tailieunhanh - Báo cáo y học: "A web tool for finding gene candidates associated with experimentally induced arthritis in the rat"

Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học General Psychiatry cung cấp cho các bạn kiến thức về ngành y đề tài:A web tool for finding gene candidates associated with experimentally induced arthritis in the rat. | Available online http content 7 3 R485 Research article A web tool for finding gene candidates associated with experimentally induced arthritis in the rat Lars Andersson1 Greta Petersen1 Per Johnson1 and Fredrik Stâhl1 2 Open Access 1Department of Cell and Molecular Biology - Genetics Goteborg University Sweden 2School of Health Sciences University College of Borâs Borâs Sweden Corresponding author Lars Andersson Received 2 Dec 2004 Revisions requested 4 Jan 2005 Revisions received 20 Jan 2005 Accepted 24 Jan 2005 Published 18 Feb 2005 Arthritis Research Therapy 2005 7 R485-R492 DOI 86 ar1 700 This article is online at http content 7 3 R485 2005 Andersson et al. licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract Rat models are frequently used for finding genes contributing to the arthritis phenotype. In most studies however limitations in the number of animals result in a low resolution. As a result the linkage between the autoimmune experimental arthritis phenotype and the genomic region that is the quantitative trait locus can cover several hundred genes. The purpose of this work was to facilitate the search for candidate genes in such regions by introducing a web tool called Candidate Gene Capture CGC that takes advantage of free text data on gene function. The CGC tool was developed by combining genomic regions in the rat associated with the autoimmune experimental arthritis phenotype with rat human gene homology data and with descriptions of phenotypic gene effects and selected keywords. Each keyword was assigned a value which was used for ranking genes based on their description of phenotypic gene effects. The application was .