GenCLiP: a software program for clustering gene lists by literature profiling and constructing gene co-occurrence networks related to custom keywords
1 Cancer Institute, Southern Medical University, Guangzhou, 510515, PR China
2 Experiment Center, School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, PR China
3 Department of Plastic Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, PR China
4 Cancer Research Institute, Central South University, Changsha, 410078, PR China
BMC Bioinformatics 2008, 9:308 doi:10.1186/1471-2105-9-308Published: 13 July 2008
Biomedical researchers often want to explore pathogenesis and pathways regulated by abnormally expressed genes, such as those identified by microarray analyses. Literature mining is an important way to assist in this task. Many literature mining tools are now available. However, few of them allows the user to make manual adjustments to zero in on what he/she wants to know in particular.
We present our software program, GenCLiP (Gene Cluster with Literature Profiles), which is based on the methods presented by Chaussabel and Sher (Genome Biol 2002, 3(10):RESEARCH0055) that search gene lists to identify functional clusters of genes based on up-to-date literature profiling. Four features were added to this previously described method: the ability to 1) manually curate keywords extracted from the literature, 2) search genes and gene co-occurrence networks related to custom keywords, 3) compare analyzed gene results with negative and positive controls generated by GenCLiP, and 4) calculate probabilities that the resulting genes and gene networks are randomly related. In this paper, we show with a set of differentially expressed genes between keloids and normal control, how implementation of functions in GenCLiP successfully identified keywords related to the pathogenesis of keloids and unknown gene pathways involved in the pathogenesis of keloids.
With regard to the identification of disease-susceptibility genes, GenCLiP allows one to quickly acquire a primary pathogenesis profile and identify pathways involving abnormally expressed genes not previously associated with the disease.