In the following two unrelated messages, both Sean and Nianhua suggested to download and parse some data tables from the NCBI. The gene_info and several other tables seems very useful. If that is the case, why not have it pre-loaded into a SQlite and distribute it as part of the annotation package for human? Simon ================= Date: Tue, 12 Jun 2007 05:59:55 -0400 From: Sean Davis <sdavis2 at="" mail.nih.gov=""> Subject: Re: [BioC] from RefSeq GI protein identifiers to GO terms To: Lina Hultin-Rosenberg <lina.hultin-rosenberg at="" ki.se=""> Cc: bioconductor at stat.math.ethz.ch Message-ID: <466E6E9B.3020609 at mail.nih.gov> Content-Type: text/plain; charset=ISO-8859-1 Lina Hultin-Rosenberg wrote: >> Dear list, >> >> This might be a question that has been discussed previously but I could not >> find any good solution for it. I have lists of human proteins from various >> proteomics studies that I want to compare with regards to the GO terms >> associated to them. I have the RefSeq GI protein id for the proteins and my >> questions is how I best map those to other identifiers that I can use in >> subsequent GO analysis? >> >> It might be that this problem is solved best outside R but maybe someone >> still can give me a hint to the best solution. For me this is a problem that >> comes up quite often - the need to map between different identifiers - and I >> have not yet find any really good solution to it. If I for example use IPI I >> always loose some proteins/genes since the coverage is rather bad, but maybe >> there is no solution that will give perfect mapping?! > > The file located here: ftp://ftp.ncbi.nih.gov/gene/DATA/gene2refseq.gz and described in detail here: ftp://ftp.ncbi.nih.gov/gene/DATA/README maps refseq to Entrez Gene ID. Once you have the Entrez Gene ID, you can use the bioconductor annotation packages to get GO mappings. The file above is a tab-delimited text file, so you should be able to read it into R and do the matching by GI number rather easily. Hope that helps. Sean ======================== Message: 4 Date: Mon, 11 Jun 2007 12:36:31 +0000 (UTC) From: Nianhua Li <nialicn@yahoo.com> Subject: Re: [BioC] getting Locus Link ids from gene symbol To: bioconductor at stat.math.ethz.ch Message-ID: <loom.20070611t142932-100 at="" post.gmane.org=""> Content-Type: text/plain; charset=us-ascii Hi, Alex, You can parse ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/gene_info.gz There are 4 useful columns: tax_id (column 1), GeneID (column 2), Symbol (column 3), and Synonyms (column 5). You can: 1 Read in the file 2 filter it based on tax_id 3 match your gene symboles to the "Symbol" column and find their Gene ID 4 removed the matched gene symboles from your list 5 match the rest of gene symboles to the "Synonyms" column and find their Gene ID hope this helps nianhua Nianhua Li Software Developer

Thank you so much for your help, I will try those alternatives out. Best, Lina Simon Lin skrev: > In the following two unrelated messages, both Sean and Nianhua suggested > to download and parse some data tables from the NCBI. The gene_info and > several other tables seems very useful. If that is the case, why not > have it pre-loaded into a SQlite and distribute it as part of the > annotation package for human? Simon ================= Date: Tue, 12 Jun > 2007 05:59:55 -0400 From: Sean Davis <sdavis2 at="" mail.nih.gov=""> Subject: Re: > [BioC] from RefSeq GI protein identifiers to GO terms To: Lina > Hultin-Rosenberg <lina.hultin-rosenberg at="" ki.se=""> Cc: > bioconductor at stat.math.ethz.ch Message-ID: > <466E6E9B.3020609 at mail.nih.gov> Content-Type: text/plain; > charset=ISO-8859-1 Lina Hultin-Rosenberg wrote: > >>> Dear list, >>> >>> This might be a question that has been discussed previously but I could not >>> find any good solution for it. I have lists of human proteins from various >>> proteomics studies that I want to compare with regards to the GO terms >>> associated to them. I have the RefSeq GI protein id for the proteins and my >>> questions is how I best map those to other identifiers that I can use in >>> subsequent GO analysis? >>> >>> It might be that this problem is solved best outside R but maybe someone >>> still can give me a hint to the best solution. For me this is a problem that >>> comes up quite often - the need to map between different identifiers - and I >>> have not yet find any really good solution to it. If I for example use IPI I >>> always loose some proteins/genes since the coverage is rather bad, but maybe >>> there is no solution that will give perfect mapping?! >> >> > > The file located here: > > ftp://ftp.ncbi.nih.gov/gene/DATA/gene2refseq.gz > > and described in detail here: > > ftp://ftp.ncbi.nih.gov/gene/DATA/README > > maps refseq to Entrez Gene ID. Once you have the Entrez Gene ID, you > can use the bioconductor annotation packages to get GO mappings. The > file above is a tab-delimited text file, so you should be able to read > it into R and do the matching by GI number rather easily. > > Hope that helps. > > Sean > > ======================== > Message: 4 > Date: Mon, 11 Jun 2007 12:36:31 +0000 (UTC) > From: Nianhua Li <nialicn at="" yahoo.com=""> > Subject: Re: [BioC] getting Locus Link ids from gene symbol > To: bioconductor at stat.math.ethz.ch > Message-ID: <loom.20070611t142932-100 at="" post.gmane.org=""> > Content-Type: text/plain; charset=us-ascii > > Hi, Alex, > > You can parse ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/gene_info.gz > There are 4 useful columns: tax_id (column 1), GeneID (column 2), Symbol > (column 3), and Synonyms (column 5). You can: > > 1 Read in the file > 2 filter it based on tax_id > 3 match your gene symboles to the "Symbol" column and find their Gene ID > 4 removed the matched gene symboles from your list > 5 match the rest of gene symboles to the "Synonyms" column and find their Gene > ID > > hope this helps > > nianhua > > Nianhua Li > Software Developer > > _______________________________________________ > 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 > >

Dear Simon and Sean, sorry to get back to this issue so late but I have tried out various options to try to solve it. I parsed the files you mentioned but did not get many hits since many of my proteins does not have a Entrez gene id for some reason. In my search I also tried some of the Entrez e-utils (http://eutils.ncbi.nlm.nih.gov/entrez/query/static/eutils_help.html) and could get the accession numbers for my proteins. Can I go from accession number to GO term using biomaRt for example? Thanks again! Best, Lina Rosenberg Simon Lin skrev: > In the following two unrelated messages, both Sean and Nianhua suggested > to download and parse some data tables from the NCBI. The gene_info and > several other tables seems very useful. If that is the case, why not > have it pre-loaded into a SQlite and distribute it as part of the > annotation package for human? Simon ================= Date: Tue, 12 Jun > 2007 05:59:55 -0400 From: Sean Davis <sdavis2 at="" mail.nih.gov=""> Subject: Re: > [BioC] from RefSeq GI protein identifiers to GO terms To: Lina > Hultin-Rosenberg <lina.hultin-rosenberg at="" ki.se=""> Cc: > bioconductor at stat.math.ethz.ch Message-ID: > <466E6E9B.3020609 at mail.nih.gov> Content-Type: text/plain; > charset=ISO-8859-1 Lina Hultin-Rosenberg wrote: > >>> Dear list, >>> >>> This might be a question that has been discussed previously but I could not >>> find any good solution for it. I have lists of human proteins from various >>> proteomics studies that I want to compare with regards to the GO terms >>> associated to them. I have the RefSeq GI protein id for the proteins and my >>> questions is how I best map those to other identifiers that I can use in >>> subsequent GO analysis? >>> >>> It might be that this problem is solved best outside R but maybe someone >>> still can give me a hint to the best solution. For me this is a problem that >>> comes up quite often - the need to map between different identifiers - and I >>> have not yet find any really good solution to it. If I for example use IPI I >>> always loose some proteins/genes since the coverage is rather bad, but maybe >>> there is no solution that will give perfect mapping?! >> >> > > The file located here: > > ftp://ftp.ncbi.nih.gov/gene/DATA/gene2refseq.gz > > and described in detail here: > > ftp://ftp.ncbi.nih.gov/gene/DATA/README > > maps refseq to Entrez Gene ID. Once you have the Entrez Gene ID, you > can use the bioconductor annotation packages to get GO mappings. The > file above is a tab-delimited text file, so you should be able to read > it into R and do the matching by GI number rather easily. > > Hope that helps. > > Sean > > ======================== > Message: 4 > Date: Mon, 11 Jun 2007 12:36:31 +0000 (UTC) > From: Nianhua Li <nialicn at="" yahoo.com=""> > Subject: Re: [BioC] getting Locus Link ids from gene symbol > To: bioconductor at stat.math.ethz.ch > Message-ID: <loom.20070611t142932-100 at="" post.gmane.org=""> > Content-Type: text/plain; charset=us-ascii > > Hi, Alex, > > You can parse ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/gene_info.gz > There are 4 useful columns: tax_id (column 1), GeneID (column 2), Symbol > (column 3), and Synonyms (column 5). You can: > > 1 Read in the file > 2 filter it based on tax_id > 3 match your gene symboles to the "Symbol" column and find their Gene ID > 4 removed the matched gene symboles from your list > 5 match the rest of gene symboles to the "Synonyms" column and find their Gene > ID > > hope this helps > > nianhua > > Nianhua Li > Software Developer > > _______________________________________________ > 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 > >

If you do not have a large number of sequences, BioMart is a good choice. -Simon ----- Original Message ----- From: "Lina Hultin-Rosenberg" <lina.hultin-rosenberg@ki.se> To: "Simon Lin" <simonlin at="" duke.edu=""> Cc: <sdavis2 at="" mail.nih.gov="">; <bioconductor at="" stat.math.ethz.ch=""> Sent: Friday, June 29, 2007 12:54 AM Subject: Re: [BioC] from RefSeq to GO terms / gene symbol to geneID > Dear Simon and Sean, > > sorry to get back to this issue so late but I have tried out various > options to try to solve it. I parsed the files you mentioned but did not > get many hits since many of my proteins does not have a Entrez gene id for > some reason. In my search I also tried some of the Entrez e-utils > (http://eutils.ncbi.nlm.nih.gov/entrez/query/static/eutils_help.html) and > could get the accession numbers for my proteins. Can I go from accession > number to GO term using biomaRt for example? > > Thanks again! > > Best, > Lina Rosenberg > > Simon Lin skrev: >> In the following two unrelated messages, both Sean and Nianhua suggested >> to download and parse some data tables from the NCBI. The gene_info and >> several other tables seems very useful. If that is the case, why not have >> it pre-loaded into a SQlite and distribute it as part of the annotation >> package for human? Simon ================= Date: Tue, 12 Jun 2007 >> 05:59:55 -0400 From: Sean Davis <sdavis2 at="" mail.nih.gov=""> Subject: Re: >> [BioC] from RefSeq GI protein identifiers to GO terms To: Lina >> Hultin-Rosenberg <lina.hultin-rosenberg at="" ki.se=""> Cc: >> bioconductor at stat.math.ethz.ch Message-ID: >> <466E6E9B.3020609 at mail.nih.gov> Content-Type: text/plain; >> charset=ISO-8859-1 Lina Hultin-Rosenberg wrote: >> >>>> Dear list, >>>> >>>> This might be a question that has been discussed previously but I could >>>> not >>>> find any good solution for it. I have lists of human proteins from >>>> various >>>> proteomics studies that I want to compare with regards to the GO terms >>>> associated to them. I have the RefSeq GI protein id for the proteins >>>> and my >>>> questions is how I best map those to other identifiers that I can use >>>> in >>>> subsequent GO analysis? >>>> It might be that this problem is solved best outside R but maybe >>>> someone >>>> still can give me a hint to the best solution. For me this is a problem >>>> that >>>> comes up quite often - the need to map between different identifiers - >>>> and I >>>> have not yet find any really good solution to it. If I for example use >>>> IPI I >>>> always loose some proteins/genes since the coverage is rather bad, but >>>> maybe >>>> there is no solution that will give perfect mapping?! >>> >> >> The file located here: >> >> ftp://ftp.ncbi.nih.gov/gene/DATA/gene2refseq.gz >> >> and described in detail here: >> >> ftp://ftp.ncbi.nih.gov/gene/DATA/README >> >> maps refseq to Entrez Gene ID. Once you have the Entrez Gene ID, you >> can use the bioconductor annotation packages to get GO mappings. The >> file above is a tab-delimited text file, so you should be able to read >> it into R and do the matching by GI number rather easily. >> >> Hope that helps. >> >> Sean >> >> ======================== >> Message: 4 >> Date: Mon, 11 Jun 2007 12:36:31 +0000 (UTC) >> From: Nianhua Li <nialicn at="" yahoo.com=""> >> Subject: Re: [BioC] getting Locus Link ids from gene symbol >> To: bioconductor at stat.math.ethz.ch >> Message-ID: <loom.20070611t142932-100 at="" post.gmane.org=""> >> Content-Type: text/plain; charset=us-ascii >> >> Hi, Alex, >> >> You can parse ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/gene_info.gz >> There are 4 useful columns: tax_id (column 1), GeneID (column 2), Symbol >> (column 3), and Synonyms (column 5). You can: >> >> 1 Read in the file >> 2 filter it based on tax_id >> 3 match your gene symboles to the "Symbol" column and find their Gene ID >> 4 removed the matched gene symboles from your list >> 5 match the rest of gene symboles to the "Synonyms" column and find their >> Gene ID >> >> hope this helps >> >> nianhua >> >> Nianhua Li >> Software Developer >> >> _______________________________________________ >> 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 >> >> > > >