Hi, I'm trying to take a list of genes and their attributes (e.g. exon count) and get average exon count for genes in the different top level GO categories (1 step below the MF, BP and CC top domain). After reading the maillists, vignettes and googling around I'm still baffled - and I thought this would be easy. I'm using biomaRt to get the GO id's but I think I need to "walk up" the GO graph and get the near top GO id. library("biomaRt") library(GO) mart <- useMart("ensembl", dataset = "hsapiens_gene_ensembl") go1 = getBM(attributes=c("ensembl_gene_id","go","evidence_code"),filte r="ensembl_gene_id",values=g2,mart=mart) goids=go1[,2] Now I have: >goids[1:10] [1] "GO:0000089" "GO:0000090" "GO:0000093" "GO:0003674" "GO:0005634" [6] "GO:0005813" "GO:0005819" "GO:0007049" "GO:0007093" "GO:0051301" > I guess I should filter them to get MF ids only, then walk up the graph and get the 2 level MF id using GOMFANCESTOR somehow (?) I hope somebody will hint me in the rigth direction or point to some examplified documentation - if it exists somewhere. Kind regards, Palle -- Palle Villesen Fredsted, Assoc. prof., Ph.D. Bioinformatics Research Center H. Guldbergs gade 10, build. 1090, DK-8000 Aarhus C Contact: +45 8942 3099 / +45 61708600 / www.birc.au.dk

Hi Palle, You can take a look at the package goTools. The main function ontoCompare takes lists of GO ids as argument (you will need to set the argument probeType="GO" in the function) and returns the number GO ids below top GO nodes. The default nodes are 1 step below MF, BP and CC, but you can modify them as needed. Best, Agnes ________________________________ From: bioconductor-bounces@stat.math.ethz.ch on behalf of Palle Villesen (BiRC) Sent: Fri 9/7/2007 2:02 AM To: bioconductor at stat.math.ethz.ch Subject: [BioC] Getting top level GO category for a list of genes Hi, I'm trying to take a list of genes and their attributes (e.g. exon count) and get average exon count for genes in the different top level GO categories (1 step below the MF, BP and CC top domain). After reading the maillists, vignettes and googling around I'm still baffled - and I thought this would be easy. I'm using biomaRt to get the GO id's but I think I need to "walk up" the GO graph and get the near top GO id. library("biomaRt") library(GO) mart <- useMart("ensembl", dataset = "hsapiens_gene_ensembl") go1 = getBM(attributes=c("ensembl_gene_id","go","evidence_code"),filte r="ensembl_gene_id",values=g2,mart=mart) goids=go1[,2] Now I have: >goids[1:10] [1] "GO:0000089" "GO:0000090" "GO:0000093" "GO:0003674" "GO:0005634" [6] "GO:0005813" "GO:0005819" "GO:0007049" "GO:0007093" "GO:0051301" > I guess I should filter them to get MF ids only, then walk up the graph and get the 2 level MF id using GOMFANCESTOR somehow (?) I hope somebody will hint me in the rigth direction or point to some examplified documentation - if it exists somewhere. Kind regards, Palle -- Palle Villesen Fredsted, Assoc. prof., Ph.D. Bioinformatics Research Center H. Guldbergs gade 10, build. 1090, DK-8000 Aarhus C Contact: +45 8942 3099 / +45 61708600 / www.birc.au.dk _______________________________________________ Bioconductor mailing list Bioconductor at stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/bioconductor Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor