Positional Details with Features through UniProt.ws Ultimately to display as tracks in ggbio
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@anne-deslattes-mays-5977
Last seen 7.6 years ago
United States
Dear all, biocLite(?UniProt.ws?) libraryUniProt.ws) selectUniProt.ws,keys=("P02794"),columns=c("DOMAINS","FEATURES"),key type="UNIPROTKB") Getting extra data for P02794 NA NA etc UNIPROTKB DOMAINS 1 P02794 Ferritin-like diiron domain (1) FEATURES 1 Chain (2); Domain (1); Erroneous initiation (1); Helix (6); Initiator methionine (1); Metal binding (6); Modified residue (4); Sequence conflict (1); Turn (2) What I want are the positional details for each of these features ? which are visible through the uniprot web page. FTH1 is 183 amino acids in length. There are 6 metal binding sites, each at a specific position. This information is there since you can have the web site return the positional details. I would like them so I may manipulate them with new evidential information. Ultimately I wish to display them with tracks from ggbio ? pb.53A.pos.ga <- readGAlignmentsFromBam(pb.53A.pos.bamfile, param = ScanBamParam(which = genesymbol["FTH1"],what=c("seq")), use.names = TRUE) FTH1.ga <- geom_alignment(data = txdb,which=genesymbol["FTH1"]) So here I have sample information which I have aligned to the reference genome. I retrieve that information from a bam file. # create the GAlignments objects for each isoform FTH1.isoform.1 <- pb.53A.pos.ga[c(7)] FTH1.isoform.2 <- pb.53A.pos.ga[c(15)] FTH1.isoform.3 <- pb.53A.pos.ga[c(13)] FTH1.isoform.4 <- pb.53A.pos.ga[c(8)] FTH1.isoform.5 <- pb.53A.pos.ga[c(2)] FTH1.isoform.6 <- pb.53A.pos.ga[c(1)] p1 <- autoplot(FTH1.isoform.1, fill = "brown", color = "brown") p2 <- autoplot(FTH1.isoform.2, fill = "blue", color = "blue") p3 <- autoplot(FTH1.isoform.3, fill = "brown", color = "brown") p4 <- autoplot(FTH1.isoform.4, fill = "brown", color = "brown") p5 <- autoplot(FTH1.isoform.5, fill = "brown", color = "brown") p6 <- autoplot(FTH1.isoform.6, fill = "brown", color = "brown") tracks( FTH1=p1.FTH1, "Iso 1"=p1, "Iso 2"=p2, "Iso 3"=p3, "Iso 4"=p4, "Iso 5"=p5, "Iso 6"=p6) I then can autopilot each of the separate isoforms. What I want to do however, is annotate the isoforms so that they each show the coding region with the full height of the bar, and a reduced height for the non-coding regions. Additionally, I want to color the graphic with the details for the protein, such as the metal binding sites, domains, etc. So that computationally I can generate an informative picture which explains what is lost or gained in separate isoforms. Thoughts? Anne R version 3.1.0 (2014-04-10) Platform: x86_64-apple-darwin13.1.0 (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] parallel stats graphics grDevices utils datasets methods [8] base other attached packages: [1] UniProt.ws_2.4.2 [2] RCurl_1.95-4.3 [3] bitops_1.0-6 [4] RSQLite_0.11.4 [5] DBI_0.2-7 [6] biomaRt_2.20.0 [7] BiocInstaller_1.14.2 [8] GenomicAlignments_1.0.5 [9] BSgenome_1.32.0 [10] Rsamtools_1.16.1 [11] Biostrings_2.32.1 [12] XVector_0.4.0 [13] ggbio_1.12.8 [14] ggplot2_1.0.0 [15] TxDb.Hsapiens.UCSC.hg19.knownGene_2.14.0 [16] GenomicFeatures_1.16.2 [17] AnnotationDbi_1.26.0 [18] Biobase_2.24.0 [19] GenomicRanges_1.16.4 [20] GenomeInfoDb_1.0.2 [21] IRanges_1.22.10 [22] BiocGenerics_0.10.0 loaded via a namespace (and not attached): [1] BatchJobs_1.3 BBmisc_1.7 BiocParallel_0.6.1 [4] biovizBase_1.12.1 brew_1.0-6 checkmate_1.2 [7] cluster_1.15.2 codetools_0.2-8 colorspace_1.2-4 [10] dichromat_2.0-0 digest_0.6.4 fail_1.2 [13] foreach_1.4.2 Formula_1.1-2 grid_3.1.0 [16] gridExtra_0.9.1 gtable_0.1.2 Hmisc_3.14-4 [19] iterators_1.0.7 labeling_0.2 lattice_0.20-29 [22] latticeExtra_0.6-26 MASS_7.3-33 munsell_0.4.2 [25] plyr_1.8.1 proto_0.3-10 RColorBrewer_1.0-5 [28] Rcpp_0.11.2 reshape2_1.4 rtracklayer_1.24.2 [31] scales_0.2.4 sendmailR_1.1-2 splines_3.1.0 [34] stats4_3.1.0 stringr_0.6.2 survival_2.37-7 [37] tcltk_3.1.0 tools_3.1.0 VariantAnnotation_1.10.5 [40] XML_3.98-1.1 zlibbioc_1.10.0 [[alternative HTML version deleted]]
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Tengfei Yin ▴ 490
@tengfei-yin-6162
Last seen 7.7 years ago
Hey Anne, So sorry for the late reply. Ideally, I should have some kind of mapper function in biovizBase to help map protein space to genomic space, so you don't have to do it yourself, but before I have that, a hack would be massage your protein domain data into a GRanges object, with domain function as coloumn, and use genomic coordinates, and then create a separate track to plot the object as rectangle and use color legend to indicate domain function. I will try to develop a more general approach for doing this, if you want, please send me an example RData or example data, so we can work on that together. ps: in case I don't miss your request, feel free to use github page issues <https: github.com="" tengfei="" ggbio="" issues="">here cheers Tengfei On Sat, Aug 16, 2014 at 6:57 AM, Anne Deslattes Mays <ad376 at="" georgetown.edu=""> wrote: > Dear all, > > biocLite(?UniProt.ws?) > libraryUniProt.ws) > > > selectUniProt.ws,keys=("P02794"),columns=c("DOMAINS","FEATURES"),k eytype="UNIPROTKB") > Getting extra data for P02794 NA NA etc > UNIPROTKB DOMAINS > 1 P02794 Ferritin-like diiron domain (1) > > > FEATURES > 1 Chain (2); Domain (1); Erroneous initiation (1); Helix (6); Initiator > methionine (1); Metal binding (6); Modified residue (4); Sequence conflict > (1); Turn (2) > > What I want are the positional details for each of these features ? which > are visible through the uniprot web page. > FTH1 is 183 amino acids in length. There are 6 metal binding sites, each > at a specific position. > This information is there since you can have the web site return the > positional details. I would like them so I may manipulate them with new > evidential information. > > Ultimately I wish to display them with tracks from ggbio ? > pb.53A.pos.ga <- readGAlignmentsFromBam(pb.53A.pos.bamfile, > param = ScanBamParam(which = > genesymbol["FTH1"],what=c("seq")), > use.names = TRUE) > > FTH1.ga <- geom_alignment(data = txdb,which=genesymbol["FTH1"]) > > So here I have sample information which I have aligned to the reference > genome. I retrieve that information from a bam file. > # create the GAlignments objects for each isoform > FTH1.isoform.1 <- pb.53A.pos.ga[c(7)] > FTH1.isoform.2 <- pb.53A.pos.ga[c(15)] > FTH1.isoform.3 <- pb.53A.pos.ga[c(13)] > FTH1.isoform.4 <- pb.53A.pos.ga[c(8)] > FTH1.isoform.5 <- pb.53A.pos.ga[c(2)] > FTH1.isoform.6 <- pb.53A.pos.ga[c(1)] > > > p1 <- autoplot(FTH1.isoform.1, fill = "brown", color = "brown") > p2 <- autoplot(FTH1.isoform.2, fill = "blue", color = "blue") > p3 <- autoplot(FTH1.isoform.3, fill = "brown", color = "brown") > p4 <- autoplot(FTH1.isoform.4, fill = "brown", color = "brown") > p5 <- autoplot(FTH1.isoform.5, fill = "brown", color = "brown") > p6 <- autoplot(FTH1.isoform.6, fill = "brown", color = "brown") > > tracks( FTH1=p1.FTH1, > "Iso 1"=p1, > "Iso 2"=p2, > "Iso 3"=p3, > "Iso 4"=p4, > "Iso 5"=p5, > "Iso 6"=p6) > > > I then can autopilot each of the separate isoforms. What I want to do > however, is annotate the isoforms so that they each show the coding region > with the full height of the bar, and a reduced height for the non- coding > regions. > > Additionally, I want to color the graphic with the details for the > protein, such as the metal binding sites, domains, etc. So that > computationally I can generate an informative picture which explains what > is lost or gained in separate isoforms. > > Thoughts? > > Anne > R version 3.1.0 (2014-04-10) > Platform: x86_64-apple-darwin13.1.0 (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] parallel stats graphics grDevices utils datasets methods > [8] base > > other attached packages: > [1] UniProt.ws_2.4.2 > [2] RCurl_1.95-4.3 > [3] bitops_1.0-6 > [4] RSQLite_0.11.4 > [5] DBI_0.2-7 > [6] biomaRt_2.20.0 > [7] BiocInstaller_1.14.2 > [8] GenomicAlignments_1.0.5 > [9] BSgenome_1.32.0 > [10] Rsamtools_1.16.1 > [11] Biostrings_2.32.1 > [12] XVector_0.4.0 > [13] ggbio_1.12.8 > [14] ggplot2_1.0.0 > [15] TxDb.Hsapiens.UCSC.hg19.knownGene_2.14.0 > [16] GenomicFeatures_1.16.2 > [17] AnnotationDbi_1.26.0 > [18] Biobase_2.24.0 > [19] GenomicRanges_1.16.4 > [20] GenomeInfoDb_1.0.2 > [21] IRanges_1.22.10 > [22] BiocGenerics_0.10.0 > > loaded via a namespace (and not attached): > [1] BatchJobs_1.3 BBmisc_1.7 BiocParallel_0.6.1 > [4] biovizBase_1.12.1 brew_1.0-6 checkmate_1.2 > [7] cluster_1.15.2 codetools_0.2-8 colorspace_1.2-4 > [10] dichromat_2.0-0 digest_0.6.4 fail_1.2 > [13] foreach_1.4.2 Formula_1.1-2 grid_3.1.0 > [16] gridExtra_0.9.1 gtable_0.1.2 Hmisc_3.14-4 > [19] iterators_1.0.7 labeling_0.2 lattice_0.20-29 > [22] latticeExtra_0.6-26 MASS_7.3-33 munsell_0.4.2 > [25] plyr_1.8.1 proto_0.3-10 RColorBrewer_1.0-5 > [28] Rcpp_0.11.2 reshape2_1.4 rtracklayer_1.24.2 > [31] scales_0.2.4 sendmailR_1.1-2 splines_3.1.0 > [34] stats4_3.1.0 stringr_0.6.2 survival_2.37-7 > [37] tcltk_3.1.0 tools_3.1.0 > VariantAnnotation_1.10.5 > [40] XML_3.98-1.1 zlibbioc_1.10.0 > [[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 > -- Tengfei Yin, PhD Product Manager Seven Bridges Genomics sbgenomics.com One Broadway FL 7 Cambridge, MA 02142 (617) 866-0446 [[alternative HTML version deleted]]
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