) and Crohn’s disease (CD). RA-associated multi-SNP models were also replicated in an independent case-control dataset. The method we present provides a framework for capturing joint contributions of genetic factors to complex traits. In contrast to hypothesis-free approaches, its results can be given a direct biological interpretation. The replicated multi-SNP models generated by our analysis may serve as a predictor to estimate the risk of RA development in individuals of Caucasian ancestry.</p> </div

The <i>p-</i>values associated with the pairwise comparisons of the RA group and the two control groups using the successful models derived from immune system related pathways.

<p>The fitness <i>p-</i>values measure the fitness of each successful model retrieved by Genetic Algorithm engine. They are calculated by comparing original case and control datasets using corresponding successful models. Randomization-test <i>p-</i>values measure the significance of fitness <i>p-</i>values of their corresponding successful model by comparing permuted case and control datasets. According to Bonferroni’s correction, a fitness <i>p-</i>value <6.944×10<b>⁻</b>⁹ and a randomization test <i>p-</i>value <0.00104 were considered significant. The <i>p-</i>values of the models showing strong or moderate association with rheumatoid arthritis are in bold.</p

<i>Disease risk-Score class</i> diagram for RA <i>vs.</i> CTR and NARAC-A <i>vs.</i> NARAC-C comparisons.

<p>For each comparison overall score variable derived from the entire set of 44 SNPs present in the eight replicated RA-associated models was discretized into 12 bins, and for each bin the posterior probability of being affected by disease was calculated based on Bayes formula.</p

The <i>p-</i>values associated with the comparison of the RA and CTR groups and NARAC-A and NARAC-C using the successful models derived from pathways under consideration.

<p>According to Bonferroni’s correction, a fitness <i>p-</i>value <6.944×10<b>⁻</b>⁹ and a randomization test <i>p-</i>value <0.00208 were considered significant. The <i>p-</i>values of the models showing significant association with rheumatoid arthritis comparing RA <i>vs.</i> CTR are in bold. Of these 12 pathways, five were replicated in NARAC dataset at the significance level of 0.00208 and three were replicated at the significance level of 0.05. The <i>p</i>-values of these replicated models are also in bold.</p

Multivariate regression of disease-state on the score variables derived from the successful models showing strong or moderate association with rheumatoid arthritis (comparing RA <i>vs.</i> CTR).

<p>Pathway 1: B-cell Receptor Signaling Pathway.</p><p>Pathway 2: Chemokine Signaling Pathway.</p><p>Pathway 3: Complement and Coagulation Cascades Pathway.</p><p>Pathway 4: Cytokine -Cytokine Receptor Interaction Pathway.</p><p>Pathway 5: Fc Gamma R-mediated Phagocytosis Pathway.</p><p>Pathway 6: Intestinal Immune Network for IgA Production Pathway.</p><p>Pathway 7: Leukocyte Trans-endothelial Migration Pathway.</p><p>Pathway 8: Natural Killer Cell Mediated Cytotoxicity Pathway.</p><p>Pathway 9: T-cell Receptor Signaling Pathway.</p><p>Pathway 10: Toll-like Receptor Signaling Pathway.</p

The <i>p-</i>values associated with the pairwise comparisons of the RA group and the two control groups using successful models derived from negative control pathways.

<p>The <i>p-</i>values associated with the pairwise comparisons of the RA group and the two control groups using successful models derived from negative control pathways.</p