Publications
APS Bulletin • Volume 15, Number 3, Summer 2005
Web Site Reviews
Michael E. Clark, PhD, Department Editor
The GeneNetwork / WebQTL
Reviewed by William R. Lariviere, PhD
Site Audience
This Web site is a resource of interest to basic researchers for an integrative multiscale systems genetic analysis of complex traits and networks of genes, transcripts from several tissues, and higher-order phenotypes based on rodent genetic models that serve as genetic reference populations. Although the bulk of the phenotype data relates to the effects of alcohol, there are some pain and pain-related traits that interested pain researchers can analyze. It was funded by grants from the National Institute of Mental Health, the National Institute on Drug Abuse, the National Science Foundation, the National Institute on Alcohol Abuse and Alcoholism, and the National Cancer Institute. Investigators at the University of Tennessee Health Science Center created the site in 2001 and currently maintain it.
Content Appraisal
Tools available allow for gene mapping of the catalogued phenotypes and for genetic correlation analysis of pain traits with immune responses, other behaviors, and a wealth of microarray data in trans-criptome databases. The data collection is constantly expanding. Users can explore genes, gene networks, and behavioral phenotypes and their regulation by genetic polymorphisms. Tools for visualization of the output are a strong point. New user-generated recombinant inbred strain data can also be entered temporarily and analyzed. From output views, there is excellent connectivity to online resources for quick perusal of functions and other information on genes of interest (e.g., Gene Ontology Tree Machine, Ensembl, University of California in Santa Cruz Genome Browser, and PubMed). There is also a Links page for the exploration of both human and rodent gene data and their relatedness.
Navigation/Ease of Use
I found that the text and video tutorials (a Help feature) greatly facilitated my learning of the different parts and tools of the Web site. It is also a must to peruse the long list of phenotypes to gain familiarity prior to analyses. Without these aids, the user would have to spend more time navigating the site in order to gain some level of familiarity. Based on user feedback, ease of navigation is constantly being improved.
Recommendations
A site map would be helpful. Currently, efforts are under way to increase the number of pain traits and pain-related tissues included on the site. At present, WebQTL has rich genetic information and tools that can be used to explore brain gene networks, starting from genes related to pain. For example, one can determine that expression of Pomc mRNA is genetically correlated with expression of Oprm. The WebQTL site also has instructional value for those with some background learning about genetic variation in transcription and advanced genomic analyses of complex phenotypes (such as pain traits). A similar site, Mouse Phenome Database (www.jax.org/phenome), is richer in pain phenotypes but has fewer utilities for multivariate analysis and QTL mapping. It does feature genetic correlation analysis from individual observations, however.
William R. Lariviere, PhD, is Assistant Professor at University of Pittsburgh Medical Center, Pittsburg, PA.
Please direct your comments or suggestions for future Web Site Reviews to Michael E. Clark, PhD, Department Editor, at michaeleclark2@msn.com