Department of Quantitative Health Sciences
Genetic Association Studies; Software; Internet; Databases, Genetic
Bioinformatics | Genetics and Genomics | Life Sciences | Medicine and Health Sciences
BACKGROUND: The availability of research platforms like the web tools of the National Center for Biotechnology Information (NCBI) has transformed the time-consuming task of identifying candidate genes from genetic studies to an interactive process where data from a variety of sources are obtained to select likely genes for follow-up. This process presents its own set of challenges, as the genetic researcher has to interact with several tools in a time-intensive, manual, and cumbersome manner. We developed a method and implemented an effective software system to address these challenges by multidisciplinary efforts of professional software developers with domain experts. The method presented in this paper, Gene RECQuest, simplifies the interaction with existing research platforms through the use of advanced integration technologies.
FINDINGS: Gene RECQuest is a web-based application that assists in the identification of candidate genes from linkage and association studies using information from Online Mendelian Inheritance in Man (OMIM) and PubMed. To illustrate the utility of Gene RECQuest we used it to identify genes physically located within a linkage region as potential candidate genes for a quantitative trait locus (QTL) for very low density lipoprotein (VLDL) response on chromosome 18.
CONCLUSION: Gene RECQuest provides a tool which enables researchers to easily identify and organize literature supporting their own expertise and make informed decisions. It is important to note that Gene RECQuest is a data acquisition and organization software, and not a data analysis method.
DOI of Published Version
BMC Res Notes. 2009 Sep 30;2:201. Link to article on publisher's site
BMC research notes
Sadasivam RS, Sundar G, Vaughan LK, Tanik MM, Arnett DK. (2009). Genetic region characterization (Gene RECQuest) - software to assist in identification and selection of candidate genes from genomic regions. Open Access Publications by UMMS Authors. https://doi.org/10.1186/1756-0500-2-201. Retrieved from https://escholarship.umassmed.edu/oapubs/2100