Dear all, I have a script that generates a large number of gene sets (vectors of gene names) and would like to apply a functional analysis (e.g. how many transcription factors occur in each gene set? how many kinases... etc.). I can convert the gene names into different formats using biomaRt, however, the only functional analysis tools I have found in R apply an enrichment analysis on either GO or KEGG gene sets. Is there a package that allows me to answer more simple questions, e.g. just counting the number of transcription factors in a gene set by connecting to a public database? Many thanks, Rainer _________________________________________________ herausragenden Schutz gegen Massenmails. http://mail.yahoo.com

On Wed, Mar 31, 2010 at 8:29 AM, Rainer Tischler <rainer_t62 at="" yahoo.de=""> wrote: > Dear all, > > I have a script that generates a large number of gene sets (vectors of gene names) and would like to apply a functional analysis (e.g. how many transcription factors occur in each gene set? how many kinases... etc.). I can convert the gene names into different formats using biomaRt, however, the only functional analysis tools I have found in R apply an enrichment analysis on either GO or KEGG gene sets. Is there a package that allows me to answer more simple questions, e.g. just counting the number of transcription factors in a gene set by connecting to a public database? > Hi, Rainer. It will depend a bit, but for transcription factors, for example, I think it suffices to get the genes that are annotated with or descendents of GO:0003700 (transcription factor activity). Sean > library(org.Hs.eg.db) > egs = keys(org.Hs.egGO) > length(egs) [1] 45469 > randGenes = sample(egs,100) > tfGenes = get('GO:0003700',org.Hs.egGO2ALLEGS) > intersect(tfGenes,randGenes) > sessionInfo() R version 2.11.0 Under development (unstable) (2009-11-03 r50304) x86_64-apple-darwin locale: [1] en_US.UTF-8/en_US.UTF-8/C/C/en_US.UTF-8/en_US.UTF-8 attached base packages: [1] stats graphics grDevices datasets utils methods base other attached packages: [1] org.Hs.eg.db_2.4.0 RSQLite_0.8-4 DBI_0.2-5 [4] AnnotationDbi_1.9.6 Biobase_2.7.5 loaded via a namespace (and not attached): [1] tools_2.11.0

(My apologies if this is not the proper place for introducing a non-R-based tool) Rainer. We developed a very simple tool for grouping genes in base on their annotations, that can be useful for you. You just need a list of gene names and their functional annotations (sentences in free text or keywords of any type). Just paste the data as text-tabulated text in the area. The system compares the annotations and puts together those genes with similar words. It is independent on any external database. If your table contains logRatio measures for one or several microarray experiments, they will appear in the results as colored boxes (see example2 in the main page). Even if this tool will not fully solve your question, It is a extremely simple text-mining tool that helps the users finding functionally related genes on a list. It is up to the user to extract conclusions from the results. The system is called "Researcher's Digest": http://bioinfogp.cnb.csic.es/tools/digest I hope that helps. best, Juan Carlos Oliveros,Ph.D CNB-CSIC, Spain Quoting Rainer Tischler <rainer_t62@yahoo.de>: > Dear all, > > I have a script that generates a large number of gene sets (vectors > of gene names) and would like to apply a functional analysis (e.g. > how many transcription factors occur in each gene set? how many > kinases... etc.). I can convert the gene names into different > formats using biomaRt, however, the only functional analysis tools I > have found in R apply an enrichment analysis on either GO or KEGG > gene sets. Is there a package that allows me to answer more simple > questions, e.g. just counting the number of transcription factors in > a gene set by connecting to a public database? > > Many thanks, > Rainer > > _________________________________________________ >   herausragenden Schutz gegen Massenmails. > http://mail.yahoo.com > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > [[alternative HTML version deleted]]