BACKGROUND:Rat models are frequently used to find genomic regions that contribute to complex diseases, so called quantitative trait loci (QTLs). In general, the genomic regions found to be associated with a quantitative trait are rather large, covering hundreds of genes. To help selecting appropriate candidate genes from QTLs associated with type 2 diabetes models in rat, we have developed a web tool called Candidate Gene Capture (CGC), specifically adopted for this disorder.METHODS:CGC combines diabetes-related genomic regions in rat with rat/human homology data, textual descriptions of gene effects and an array of 789 keywords. Each keyword is assigned values that reflect its co-occurrence with 24 different reference terms describing sub-phenotypes of type 2 diabetes (for example "insulin resistance"). The genes are then ranked based on the occurrences of keywords in the describing texts.RESULTS:CGC includes QTLs from type 2 diabetes models in rat. When comparing gene rankings from CGC based on one sub-phenotype, with manual gene ratings for four QTLs, very similar results were obtained. In total, 24 different sub-phenotypes are available as reference terms in the application and based on differences in gene ranking, they fall into separate clusters.CONCLUSION:The very good agreement between the CGC gene ranking and the manual rating confirms that CGC is as a reliable tool for interpreting textual information. This, together with the possibility to select many different sub-phenotypes, makes CGC a versatile tool for finding candidate genes. CGC is publicly available at http://ratmap.org/CGC.

Background: The names of genes are central in describing their function and relationship. However, gene symbols are often a subject of controversy. In addition, the discovery of mammalian genes is now so rapid that a proper use of gene symbol nomenclature rules tends to be overlooked. This is currently the situation in the rat and there is a need for a cohesive and unifying overview of all rat gene symbols in use. Based on the experiences in rat gene symbol curation that we have gained from running the "Ratmap" rat genome database, we have now developed a database that unifies different rat gene naming attempts with the accepted rat gene symbol nomenclature rules.Description: This paper presents a newly developed database known as RGST (Rat Gene Symbol Tracker). The database contains rat gene symbols from three major sources: the Rat Genome Database (RGD), Ensembl, and NCBI- Gene. All rat symbols are compared with official symbols from orthologous human genes as specified by the Human Gene Nomenclature Committee (HGNC). Based on the outcome of the comparisons, a rat gene symbol may be selected. Rat symbols that do not match a human ortholog undergo a strict procedure of comparisons between the different rat gene sources as well as with the Mouse Genome Database (MGD). For each rat gene this procedure results in an unambiguous gene designation. The designation is presented as a status level that accompanies every rat gene symbol suggested in the database. The status level describes both how a rat symbol was selected, and its validity.Conclusion: This database fulfils the important need of unifying rat gene symbols into an automatic and cohesive nomenclature system. The RGST database is available directly from the RatMap home page: http://ratmap.org.

The inbred BDII rat is a valuable experimental model for the genetic analysis of endometrial adenocarcinoma (EAC). One common aberration detected by comparative genomic hybridization in rat EAC was gain/amplification affecting the proximal part of rat chromosome 6 (RNO6). We applied rat and mouse chromosome painting probes onto tumor cell metaphase preparations in order to detect and characterize gross RNO6 aberrations. In addition, the RNO6q11–q16 segment was analyzed by fluorescence in situ hybridization with probes representing 12 cancer‐related genes in the region. The analysis revealed that seven tumors contained large RNO6‐derived homogeneously staining regions (HSRs) in addition to several normal or near‐normal RNO6 chromosomes. Five tumors (two of which also had HSRs) exhibited a selective increase of the RNO6q11–q16 segment, sometimes in conjunction with moderate amplification of one or a few genes. Most commonly, the amplification affected the region centered around band 6q16 and included the Mycn, Ddx1, and Rrm2 genes. A second region, centering around Slc8a1 and Xdh, also was affected by gene amplification but to a lesser extent. The aberrations in the proximal part of RNO6 were further analyzed using allelotyping of microsatellite markers in all tumors from animals that were heterozygous in the proximal RNO6 region. We could detect allelic imbalance (AI) in 12 of 20 informative tumors, 6 of which were in addition to those already analyzed by molecular cytogenetic methods as described. Our findings suggest that increase/amplification of genes in this chromosome region contribute to the development of this hormone‐dependent tumor. © 2005 Wiley‐Liss, Inc.

The rat genome database RatMap (http://ratmap.org or http://ratmap.gen.gu.se) has been one of the main resources for rat genome information since 1994. The database is maintained by CMB-Genetics at Goteborg University in Sweden and provides information on rat genes, polymorphic rat DNA-markers and rat quantitative trait loci (QTLs), all curated at RatMap. The database is under the supervision of the Rat Gene and Nomenclature Committee (RGNC); thus much attention is paid to rat gene nomenclature. RatMap presents information on rat idiograms, karyotypes and provides a unified presentation of the rat genome sequence and integrated rat linkage maps. A set of tools is also available to facilitate the identification and characterization of rat QTLs, as well as the estimation of exon/intron number and sizes in individual rat genes. Furthermore, comparative gene maps of rat in regard to mouse and human are provided.

Published quantitative trait locus (QTL) data, as well as all known rat genes and DNA markers, have since 1993 been collected and made easily accessible at the rat genome database, RatMap. The objective of the present study is to fully integrate available data concerning rat models with human genome information. The final goal of this process is to make results from any rat model experiment directly applicable to humans. The overall goal of this work is to create an automatic system which, for any given rat chromosomal region associated with a QTL, will characterize both mapped rat genes and all putative homologous human genes in that region. This article reports the use of the web application to find human gene candidates contributing to an arthritis phenotype.