predicting transcription factors
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Jing Huang ▴ 380
@jing-huang-4737
Last seen 10.2 years ago
Hi Experts, I am interested in predicting transcription factors for a specific family of genes. According to my readings, it is possible to predict transcription factors for the genes that are expressed accordingly with similar pattern (up or down regulated). I don't know how. Can somebody provide advices? Many thanks Jing Huang PhD OHSU [[alternative HTML version deleted]]
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Paul Shannon ▴ 750
@paul-shannon-5161
Last seen 10.2 years ago
Dear Jing Huang, Let me make sure I correctly grasp your problem. 1) You have a set of co-regulated genes 2) You wish to identify possible shared transcription factors for these genes If this is an accurate statement, then one good approach is to 1) Obtain promoter sequence of each gene, often estimated to be 3k upstream, and 300 bases downstream, of the transcription start site. BioC provides excellent tools and data for this. 2) Search for enriched transcription factor binding sites in these promoters. The meme website (or the downloaded meme software) is one traditional way to do the search. We have some BioC packages, including MotIV, and my soon-to-be-released collection of transcription factor matrices, which provide a good solution for this also, and have the advantage that your analysis can be performed entirely in R, reproducibly. Is this helpful? I can provide more detail. - Paul On Jun 5, 2012, at 9:44 AM, Jing Huang wrote: > Hi Experts, > > I am interested in predicting transcription factors for a specific family of genes. According to my readings, it is possible to predict transcription factors for the genes that are expressed accordingly with similar pattern (up or down regulated). I don't know how. > > Can somebody provide advices? > > Many thanks > > Jing Huang PhD > > OHSU > > > > [[alternative HTML version deleted]] > > _______________________________________________ > 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
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THANK you Paul. Your interpretation is right on my problem. Jing On 6/5/12 10:24 AM, "Paul Shannon" <pshannon at="" fhcrc.org=""> wrote: >Dear Jing Huang, > >Let me make sure I correctly grasp your problem. > > 1) You have a set of co-regulated genes > 2) You wish to identify possible shared transcription factors for these >genes > >If this is an accurate statement, then one good approach is to > > 1) Obtain promoter sequence of each gene, often estimated to be 3k >upstream, and 300 bases downstream, of the transcription start site. > BioC provides excellent tools and data for this. > > 2) Search for enriched transcription factor binding sites in these >promoters. > >The meme website (or the downloaded meme software) is one traditional way >to do the search. We have some BioC packages, including MotIV, and my >soon-to-be-released collection of transcription factor matrices, which >provide a good solution for this also, and have the advantage that your >analysis can be performed entirely in R, reproducibly. > >Is this helpful? I can provide more detail. > > - Paul > > > >On Jun 5, 2012, at 9:44 AM, Jing Huang wrote: > >> Hi Experts, >> >> I am interested in predicting transcription factors for a specific >>family of genes. According to my readings, it is possible to predict >>transcription factors for the genes that are expressed accordingly with >>similar pattern (up or down regulated). I don't know how. >> >> Can somebody provide advices? >> >> Many thanks >> >> Jing Huang PhD >> >> OHSU >> >> >> >> [[alternative HTML version deleted]] >> >> _______________________________________________ >> 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 >
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Hi Paul, Your interpretations are right on my problem. Jing On 6/5/12 10:24 AM, "Paul Shannon" <pshannon at="" fhcrc.org=""> wrote: >Dear Jing Huang, > >Let me make sure I correctly grasp your problem. > > 1) You have a set of co-regulated genes > 2) You wish to identify possible shared transcription factors for these >genes > >If this is an accurate statement, then one good approach is to > > 1) Obtain promoter sequence of each gene, often estimated to be 3k >upstream, and 300 bases downstream, of the transcription start site. > BioC provides excellent tools and data for this. > > 2) Search for enriched transcription factor binding sites in these >promoters. > >The meme website (or the downloaded meme software) is one traditional way >to do the search. We have some BioC packages, including MotIV, and my >soon-to-be-released collection of transcription factor matrices, which >provide a good solution for this also, and have the advantage that your >analysis can be performed entirely in R, reproducibly. > >Is this helpful? I can provide more detail. > > - Paul > > > >On Jun 5, 2012, at 9:44 AM, Jing Huang wrote: > >> Hi Experts, >> >> I am interested in predicting transcription factors for a specific >>family of genes. According to my readings, it is possible to predict >>transcription factors for the genes that are expressed accordingly with >>similar pattern (up or down regulated). I don't know how. >> >> Can somebody provide advices? >> >> Many thanks >> >> Jing Huang PhD >> >> OHSU >> >> >> >> [[alternative HTML version deleted]] >> >> _______________________________________________ >> 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 >
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Dear list members I am struggling with cummeRbund. I tried some codes listed here, and am confronted with some errors. > cuff_data <- readCufflinks('diff_out') > csDensity(genes(cuff_data)) Error in dat$fpkm + pseudocount : non-numeric argument to binary operator > diffGeneIDs <- getSig(cuff_data, level="genes", alpha=0.05) > diffGenes <- getGenes(cuff_data, diffGeneIDs) Error in sqliteExecStatement(conn, statement, ...) : RS-DBI driver: (RS_SQLite_exec: could not execute1: cannot start a transaction within a transaction) > sessionInfo() R version 2.15.0 (2012-03-30) Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit) locale: [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] cummeRbund_1.2.0 reshape2_1.2.1 ggplot2_0.9.1 RSQLite_0.11.1 DBI_0.2-5 loaded via a namespace (and not attached): [1] colorspace_1.1-1 dichromat_1.2-4 digest_0.5.2 grid_2.15.0 [5] labeling_0.1 MASS_7.3-18 memoise_0.1 munsell_0.3 [9] plyr_1.7.1 proto_0.3-9.2 RColorBrewer_1.0-5 scales_0.2.1 [13] stringr_0.6 I cannot figure out the reason, could anyone give me some hints? Thanks in advance! Best wishes Li
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Hi Li, Sorry for the delayed response. I have not been able to reproduce the first error, can you email me directly your cuffData.db file and I will see if I can figure out what's going on.. As for the second error. This arises from the fact that the csDensity function is dying in the middle of a transaction. Another call to readCufflinks() should re-establish a connection to the database and resolve this issue. Cheers, Loyal On Jun 5, 2012, at 7:40 PM, Wang, Li wrote: > Dear list members > > I am struggling with cummeRbund. I tried some codes listed here, and am confronted with some errors. > >> cuff_data <- readCufflinks('diff_out') >> csDensity(genes(cuff_data)) > > Error in dat$fpkm + pseudocount : non-numeric argument to binary operator > >> diffGeneIDs <- getSig(cuff_data, level="genes", alpha=0.05) >> diffGenes <- getGenes(cuff_data, diffGeneIDs) > > Error in sqliteExecStatement(conn, statement, ...) : > RS-DBI driver: (RS_SQLite_exec: could not execute1: cannot start a transaction within a transaction) > >> sessionInfo() > R version 2.15.0 (2012-03-30) > Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit) > > locale: > [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 > > attached base packages: > [1] stats graphics grDevices utils datasets methods base > > other attached packages: > [1] cummeRbund_1.2.0 reshape2_1.2.1 ggplot2_0.9.1 RSQLite_0.11.1 DBI_0.2-5 > > loaded via a namespace (and not attached): > [1] colorspace_1.1-1 dichromat_1.2-4 digest_0.5.2 grid_2.15.0 > [5] labeling_0.1 MASS_7.3-18 memoise_0.1 munsell_0.3 > [9] plyr_1.7.1 proto_0.3-9.2 RColorBrewer_1.0-5 scales_0.2.1 > [13] stringr_0.6 > > I cannot figure out the reason, could anyone give me some hints? > > Thanks in advance! > > Best wishes > Li > > _______________________________________________ > 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
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