Hi Michael, > Again, I ask, does anyone have any simple examples of going from a list > of LocusLink IDs to a list of GO Terms? (i.e. GO identifiers and the > biological function/term associated with those identifiers) I think the environment "GOLOCUSID2GO" in the GO package is what you are looking for. Use ?GOLOCUSID2GO to see the examples. HTH, Ting ______________________________________ Ting-Yuan Liu Program in Computational Biology Division of Public Health Sciences Fred Hutchinson Cancer Research Center Seattle, WA, USA ______________________________________ > > Many thanks > Mick > > -----Original Message----- > From: Sean Davis [mailto:sdavis2 at mail.nih.gov] > Sent: 01 March 2006 11:44 > To: michael watson (IAH-C); Bioconductor > Subject: Re: [BioC] Quick start to linking GO terms and microarray data > > > > > On 3/1/06 6:20 AM, "michael watson (IAH-C)" <michael.watson at="" bbsrc.ac.uk=""> > wrote: > > > Hi > > > > I want to investigate the GO terms associated with my microarray data > > (normally, a list of genes from topTable() in limma) > > > > I have read the vignettes for goTools and GOStats, and to be honest, I > > am still a little unclear what the overall process is, particularly if > I > > am working with a custom array and not with affy or operon. > > > > Lets say, for example, I have my array data in a data.frame containing > > gene names. In a separate data frame I have a link between my gene > > names and LocusLink IDs. How do I: > > > > 1) Find the GO terms associated with subsets of my genes? (I realise I > > can use merge() to link my array data to the LocusLink ids, but what > do > > I do then?) > > > > 2) Fins out if a particular GO term is statistically over- represented > in > > a particular group > > Hi, Mick. > > I would take your locuslink IDs for your genes and dump out two lists to > a > text file: > > 1) All LocusIDs on your array. > 2) All LoucsIDs in your genelist. > > Then use an external program or web tool such as DAVID/EASE to do the > analysis. > > That said, there was some discussion on using straight locusIDs (rather > than > requiring a metadata package) in GOHyperG. I don't know where that > conversion stands. > > As to your question about linking genes to GO, that is actually done at > the > transcript/protein level. Merging to entrez gene (locuslink) happens > after > the fact. Using various data sources, you can link by refseq, > locuslink, > ensembl ids, ucsc knowngenes, human invitational ids (human), and > probably > several others in species other than human. > > Sean > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor >

Hi Mike, As Wolfgang already suggested you can do this with the biomaRt package. Here is how should do this: > library(biomaRt) Loading required package: XML Loading required package: RCurl > mart = useMart("ensembl",dataset="hsapiens_gene_ensembl") Checking attributes and filters ... ok > getGO(id=c(100,620),type="entrezgene",mart=mart) go_id go_description evidence_code 1 GO:0004000 adenosine deaminase activity TAS 2 GO:0016787 hydrolase activity IEA 3 GO:0009117 nucleotide metabolism IEA 4 GO:0009168 purine ribonucleoside monophosphate biosynthesis IEA 5 GO:0019735 antimicrobial humoral response (sensu Vertebrata) TAS 6 GO:0006955 immune response IMP 7 GO:0006955 immune response IEA 8 GO:0006163 purine nucleotide metabolism IMP 9 GO:0006163 purine nucleotide metabolism IEA 10 GO:0005737 cytoplasm IDA 11 GO:0005737 cytoplasm IEA ensembl_gene_id ensembl_transcript_id 1 ENSG00000196839 ENST00000359372 2 ENSG00000196839 ENST00000359372 3 ENSG00000196839 ENST00000359372 4 ENSG00000196839 ENST00000359372 5 ENSG00000196839 ENST00000359372 6 ENSG00000196839 ENST00000359372 7 ENSG00000196839 ENST00000359372 8 ENSG00000196839 ENST00000359372 9 ENSG00000196839 ENST00000359372 10 ENSG00000196839 ENST00000359372 11 ENSG00000196839 ENST00000359372 best, Steffen michael watson (IAH-C) wrote: >Thanks Sean, but I really wanted to demonstrate this in Bioconductor :-S > >I tried running the vignettes in goTools, the first time it froze up my >PC for about 30 minutes and then gave out a cryptic message about >coercing x to a list, the second time it froze up my PC and then R >crashed with no warning :-S > >As far as I can tell, GOStats doesn't have any clear examples of simple >mapping of microarray data to GO terms. > >Given that one of the major, fundamental tasks biologists want to do is >find out functional information for significantly differentailly >expressed genes, shouldn't this be a little easier, and a little more >transparent, in bioconductor? > >Again, I ask, does anyone have any simple examples of going from a list >of LocusLink IDs to a list of GO Terms? (i.e. GO identifiers and the >biological function/term associated with those identifiers) > >Many thanks >Mick > >-----Original Message----- >From: Sean Davis [mailto:sdavis2 at mail.nih.gov] >Sent: 01 March 2006 11:44 >To: michael watson (IAH-C); Bioconductor >Subject: Re: [BioC] Quick start to linking GO terms and microarray data > > > > >On 3/1/06 6:20 AM, "michael watson (IAH-C)" <michael.watson at="" bbsrc.ac.uk=""> >wrote: > > > >>Hi >> >>I want to investigate the GO terms associated with my microarray data >>(normally, a list of genes from topTable() in limma) >> >>I have read the vignettes for goTools and GOStats, and to be honest, I >>am still a little unclear what the overall process is, particularly if >> >> >I > > >>am working with a custom array and not with affy or operon. >> >>Lets say, for example, I have my array data in a data.frame containing >>gene names. In a separate data frame I have a link between my gene >>names and LocusLink IDs. How do I: >> >>1) Find the GO terms associated with subsets of my genes? (I realise I >>can use merge() to link my array data to the LocusLink ids, but what >> >> >do > > >>I do then?) >> >>2) Fins out if a particular GO term is statistically over- represented >> >> >in > > >>a particular group >> >> > >Hi, Mick. > >I would take your locuslink IDs for your genes and dump out two lists to >a >text file: > >1) All LocusIDs on your array. >2) All LoucsIDs in your genelist. > >Then use an external program or web tool such as DAVID/EASE to do the >analysis. > >That said, there was some discussion on using straight locusIDs (rather >than >requiring a metadata package) in GOHyperG. I don't know where that >conversion stands. > >As to your question about linking genes to GO, that is actually done at >the >transcript/protein level. Merging to entrez gene (locuslink) happens >after >the fact. Using various data sources, you can link by refseq, >locuslink, >ensembl ids, ucsc knowngenes, human invitational ids (human), and >probably >several others in species other than human. > >Sean > >_______________________________________________ >Bioconductor mailing list >Bioconductor at stat.math.ethz.ch >https://stat.ethz.ch/mailman/listinfo/bioconductor > > >

Hi, Next to Ensembl, biomaRt currently includes Wormbase, VEGA, Uniprot and msd. Soon I expect plants to be represented as well via the Gramene database (http://www.gramene.org). Best, Steffen michael watson (IAH-C) wrote: >Hi Steffen, Wolfgang > >Thanks a lot, the biomaRt package looks wonderful for the species that >are in ensembl... Are there any functions within it to annotate other >species? (Eg bacteria, plants etc) > >Many thanks >Mick > >-----Original Message----- >From: Steffen Durinck [mailto:sdurinck at ebi.ac.uk] >Sent: 01 March 2006 13:24 >To: michael watson (IAH-C) >Cc: Sean Davis; Bioconductor >Subject: Re: [BioC] Quick start to linking GO terms and microarray data > >Hi Mike, > >As Wolfgang already suggested you can do this with the biomaRt package. >Here is how should do this: > > > library(biomaRt) >Loading required package: XML >Loading required package: RCurl > > mart = useMart("ensembl",dataset="hsapiens_gene_ensembl") >Checking attributes and filters ... ok > > getGO(id=c(100,620),type="entrezgene",mart=mart) > > go_id go_description >evidence_code >1 GO:0004000 adenosine deaminase >activity TAS >2 GO:0016787 hydrolase >activity IEA >3 GO:0009117 nucleotide >metabolism IEA >4 GO:0009168 purine ribonucleoside monophosphate >biosynthesis IEA >5 GO:0019735 antimicrobial humoral response (sensu >Vertebrata) TAS >6 GO:0006955 immune >response IMP >7 GO:0006955 immune >response IEA >8 GO:0006163 purine nucleotide >metabolism IMP >9 GO:0006163 purine nucleotide >metabolism IEA >10 GO:0005737 >cytoplasm IDA >11 GO:0005737 >cytoplasm IEA > ensembl_gene_id ensembl_transcript_id >1 ENSG00000196839 ENST00000359372 >2 ENSG00000196839 ENST00000359372 >3 ENSG00000196839 ENST00000359372 >4 ENSG00000196839 ENST00000359372 >5 ENSG00000196839 ENST00000359372 >6 ENSG00000196839 ENST00000359372 >7 ENSG00000196839 ENST00000359372 >8 ENSG00000196839 ENST00000359372 >9 ENSG00000196839 ENST00000359372 >10 ENSG00000196839 ENST00000359372 >11 ENSG00000196839 ENST00000359372 > > >best, >Steffen > >michael watson (IAH-C) wrote: > > > >>Thanks Sean, but I really wanted to demonstrate this in Bioconductor >> >> >:-S > > >>I tried running the vignettes in goTools, the first time it froze up my >>PC for about 30 minutes and then gave out a cryptic message about >>coercing x to a list, the second time it froze up my PC and then R >>crashed with no warning :-S >> >>As far as I can tell, GOStats doesn't have any clear examples of simple >>mapping of microarray data to GO terms. >> >>Given that one of the major, fundamental tasks biologists want to do is >>find out functional information for significantly differentailly >>expressed genes, shouldn't this be a little easier, and a little more >>transparent, in bioconductor? >> >>Again, I ask, does anyone have any simple examples of going from a list >>of LocusLink IDs to a list of GO Terms? (i.e. GO identifiers and the >>biological function/term associated with those identifiers) >> >>Many thanks >>Mick >> >>-----Original Message----- >>From: Sean Davis [mailto:sdavis2 at mail.nih.gov] >>Sent: 01 March 2006 11:44 >>To: michael watson (IAH-C); Bioconductor >>Subject: Re: [BioC] Quick start to linking GO terms and microarray data >> >> >> >> >>On 3/1/06 6:20 AM, "michael watson (IAH-C)" >> >> ><michael.watson at="" bbsrc.ac.uk=""> > > >>wrote: >> >> >> >> >> >>>Hi >>> >>>I want to investigate the GO terms associated with my microarray data >>>(normally, a list of genes from topTable() in limma) >>> >>>I have read the vignettes for goTools and GOStats, and to be honest, I >>>am still a little unclear what the overall process is, particularly if >>> >>> >>> >>> >>I >> >> >> >> >>>am working with a custom array and not with affy or operon. >>> >>>Lets say, for example, I have my array data in a data.frame containing >>>gene names. In a separate data frame I have a link between my gene >>>names and LocusLink IDs. How do I: >>> >>>1) Find the GO terms associated with subsets of my genes? (I realise I >>>can use merge() to link my array data to the LocusLink ids, but what >>> >>> >>> >>> >>do >> >> >> >> >>>I do then?) >>> >>>2) Fins out if a particular GO term is statistically over- represented >>> >>> >>> >>> >>in >> >> >> >> >>>a particular group >>> >>> >>> >>> >>Hi, Mick. >> >>I would take your locuslink IDs for your genes and dump out two lists >> >> >to > > >>a >>text file: >> >>1) All LocusIDs on your array. >>2) All LoucsIDs in your genelist. >> >>Then use an external program or web tool such as DAVID/EASE to do the >>analysis. >> >>That said, there was some discussion on using straight locusIDs (rather >>than >>requiring a metadata package) in GOHyperG. I don't know where that >>conversion stands. >> >>As to your question about linking genes to GO, that is actually done at >>the >>transcript/protein level. Merging to entrez gene (locuslink) happens >>after >>the fact. Using various data sources, you can link by refseq, >>locuslink, >>ensembl ids, ucsc knowngenes, human invitational ids (human), and >>probably >>several others in species other than human. >> >>Sean >> >>_______________________________________________ >>Bioconductor mailing list >>Bioconductor at stat.math.ethz.ch >>https://stat.ethz.ch/mailman/listinfo/bioconductor >> >> >> >> >> > > > > >

> > michael watson (IAH-C) wrote: > >> Hi Steffen, Wolfgang >> >> Thanks a lot, the biomaRt package looks wonderful for the species that >> are in ensembl... Are there any functions within it to annotate other >> species? (Eg bacteria, plants etc) Mick, This is a quick-and-dirty solution that will get you whatever NCBI has available for gene ontology, including arabidopsis, for example. Hope this gets you another few species. The species IDs included are: > unique(gene2go$taxID) [1] 3702 4932 6239 7227 7955 9031 9606 10090 10116 36329 [11] 39947 83333 185431 195099 198094 211586 214684 223283 243164 243231 [21] 243233 246200 265669 284812 Hope this helps. Sean > download.file('ftp://ftp.ncbi.nih.gov/gene/DATA/gene2go.gz', destfile='gene2go.gz') trying URL 'ftp://ftp.ncbi.nih.gov/gene/DATA/gene2go.gz' ftp data connection made, file length 5541317 bytes opened URL ================================================== downloaded 5411Kb > gene2go <- read.table(gzfile('gene2go.gz'),sep="\t",header=FALSE,quote="") > colnames(gene2go) <- c('taxID', 'geneID', 'goID', 'evidence', 'qualifier', 'goTerm', 'pubmedlist') > gene2go[match(1:10,gene2go$geneID),] taxID geneID goID evidence qualifier 272227 9606 1 GO:0000004 ND 272230 9606 2 GO:0004867 IEA NA NA NA <na> <na> <na> NA.1 NA NA <na> <na> <na> NA.2 NA NA <na> <na> <na> NA.3 NA NA <na> <na> <na> NA.4 NA NA <na> <na> <na> NA.5 NA NA <na> <na> <na> 272240 9606 9 GO:0004060 TAS 272244 9606 10 GO:0004060 TAS goTerm pubmedlist 272227 biological process unknown - 272230 serine-type endopeptidase inhibitor activity - NA <na> <na> NA.1 <na> <na> NA.2 <na> <na> NA.3 <na> <na> NA.4 <na> <na> NA.5 <na> <na> 272240 arylamine N-acetyltransferase activity 10908296 272244 arylamine N-acetyltransferase activity 2340091 # and an example from A. thaliana # the GO for A. thaliana is from TAIR > gene2go[match(819280,gene2go$geneID),] taxID geneID goID evidence qualifier goTerm 12430 3702 819280 GO:0003700 ISS transcription factor activity pubmedlist 12430 7948864

Hi Mick, The biomaRt package can retrieve data from BioMart data management systems (see: http://www.biomart.org). Any database that provides such a BioMart implementation can thus be queried. Ensembl and Wormbase for example provide this and are queried in real-time through biomaRt. For species that are not in these systems, the biomaRt package can not provide help unless a local BioMart for this species is set up or you can try to convince the database of interest to include a BioMart system. I expect plants and fly to be included soon but have no information on other species. Best, Steffen michael watson (IAH-C) wrote: >Hi Steffen > >Sorry if I am confused, but getGO() seems to require a connection to an >ensembl database. If I have identifiers for a species that is not in >ensembl, can I still use biomaRt to retrieve GO (and other) annotations? > >If so, it is a little unclear how to do this from the vignettes :-S > >Thank you for the help > >Mick > >-----Original Message----- >From: Steffen Durinck [mailto:sdurinck at ebi.ac.uk] >Sent: 01 March 2006 13:43 >To: michael watson (IAH-C) >Cc: Bioconductor >Subject: Re: [BioC] Quick start to linking GO terms and microarray data > >Hi, > >Next to Ensembl, biomaRt currently includes Wormbase, VEGA, Uniprot and >msd. >Soon I expect plants to be represented as well via the Gramene database >(http://www.gramene.org). > >Best, >Steffen > > >michael watson (IAH-C) wrote: > > > >>Hi Steffen, Wolfgang >> >>Thanks a lot, the biomaRt package looks wonderful for the species that >>are in ensembl... Are there any functions within it to annotate other >>species? (Eg bacteria, plants etc) >> >>Many thanks >>Mick >> >>-----Original Message----- >>From: Steffen Durinck [mailto:sdurinck at ebi.ac.uk] >>Sent: 01 March 2006 13:24 >>To: michael watson (IAH-C) >>Cc: Sean Davis; Bioconductor >>Subject: Re: [BioC] Quick start to linking GO terms and microarray data >> >>Hi Mike, >> >>As Wolfgang already suggested you can do this with the biomaRt package. >>Here is how should do this: >> >> >> >>>library(biomaRt) >>> >>> >>Loading required package: XML >>Loading required package: RCurl >> >> >>>mart = useMart("ensembl",dataset="hsapiens_gene_ensembl") >>> >>> >>Checking attributes and filters ... ok >> >> >>>getGO(id=c(100,620),type="entrezgene",mart=mart) >>> >>> >> go_id go_description >>evidence_code >>1 GO:0004000 adenosine deaminase >>activity TAS >>2 GO:0016787 hydrolase >>activity IEA >>3 GO:0009117 nucleotide >>metabolism IEA >>4 GO:0009168 purine ribonucleoside monophosphate >>biosynthesis IEA >>5 GO:0019735 antimicrobial humoral response (sensu >>Vertebrata) TAS >>6 GO:0006955 immune >>response IMP >>7 GO:0006955 immune >>response IEA >>8 GO:0006163 purine nucleotide >>metabolism IMP >>9 GO:0006163 purine nucleotide >>metabolism IEA >>10 GO:0005737 >>cytoplasm IDA >>11 GO:0005737 >>cytoplasm IEA >> ensembl_gene_id ensembl_transcript_id >>1 ENSG00000196839 ENST00000359372 >>2 ENSG00000196839 ENST00000359372 >>3 ENSG00000196839 ENST00000359372 >>4 ENSG00000196839 ENST00000359372 >>5 ENSG00000196839 ENST00000359372 >>6 ENSG00000196839 ENST00000359372 >>7 ENSG00000196839 ENST00000359372 >>8 ENSG00000196839 ENST00000359372 >>9 ENSG00000196839 ENST00000359372 >>10 ENSG00000196839 ENST00000359372 >>11 ENSG00000196839 ENST00000359372 >> >> >>best, >>Steffen >> >>michael watson (IAH-C) wrote: >> >> >> >> >> >>>Thanks Sean, but I really wanted to demonstrate this in Bioconductor >>> >>> >>> >>> >>:-S >> >> >> >> >>>I tried running the vignettes in goTools, the first time it froze up >>> >>> >my > > >>>PC for about 30 minutes and then gave out a cryptic message about >>>coercing x to a list, the second time it froze up my PC and then R >>>crashed with no warning :-S >>> >>>As far as I can tell, GOStats doesn't have any clear examples of >>> >>> >simple > > >>>mapping of microarray data to GO terms. >>> >>>Given that one of the major, fundamental tasks biologists want to do >>> >>> >is > > >>>find out functional information for significantly differentailly >>>expressed genes, shouldn't this be a little easier, and a little more >>>transparent, in bioconductor? >>> >>>Again, I ask, does anyone have any simple examples of going from a >>> >>> >list > > >>>of LocusLink IDs to a list of GO Terms? (i.e. GO identifiers and the >>>biological function/term associated with those identifiers) >>> >>>Many thanks >>>Mick >>> >>>-----Original Message----- >>>From: Sean Davis [mailto:sdavis2 at mail.nih.gov] >>>Sent: 01 March 2006 11:44 >>>To: michael watson (IAH-C); Bioconductor >>>Subject: Re: [BioC] Quick start to linking GO terms and microarray >>> >>> >data > > >>> >>> >>>On 3/1/06 6:20 AM, "michael watson (IAH-C)" >>> >>> >>> >>> >><michael.watson at="" bbsrc.ac.uk=""> >> >> >> >> >>>wrote: >>> >>> >>> >>> >>> >>> >>> >>>>Hi >>>> >>>>I want to investigate the GO terms associated with my microarray data >>>>(normally, a list of genes from topTable() in limma) >>>> >>>>I have read the vignettes for goTools and GOStats, and to be honest, >>>> >>>> >I > > >>>>am still a little unclear what the overall process is, particularly >>>> >>>> >if > > >>>> >>>> >>>> >>>> >>>> >>>> >>>I >>> >>> >>> >>> >>> >>> >>>>am working with a custom array and not with affy or operon. >>>> >>>>Lets say, for example, I have my array data in a data.frame >>>> >>>> >containing > > >>>>gene names. In a separate data frame I have a link between my gene >>>>names and LocusLink IDs. How do I: >>>> >>>>1) Find the GO terms associated with subsets of my genes? (I realise >>>> >>>> >I > > >>>>can use merge() to link my array data to the LocusLink ids, but what >>>> >>>> >>>> >>>> >>>> >>>> >>>do >>> >>> >>> >>> >>> >>> >>>>I do then?) >>>> >>>>2) Fins out if a particular GO term is statistically over- represented >>>> >>>> >>>> >>>> >>>> >>>> >>>in >>> >>> >>> >>> >>> >>> >>>>a particular group >>>> >>>> >>>> >>>> >>>> >>>> >>>Hi, Mick. >>> >>>I would take your locuslink IDs for your genes and dump out two lists >>> >>> >>> >>> >>to >> >> >> >> >>>a >>>text file: >>> >>>1) All LocusIDs on your array. >>>2) All LoucsIDs in your genelist. >>> >>>Then use an external program or web tool such as DAVID/EASE to do the >>>analysis. >>> >>>That said, there was some discussion on using straight locusIDs >>> >>> >(rather > > >>>than >>>requiring a metadata package) in GOHyperG. I don't know where that >>>conversion stands. >>> >>>As to your question about linking genes to GO, that is actually done >>> >>> >at > > >>>the >>>transcript/protein level. Merging to entrez gene (locuslink) happens >>>after >>>the fact. Using various data sources, you can link by refseq, >>>locuslink, >>>ensembl ids, ucsc knowngenes, human invitational ids (human), and >>>probably >>>several others in species other than human. >>> >>>Sean >>> >>>_______________________________________________ >>>Bioconductor mailing list >>>Bioconductor at stat.math.ethz.ch >>>https://stat.ethz.ch/mailman/listinfo/bioconductor >>> >>> >>> >>> >>> >>> >>> >> >> >> >> >> > > > > >