¶ Presentations

Motivation: To create the enormous diversity of 1012 immunoglobulins (IG) and T cell receptors (TR) per individual, very complex mechanisms occur at the DNA level: the combinatorial diversity results from the junction of the variable (V), diversity (D) and joining (J) genes; the N-diversity represents the addition at random of nucleotides not encoded in the genome; and somatic hypermutations occur in IG rearranged sequences. The accurate annotation of the junction between V, D, J genes in rearranged IG and TR sequences represents therefore a huge challenge by its uniqueness and complexity. We developed IMGT/JunctionAnalysis to analyse automatically in detail the IG and TR junctions, according to the IMGT Scientific chart rules, based on the IMGT-ONTOLOGY concepts. Results: IMGT/JunctionAnalysis is the first tool for the detailed analysis of the IG and TR complex V-J and V-D-J JUNCTION(s). It delimits, at the nucleotide level, the genes resulting from the combinatorial diversity. It identifies accurately the D genes in the junctions of IG heavy (IGH), TR beta (TRB) and delta (TRD) chains. It delimits the palindromic P-REGION(s) and the N-REGION(s) resulting from the N-diversity. It evaluates the number of somatic hypermutations for each gene, within the JUNCTION. IMGT/JunctionAnalysis is capable of analysing, in a single run, an unlimited number of junctions from the same species (currently human or mouse) and from the same locus. Summary: The Gene Ontology Categorizer, developed jointly by the Los Alamos National Laboratory and Procter & Gamble Corp., provides a capability for the categorization task in the Gene Ontology (GO): given a list of genes of interest, what are the best nodes of the GO to summarize or categorize that list? The motivating question is from a drug discovery process, where after some gene expression analysis experiment, we wish to understand the overall effect of some cell treatment or condition by identifying 'where' in the GO the differentially expressed genes fall: 'clustered' together in one place? in two places? uniformly spread throughout the GO? 'high', or 'low'? In order to address this need, we view bio-ontologies more as combinatorially structured databases than facilities for logical inference, and draw on the discrete mathematics of finite partially ordered sets (posets) to develop data representation and algorithms appropriate for the GO. In doing so, we have laid the foundations for a general set of methods to address not just the categorization task, but also other tasks (e.g. distances in ontologies and ontology …