EPConDB: a web resource for gene expression related to pancreatic development, beta-cell function and diabetes.

EPConDB (http://www.cbil.upenn.

Novel genes identified by manual annotation and microarray expression analysis in the pancreas.

The mouse PancChip, a microarray developed for studying endocrine pancreatic development and diabetes, represents over 13,000 cDNAs. After computationally assigning the cDNAs on the array to known genes, manual curation of the remaining sequences identified 211 novel transcripts. In microarray experiments, we found that 196 of these transcripts were expressed in total pancreas and/or pancreatic islets.

Transcriptional program of the endocrine pancreas in mice and humans.

The Endocrine Pancreas Consortium was formed in late 1999 to derive and sequence cDNA libraries enriched for rare transcripts expressed in the mammalian endocrine pancreas. Over the past 3 years, the Consortium has generated 20 cDNA libraries from mouse and human pancreatic tissues and deposited >150,000 sequences into the public expressed sequence tag databases. A special effort was made to enrich for cDNAs from the endocrine pancreas by constructing libraries from isolated islets.

Functional genomics of the endocrine pancreas: the pancreas clone set and PancChip, new resources for diabetes research.

Over the past 5 years, microarrays have greatly facilitated large-scale analysis of gene expression levels. Although these arrays were not specifically geared to represent tissues and pathways known to be affected by diabetes, they have been used in both type 1 and type 2 diabetes research. To prepare a tool that is particularly useful in the study of type 1 diabetes, we have assembled a nonredundant set of 3,400 clones representing genes expressed in the mouse pancreas or pathways known to be affected by diabetes.

Predicting gene ontology functions from ProDom and CDD protein domains.

A heuristic algorithm for associating Gene Ontology (GO) defined molecular functions to protein domains as listed in the ProDom and CDD databases is described. The algorithm generates rules for function-domain associations based on the intersection of functions assigned to gene products by the GO consortium that contain ProDom and/or CDD domains at varying levels of sequence similarity. The hierarchical nature of GO molecular functions is incorporated into rule generation.