Hi Marc, The code is working. I will have to include in the code to extract the IPI's in my list. Thanks, Viritha On Wed, Mar 9, 2011 at 8:30 PM, Marc Carlson <mcarlson@fhcrc.org> wrote: > Hi Viritha, > > The merge function is in base R. > > What is the output from your sessionInfo() > > > Marc > > > On 03/09/2011 01:19 PM, viritha kaza wrote: > > Hi Marc, > > Thanks for the explanation.I now realised that there can not be 1:1 > > relation between the IPI and the Entrez ids. Like how Dick was saying > > I used both the methods first by the site: > > ftp://ftp.ebi.ac.uk/pub/databases/IPI/current/ipi.HUMAN.xrefs.gz. > > and then the remaining with the code in biomart: > > as I had mentioned previously > > code: > > >source('http://bioconductor.org/biocLite.R') > > > biocLite("biomaRt") > > >library("biomaRt") > > >ensembl = useMart("ensembl", dataset = "hsapiens_gene_ensembl") > > >ipi=scan("ipi_test.txt",what =character(),sep='\n',quote="") > > # ipi_test file contained the IPI's that I wanted to convert > > >getBM(attributes = > > c("ipi","entrezgene","hgnc_symbol"),filters="ipi",values=ipi,mart = > > ensembl) > > >write.table(ipi_entrez,"ipi_entrez_test.txt",sep='\t') > > > > I could not find the merge method of yours which dick was mentioning. > > For now I am only interested in the entrez id and genesymbol only for > > the respective IPI in my list. > > Is there an easy method for this and if they could share it, then it > > would be excellent. > > Thanks, > > Viritha > > [[alternative HTML version deleted]]

Hi Marc, Thanks for the good explanation. I do want the deprecated IPIs (or get the proteomics folks I work with to update things at their end more frequently?). Maybe it would be cleaner to just massage the IPIs I get so as to change deprecated IPIs to current ones, then use the merged GO and PROSITE table. I'll have to play around with that and see. I fuss around a lot trying to get every possible Entrez Gene ID for the IPIs I deal with. So when I get Entrez Gene IDs for a subset of a list of IPIs, I go through several extra steps (biomaRt etc) to try and find Entrez Gene IDs for the IPIs that are missing them. Thanks very much, Dick ********************************************************************** ********* Richard P. Beyer, Ph.D. University of Washington Tel.:(206) 616 7378 Env. & Occ. Health Sci. , Box 354695 Fax: (206) 685 4696 4225 Roosevelt Way NE, # 100 Seattle, WA 98105-6099 http://depts.washington.edu/ceeh/members_fc_bioinfo.html http://staff.washington.edu/~dbeyer ********************************************************************** ********* On Thu, 10 Mar 2011, Marc Carlson wrote: > Hi Dick, > > So lets look at that example that you gave in your last post where you > merged the GO table with the PROSITE one. It is important to understand > that when you call merge(), it does it's magic by basically performing > an inner join on the two tables that comprise it's 1st two first > arguments. Therefore the mechanics of how that merge will do its job > mean that you have effectively restricted the results to only those > entrez genes where you have BOTH a GO annotation AND a PROSITE annotation. > > So the result you see (more EGs from the bigger join) would happen if > for example you had increased the size of the IPI tables (by pairing up > some deprecated IPI ids with some legitimate entrez gene IDs). In this > situation, these entrez gene IDs would be perfectly legitimate, but > their IPI IDs would all be older deprecated IPI ids. I am not sure if > that is what you really want or not, but if it is, then the final table > indeed would be bigger. > > > Hope that clarifies things, > > > Marc > > > > > > On 03/10/2011 07:16 AM, Dick Beyer wrote: >> Hi Marc, >> >> Thanks very much for the merge example. That's so much cleaner than my usual approach. >> >> As far as the differing numbers of IPIs, I agree that using the AC field gives a lot more IPIs than just using the ID field. I guess I'm trying to solve a different problem than other folks. I get these lists of IPIs from proteomics folks, and some of the IPIs wouldn't show up in the ID field of "ftp://ftp.ebi.ac.uk/pub/databases/IPI/current/ipi.HUMAN.dat.gz", but are present in the AC field because they are no longer the current primary identifier (http://www.ebi.ac.uk/IPI/Algorithm.html#SECONDARIES). What I am really after is the Entrez Gene ID, so for me, the IPI is just a pointer to the EG. >> >> However, with this approach: >> >> library(org.Hs.eg.db) >> allAnnots <- merge(toTable(org.Hs.egPROSITE), toTable(org.Hs.egGO), by.x="gene_id", by.y="gene_id") >> >> I get several thousand fewer EGs than using either ipi.HUMAN.dat or ipi.HUMAN.xrefs. I looked at a few of the EGs that I get out of these that are not in org.Hs.eg.db, and they seem valid. I'll do some more checking to be sure. In ipi.HUMAN.xrefs I get 22113 unique EGs and 86719 unique IPIs from a download 2 days ago from "ftp://ftp.ebi.ac.uk/pub/databases/IPI/current/ipi.HUMAN.xrefs.gz" >> >> What I had hoped I was creating was a table with any and all IPIs (primary and secondary) but each with valid EGs. >> >> Thanks very much for your great help, >> Dick >> ------------------------------ >> >> Message: 24 >> Date: Wed, 09 Mar 2011 17:25:34 -0800 >> From: Marc Carlson <mcarlson at="" fhcrc.org=""> >> To: bioconductor at r-project.org >> Subject: Re: [BioC] IPI to entrez id >> Message-ID: <4D78288E.6060904 at fhcrc.org> >> Content-Type: text/plain; charset=ISO-8859-1 >> >> Hi Dick, >> >> Is there any reason why something like this won't work for you to attach >> the GO Ids? >> >> merge(dat.all, toTable(org.Hs.egGO), by.x="EG", by.y="gene_id") >> >> >> >> When I build the org.Hs.eg.db package, I download the IPI Ids in the >> mySQL database from the following source: >> >> ftp://ftp.ebi.ac.uk/pub/databases/IPI/current >> >> >> The most recent time I did this (last week) results in an ipi to gene >> table that contains only 86719 unique IPIs. And that number drops a bit >> when I fold the IPI IDs into the org database (which takes as its set of >> unique entrez gene IDs the ones that are currently listed at NCBI) to >> 77387 distinct IPI IDs. And if you merge with a GO table, I expect that >> it will drop a bit more. >> >> In looking at the file that you are parsing, I get exactly the same >> number of unique IPI ids if I extract from the ID fields and match to >> the Entrez Gene fields (which also gives me the exact same number of >> entrez gene IDs). I can only get the huge additional number of IPI Ids >> from this file if I also mine the AC field and assume that these IPI Ids >> also should map to the exact same things. But the direct database dump >> from EBI does not give me these mappings. In fact, it does not seem to >> even contain them. This causes me to be concerned that maybe these IDs >> may not what you think they are? >> >> >> Anyhow, I hope this helps, >> >> >> Marc >> >> >> >> >> On 03/08/2011 11:39 AM, Dick Beyer wrote: >> Hi to all, >> >> For several years now, I have been doing GO analysis on lists of >> proteins derived from MS. I am given IPIs by the proteomics folks and >> need the corresponding Entrez Gene IDs. Putting aside the issues of >> non-unique mapping from IPI to EG, isoforms, etc., I was wondering if >> anyone would comment on my method of getting the Entrez Gene IDs. I'd >> really like to use Marc Carlson's merge method (shown below), but that >> approach seems to miss several thousand IPI/EG matches that my method >> finds. >> >> I start with >> ftp://ftp.ebi.ac.uk/pub/databases/IPI/current/ipi.HUMAN.dat.gz, and >> extract a subset of the rows: >> >> ipiHUMAN <- readLines(con = "ipi.HUMAN.3.80.dat",n=-1) # build 11feb2011 >> dbfetch.all <- ipiHUMAN >> rm(ipiHUMAN) >> >> # Explanation of the data format is found here >> # http://www.ebi.ac.uk/2can/tutorials/formats.html#swiss >> >> length(dbfetch.all) # 3180244 >> length(eg <- grep("^DR Entrez Gene", dbfetch.all)) # 80296 >> length(ids <- grep("^ID", dbfetch.all)) # 86719 >> length(de <- grep("^DE", dbfetch.all)) # 92454 >> length(ac <- grep("^AC", dbfetch.all)) # 93720 >> length(ug <- grep("^DR UniGene", dbfetch.all)) # 88314 >> length(up <- grep("^DR UniProtKB", dbfetch.all)) # 110593 >> length(en <- grep("^DR ENSEMBL", dbfetch.all)) # 77340 >> length(rs <- grep("^DR REFSEQ_REVIEWED",dbfetch.all)) # 14559 >> >> and eventually turn this into a data.frame with the columns: >> >> "IPI","EG","GeneSymbol","UniGene","UniProtKB","ENSEMBL","REFSEQ_REV IEWED" >> >> (Note: Not every IPI entry has every field) >> >> For this build of the IPI file, my data.frame ends up as >> dim(dat.all) >> [1] 183153 7 >> >> Of these 183153 IPIs, there are 171909 unique IPIs, and 22342 unique >> Entrez Gene IDs. >> >> The merge method shown below from Marc Carlson gives 69315 unique IPIs >> and 17783 unique Entrez Gene IDs (you get the same numbers whether you >> use org.Hs.egGO2ALLEGS or org.Hs.egGO). >> >> When I build my 7 column data.frame, I initially get 22305 unique >> Entrez Gene IDs, and I then go through some additional steps of trying >> to fill in the missing EGs. I do this by taking the IPIs with no EGs, >> and using biomaRt with UniGene, UniProtKB etc as inputs to getBM(), >> and hope I get a few more EGs. >> >> For example: >> >> library(biomaRt) >> mart <- useMart( "ensembl", dataset="hsapiens_gene_ensembl") >> length(whichis.na(dat.all[,4]))) >> sum(z <- !is.na(dat.all[,4])) >> w <- >> getBM(attributes=c("entrezgene","unigene","hgnc_symbol","descriptio n"),filters="unigene",values=dat.all[z,4], >> mart=mart) >> >> By doing several of these getBM() steps, I add 37 more EGs! >> >> My method is long and painful. That merge approach is clean and >> beautiful. >> >> Is there a way to add to the merge argument or something that would >> give me the additional 100K+ IPIs and 4500+ EGs? >> >> ------------------------------ >> Message: 20 >> Date: Fri, 18 Feb 2011 13:17:18 -0800 >> From: "Carlson, Marc R" <mcarlson at="" fhcrc.org=""> >> To: <bioconductor at="" stat.math.ethz.ch=""> >> Subject: Re: [BioC] IPI to entrez id >> Message-ID: >> <1688456294.5987.1298063838120.JavaMail.root at zimbra4.fhcrc.org> >> Content-Type: text/plain; charset="utf-8" >> >> Hi Viritha, >> >> These things can never be 1:1, but you can pretty easily just cram >> them all into a huge data.frame by doing this: >> >> library(org.Hs.eg.db) >> allAnnots <- merge(toTable(org.Hs.egPROSITE), toTable(org.Hs.egGO), >> by.x="gene_id", by.y="gene_id") >> head(allAnnots) >> >> Once you have done this, you may notice that they are not only are >> these things almost never (if ever) 1:1, but that this could have been >> even worse if I had used the GO2ALL mappings (and I probably should >> have, but I can't really tell because I have almost no information >> about what you want to do). Anyhow, I hope this helps you, but if you >> have a more specific use for this information that you are willing to >> talk about then we might be able to give you a better answer. >> >> >> Marc >> ------------------------------ >> >> Thanks very much, >> Dick >> ******************************************************************* ************ >> >> Richard P. Beyer, Ph.D. University of Washington >> Tel.:(206) 616 7378 Env. & Occ. Health Sci. , Box 354695 >> Fax: (206) 685 4696 4225 Roosevelt Way NE, # 100 >> Seattle, WA 98105-6099 >> http://depts.washington.edu/ceeh/members_fc_bioinfo.html >> http://staff.washington.edu/~dbeyer >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor at r-project.org >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: >> http://news.gmane.org/gmane.science.biology.informatics.conductor >> >> >> >> ******************************************************************* ************ >> Richard P. Beyer, Ph.D. University of Washington >> Tel.:(206) 616 7378 Env. & Occ. Health Sci. , Box 354695 >> Fax: (206) 685 4696 4225 Roosevelt Way NE, # 100 >> Seattle, WA 98105-6099 >> http://depts.washington.edu/ceeh/members_fc_bioinfo.html >> http://staff.washington.edu/~dbeyer >> ******************************************************************* ************ >> >> >> > >