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  • #31
    I retried the code and was still not successful with one of my datasets. I am sure that the code I am using is the same for both datasets (I have been copying and pasting the code for both datasets). Below is the code and the results for the commands you asked me to enter for the dataset that did not work properly.

    > cuff.res=read.delim(file="gene_exp.diff", sep="\t")
    > cuff.fc=cuff.res$log2.fold_change.
    > gnames=cuff.res$gene
    > sel=gnames!="-"
    > gnames=as.character(gnames[sel])
    > cuff.fc=cuff.fc[sel]
    > names(cuff.fc)=gnames
    > gnames.eg=pathview::id2eg(gnames, category ="symbol", org="Ss")
    Loading required package: org.Ss.eg.db

    > sel2=gnames.eg[,2]>""
    > cuff.fc=cuff.fc[sel2]
    > names(cuff.fc)=gnames.eg[sel2,2]
    > range(cuff.fc)
    [1] -Inf Inf
    > cuff.fc[cuff.fc>10]=10
    > cuff.fc[cuff.fc< -10]=-10
    > exp.fc=cuff.fc
    > out.suffix="cuff"
    > require(gage)
    > kg.ssc=kegg.gsets("ssc")
    > kegg.gs=kg.ssc$kg.sets[kg.ssc$sigmet.idx]
    > fc.kegg.p <- gage(exp.fc, gsets = kegg.gs, ref = NULL, samp = NULL)
    > sel <- fc.kegg.p$greater[, "q.val"] < 0.1 &
    + !is.na(fc.kegg.p$greater[, "q.val"])
    > path.ids <- rownames(fc.kegg.p$greater)[sel]
    > sel.l <- fc.kegg.p$less[, "q.val"] < 0.1 &
    + !is.na(fc.kegg.p$less[, "q.val"])
    > path.ids.l <- rownames(fc.kegg.p$less)[sel.l]
    > path.ids2 <- substr(c(path.ids, path.ids.l), 1, 8)
    > require(pathview)
    > pv.out.list <- sapply(path.ids2[1:3], function(pid) pathview(
    + gene.data = exp.fc, pathway.id = pid,
    + species = "ssc", out.suffix=out.suffix))
    Start tag expected, '<' not found
    Parsing ./sscNA.xml file failed, please check the file!
    Start tag expected, '<' not found
    Parsing ./sscNA.xml file failed, please check the file!
    Start tag expected, '<' not found
    Parsing ./sscNA.xml file failed, please check the file!
    > str(cuff.fc)
    Named num [1:7434] 0.6134 0 0.3767 0.0177 -2.1022 ...
    - attr(*, "names")= chr [1:7434] "100620481" "100512748" "100513321" "100154202" ...
    > head(cuff.fc)
    100620481 100512748 100513321 100154202 733660 100153485
    0.6133590 0.0000000 0.3766890 0.0177489 -2.1022400 -0.2612780
    > lapply(kegg.gs[1:3], head, 3)
    $`ssc00970 Aminoacyl-tRNA biosynthesis`
    [1] "100144457" "100153423" "100155115"

    $`ssc02010 ABC transporters`
    [1] "100048963" "100127449" "100144586"

    $`ssc03008 Ribosome biogenesis in eukaryotes`
    [1] "100151786" "100151831" "100152658"

    > head(fc.kegg.p$greater)
    p.geomean stat.mean p.val
    ssc04064 NF-kappa B signaling pathway 0.02469658 1.990928 0.02469658
    ssc04620 Toll-like receptor signaling pathway 0.03526210 1.828538 0.03526210
    ssc04066 HIF-1 signaling pathway 0.04658115 1.693048 0.04658115
    ssc04640 Hematopoietic cell lineage 0.04929811 1.670237 0.04929811
    ssc04670 Leukocyte transendothelial migration 0.04995310 1.657766 0.04995310
    ssc04610 Complement and coagulation cascades 0.07305357 1.467898 0.07305357
    q.val set.size exp1
    ssc04064 NF-kappa B signaling pathway 0.8234987 53 0.02469658
    ssc04620 Toll-like receptor signaling pathway 0.8234987 58 0.03526210
    ssc04066 HIF-1 signaling pathway 0.8234987 59 0.04658115
    ssc04640 Hematopoietic cell lineage 0.8234987 49 0.04929811
    ssc04670 Leukocyte transendothelial migration 0.8234987 63 0.04995310
    ssc04610 Complement and coagulation cascades 0.8234987 44 0.07305357

    Below are the results for the dataset that did work properly:

    Working in directory /srv/mds01/shared/Epigenetics/Rnaseq/kyle_results/TJT/tophat_expression/cuffdiff
    Writing image file ssc04151.cuff.png
    Working in directory /srv/mds01/shared/Epigenetics/Rnaseq/kyle_results/TJT/tophat_expression/cuffdiff
    Writing image file ssc04010.cuff.png
    Working in directory /srv/mds01/shared/Epigenetics/Rnaseq/kyle_results/TJT/tophat_expression/cuffdiff
    Writing image file ssc04080.cuff.png
    > str(cuff.fc)
    Named num [1:344025] 1.279 0 0.155 0.806 -0.958 ...
    - attr(*, "names")= chr [1:344025] "100620481" "100512748" "100513321" "100154202" ...
    > head(cuff.fc)
    100620481 100512748 100513321 100154202 100156632 733660
    1.278660 0.000000 0.154690 0.806045 -0.958121 -0.103950
    > lapply(kegg.gs[1:3], head, 3)
    $`ssc00970 Aminoacyl-tRNA biosynthesis`
    [1] "100144457" "100153423" "100155115"

    $`ssc02010 ABC transporters`
    [1] "100048963" "100127449" "100144586"

    $`ssc03008 Ribosome biogenesis in eukaryotes`
    [1] "100151786" "100151831" "100152658"

    > head(fc.kegg.p$greater)
    p.geomean stat.mean p.val
    ssc00770 Pantothenate and CoA biosynthesis 0.9995680 -3.988628 0.9995680
    ssc00630 Glyoxylate and dicarboxylate metabolism 0.9995962 -3.942065 0.9995962
    ssc00052 Galactose metabolism 0.9999012 -4.281375 0.9999012
    ssc04740 Olfactory transduction 0.9999254 -4.576407 0.9999254
    ssc00100 Steroid biosynthesis 0.9999490 -4.852319 0.9999490
    ssc00970 Aminoacyl-tRNA biosynthesis 0.9999580 -4.829070 0.9999580
    q.val set.size exp1
    ssc00770 Pantothenate and CoA biosynthesis 1 10 0.9995680
    ssc00630 Glyoxylate and dicarboxylate metabolism 1 11 0.9995962
    ssc00052 Galactose metabolism 1 15 0.9999012
    ssc04740 Olfactory transduction 1 12 0.9999254
    ssc00100 Steroid biosynthesis 1 11 0.9999490
    ssc00970 Aminoacyl-tRNA biosynthesis 1 12 0.9999580

    Comment


    • #32
      Couldnt get gnCnt to work, I read in HTseq files instead like this:

      fls <- list.files(path="~/RNAseq/02_MG-myocytes/MG_Myocytes_HTSeq/A1A2pT2D",pattern="*.txt", full.names =T)

      #tab separated values with a header
      datalist = lapply(fls, function(x)read.table(x, header=F, colClasses=c("NULL", "numeric"), nrow = nrow(read.table(x, header = F)) - 5))

      #same header/columns for all files
      datafr = do.call("cbind", datalist)

      #column names
      colnames(datafr) <- c("D102-A1","D102-A2","D104-A1","D104-A2","D117-A1","D117-A2","D121-A1","D121-A2","D153-A1","D153-A2","D155-A1","D155-A2","D161-A1","D161-A2","D162-A1","D162-A2","D167-A1","D167-A2","D173-A1","D173-A2","D176-A1","D176-A2","D177-A1","D177-A2","D179-A1","D179-A2")

      #row names
      x <- read.table(file = "~/RNAseq/02_MG-myocytes/MG_Myocytes_HTSeq/A1A2pT2D/D102-A1.txt", header=F, colClasses=c( "character", "NULL"), nrow = nrow(read.table(file = "~/RNAseq/02_MG-myocytes/MG_Myocytes_HTSeq/A1A2pT2D/D102-A1.txt", header = F)) - 5)

      rownames(datafr) <- x$V1

      hnrnp.cnts <- datafr
      Last edited by sindrle; 03-18-2014, 05:52 AM. Reason: Did an alternative route

      Comment


      • #33
        Your kegg.gs has no problem, and it is the same for both datasets:
        > lapply(kegg.gs[1:3], head, 3)
        $`ssc00970 Aminoacyl-tRNA biosynthesis`
        [1] "100144457" "100153423" "100155115"


        The problem resides in yoru expression data. In your problematic dataset (let’s call it dataset 1), you have only 7434 entries (not exactly genes):
        > str(cuff.fc)
        Named num [1:7434] 0.6134 0 0.3767 0.0177 -2.1022 ...


        In your good dataset 2, you have 344025 entries:
        > str(cuff.fc)
        Named num [1:344025] 1.279 0 0.155 0.806 -0.958 ...


        Pig has 57700 genes in total based on NCBI data. Assume your dataset 2 has a full or decent coverage on pig genes, each pig gene maps to about 6 entries in this dataset. Assume the same level of redundancy, there are only ~ 1200 unique genes in your dataset 1. In other words, this is likely a sublist of selected or significant genes. Pathway or gene set analysis like GAGE requires a comprehensive and balance coverage of genes for your research speices, e.g. the full list of genes measured in a microarray or RNA-seq study, usually in the order of several thousand or tens of thousands of genes.
        Therefore, very likely you gage analysis of dataset 1 doesn’t yield any significant pathways due to the biased preslected/short gene list with only ~ 1200 genes. Hence you get NA pathway IDs when you select significant pathways. Your dataset 2 does give you some sensible results because you seem to have a good and balanced coverage of pig genes there.

        However, you should really make sure each gene ID has only 1 expression value when doing gene set or pathway analysis because genes are treated as independent variables. In the GAGE/Pathview native workflow or joint workflow with DESeq/DESeq2/edgeR/limma, we explicitly mapped reads to Entrez Genes and summarized expression counts per genes. But your data were mapped and processed by Cufflinks likely to some transcript sequences like RefSeq mRNA’s (with Entrez Gene annotation). You should summarize the expression data for multiple transcripts of the same gene into 1 unified expression level, and then conduct pathway analysis. We describe how to do so in a secondary gage tutorial, “Gene set and data preparation”, please check Section 5-gene or transcript ID conversion:



        Originally posted by shocker8786 View Post
        I retried the code and was still not successful with one of my datasets. I am sure that the code I am using is the same for both datasets (I have been copying and pasting the code for both datasets). Below is the code and the results for the commands you asked me to enter for the dataset that did not work properly.

        > cuff.res=read.delim(file="gene_exp.diff", sep="\t")
        > cuff.fc=cuff.res$log2.fold_change.
        > gnames=cuff.res$gene
        > sel=gnames!="-"
        > gnames=as.character(gnames[sel])
        > cuff.fc=cuff.fc[sel]
        > names(cuff.fc)=gnames
        > gnames.eg=pathview::id2eg(gnames, category ="symbol", org="Ss")
        Loading required package: org.Ss.eg.db

        > sel2=gnames.eg[,2]>""
        > cuff.fc=cuff.fc[sel2]
        > names(cuff.fc)=gnames.eg[sel2,2]
        > range(cuff.fc)
        [1] -Inf Inf
        > cuff.fc[cuff.fc>10]=10
        > cuff.fc[cuff.fc< -10]=-10
        > exp.fc=cuff.fc
        > out.suffix="cuff"
        > require(gage)
        > kg.ssc=kegg.gsets("ssc")
        > kegg.gs=kg.ssc$kg.sets[kg.ssc$sigmet.idx]
        > fc.kegg.p <- gage(exp.fc, gsets = kegg.gs, ref = NULL, samp = NULL)
        > sel <- fc.kegg.p$greater[, "q.val"] < 0.1 &
        + !is.na(fc.kegg.p$greater[, "q.val"])
        > path.ids <- rownames(fc.kegg.p$greater)[sel]
        > sel.l <- fc.kegg.p$less[, "q.val"] < 0.1 &
        + !is.na(fc.kegg.p$less[, "q.val"])
        > path.ids.l <- rownames(fc.kegg.p$less)[sel.l]
        > path.ids2 <- substr(c(path.ids, path.ids.l), 1, 8)
        > require(pathview)
        > pv.out.list <- sapply(path.ids2[1:3], function(pid) pathview(
        + gene.data = exp.fc, pathway.id = pid,
        + species = "ssc", out.suffix=out.suffix))
        Start tag expected, '<' not found
        Parsing ./sscNA.xml file failed, please check the file!
        Start tag expected, '<' not found
        Parsing ./sscNA.xml file failed, please check the file!
        Start tag expected, '<' not found
        Parsing ./sscNA.xml file failed, please check the file!
        > str(cuff.fc)
        Named num [1:7434] 0.6134 0 0.3767 0.0177 -2.1022 ...
        - attr(*, "names")= chr [1:7434] "100620481" "100512748" "100513321" "100154202" ...
        > head(cuff.fc)
        100620481 100512748 100513321 100154202 733660 100153485
        0.6133590 0.0000000 0.3766890 0.0177489 -2.1022400 -0.2612780
        > lapply(kegg.gs[1:3], head, 3)
        $`ssc00970 Aminoacyl-tRNA biosynthesis`
        [1] "100144457" "100153423" "100155115"

        $`ssc02010 ABC transporters`
        [1] "100048963" "100127449" "100144586"

        $`ssc03008 Ribosome biogenesis in eukaryotes`
        [1] "100151786" "100151831" "100152658"

        > head(fc.kegg.p$greater)
        p.geomean stat.mean p.val
        ssc04064 NF-kappa B signaling pathway 0.02469658 1.990928 0.02469658
        ssc04620 Toll-like receptor signaling pathway 0.03526210 1.828538 0.03526210
        ssc04066 HIF-1 signaling pathway 0.04658115 1.693048 0.04658115
        ssc04640 Hematopoietic cell lineage 0.04929811 1.670237 0.04929811
        ssc04670 Leukocyte transendothelial migration 0.04995310 1.657766 0.04995310
        ssc04610 Complement and coagulation cascades 0.07305357 1.467898 0.07305357
        q.val set.size exp1
        ssc04064 NF-kappa B signaling pathway 0.8234987 53 0.02469658
        ssc04620 Toll-like receptor signaling pathway 0.8234987 58 0.03526210
        ssc04066 HIF-1 signaling pathway 0.8234987 59 0.04658115
        ssc04640 Hematopoietic cell lineage 0.8234987 49 0.04929811
        ssc04670 Leukocyte transendothelial migration 0.8234987 63 0.04995310
        ssc04610 Complement and coagulation cascades 0.8234987 44 0.07305357

        Below are the results for the dataset that did work properly:

        Working in directory /srv/mds01/shared/Epigenetics/Rnaseq/kyle_results/TJT/tophat_expression/cuffdiff
        Writing image file ssc04151.cuff.png
        Working in directory /srv/mds01/shared/Epigenetics/Rnaseq/kyle_results/TJT/tophat_expression/cuffdiff
        Writing image file ssc04010.cuff.png
        Working in directory /srv/mds01/shared/Epigenetics/Rnaseq/kyle_results/TJT/tophat_expression/cuffdiff
        Writing image file ssc04080.cuff.png
        > str(cuff.fc)
        Named num [1:344025] 1.279 0 0.155 0.806 -0.958 ...
        - attr(*, "names")= chr [1:344025] "100620481" "100512748" "100513321" "100154202" ...
        > head(cuff.fc)
        100620481 100512748 100513321 100154202 100156632 733660
        1.278660 0.000000 0.154690 0.806045 -0.958121 -0.103950
        > lapply(kegg.gs[1:3], head, 3)
        $`ssc00970 Aminoacyl-tRNA biosynthesis`
        [1] "100144457" "100153423" "100155115"

        $`ssc02010 ABC transporters`
        [1] "100048963" "100127449" "100144586"

        $`ssc03008 Ribosome biogenesis in eukaryotes`
        [1] "100151786" "100151831" "100152658"

        > head(fc.kegg.p$greater)
        p.geomean stat.mean p.val
        ssc00770 Pantothenate and CoA biosynthesis 0.9995680 -3.988628 0.9995680
        ssc00630 Glyoxylate and dicarboxylate metabolism 0.9995962 -3.942065 0.9995962
        ssc00052 Galactose metabolism 0.9999012 -4.281375 0.9999012
        ssc04740 Olfactory transduction 0.9999254 -4.576407 0.9999254
        ssc00100 Steroid biosynthesis 0.9999490 -4.852319 0.9999490
        ssc00970 Aminoacyl-tRNA biosynthesis 0.9999580 -4.829070 0.9999580
        q.val set.size exp1
        ssc00770 Pantothenate and CoA biosynthesis 1 10 0.9995680
        ssc00630 Glyoxylate and dicarboxylate metabolism 1 11 0.9995962
        ssc00052 Galactose metabolism 1 15 0.9999012
        ssc04740 Olfactory transduction 1 12 0.9999254
        ssc00100 Steroid biosynthesis 1 11 0.9999490
        ssc00970 Aminoacyl-tRNA biosynthesis 1 12 0.9999580

        Comment


        • #34
          Thank you very much for that explanation, this is starting to make more sense to me now. I will make the necessary changes and retry. Thanks again!

          Comment


          • #35
            Im a retard. Forgot to convert kegg.gs to gene symbols:

            data(egSymb)

            kegg.gs.sym<-lapply(kegg.gs, eg2sym)
            Attached Files
            Last edited by sindrle; 03-18-2014, 05:51 AM. Reason: I have low iq

            Comment


            • #36
              sindrle,
              I couldn’t find your original problem, but if I remember correctly, your summarizeOverlaps step didn’t work. And you did something like:

              flag <- scanBamFlag(isNotPrimaryRead=FALSE, isProperPair=TRUE)
              param <- ScanBamParam(flag=flag)
              gnCnt <- summarizeOverlaps(exByGn, bamfls, mode="Union", ignore.strand=TRUE, single.end=TRUE, param=param)


              I guess you have single end data, so try:
              flag <- scanBamFlag(isNotPrimaryRead=FALSE, isProperPair=NA)
              The following flag line is for paired end data:
              flag <- scanBamFlag(isNotPrimaryRead=FALSE, isProperPair=TRUE)

              Check help info for details:
              ?scanBamFlag

              Comment


              • #37
                Ok, thanks!
                I was thinking about the same, but I figured since I have already ran HTseq I just used the results from there.

                Good to know until next time.

                Comment


                • #38
                  Hi,
                  I was looking at the expression data from pathview object and original data supplied to pathview, I found discrepancies in the two values:

                  Original count data (human data from reference manual):
                  > cnts.d["120", ]
                  ERR127302 ERR127303 ERR127304 ERR127305
                  0.277 0.577 0.441 0.021

                  output from Pathview object for human data from reference manual
                  >pv.out.list[1]
                  ERR127302 ERR127303 ERR127304 ERR127305
                  0.3952 0.8842 1.628 1.1983

                  Does pathview transforms the original data before plotting on the kegg pathways?

                  THanks,

                  Comment


                  • #39
                    If you are look for the gene entry"120" underpv.out.list[[1]]$plot.data.gene, it may or may not be different from the original data, cnts.d, because multiple genes may be mapped to the same nodes in a KEGG pathway. What you found in $plot.data.gene is the node level summary instead of single gene data. Each node there is labeled or named after the most representative member gene.

                    Please check the pathview tutorial (page 7) and documentations for more details:
                    Pathview is a tool set for pathway based data integration and visualization. It maps and renders a wide variety of biological data on relevant pathway graphs. All users need is to supply their data and specify the target pathway. Pathview automatically downloads the pathway graph data, parses the data file, maps user data to the pathway, and render pathway graph with the mapped data. In addition, Pathview also seamlessly integrates with pathway and gene set (enrichment) analysis tools for large-scale and fully automated analysis.

                    Comment


                    • #40
                      Fold change ploting

                      Thank you for your reply, now I understand why there are differences at node level data and actual fold changes.
                      However when I want to compare the fold change that I see in the input data and on the KEGG pathway it doesn't correlate well.
                      e.g.
                      pv.out.list[[1]]
                      Gene A:
                      T1 T2 T3 T4 T5
                      0.66 4.1079 0.830 3.278 2.71

                      input fold change values:
                      Gene A
                      -1.2 0.7 -0.14 -0.48 -0.78

                      Color coding for this gene on pathway node:

                      #EF3030 #FF0000 #FF0000 #FF0000 #FF0000

                      which doesn't reflect the trend seen in the input data.

                      When I change node.sum="median" it little bit shows same trend as input fold changes.
                      pv.out.list[[1]]
                      -1.15 0.7626 0.000 1.387 1.534
                      #00FF00 #EF3030 #BEBEBE #FF0000 #FF0000

                      It is bit confusing for me.


                      Thanks

                      Comment


                      • #41
                        Again, that’s because in this pathway, node labelled “Gene A” includes gene(s) other than “Gene A” but with similar function. It is a summary of fold changes for all genes mapped to this node, should you be surprised that it is different from fold change of “Gene A” alone?
                        You may use node.sum argument to control the the node summary is calculated, mean, median, max etc.

                        Comment


                        • #42
                          Thanks for the quick reply.
                          In above example GeneA is the only gene [shown green in original kegg] on that node for that species as other genes on the node are not present in the given species.
                          Thanks,
                          Shriram

                          Comment


                          • #43
                            Im having problems with Pathview. I can only get native KEGG, the kegg.native=F does not work.

                            Also the native KEGG only has green color, not red (up regulated) and green (down regulated).

                            Why am I having these two problems?

                            Native KEGG
                            # pv.out.list <- sapply(path.ids2, function(pid) pathview(gene.data = d[,
                            # 1:2], pathway.id = pid, species = "hsa", kegg.dir = "~/RNAseq/13_Acute-Changes/13_GAGE_native_A1A2/A1A2pT2D/Pathview"))

                            Graphviz view
                            # pv.out.list <- sapply(path.ids2, function(pid) pathview(gene.data = d[,
                            # 1:2], pathway.id = pid, species = "hsa", kegg.native=F,
                            # sign.pos="bottomleft", kegg.dir = "~/RNAseq/13_Acute-Changes/13_GAGE_native_A1A2/A1A2pT2D/Pathview"))


                            # > sessionInfo()
                            # R version 3.0.3 (2013-09-25)
                            # Platform: x86_64-apple-darwin10.8.0 (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] Rsamtools_1.14.3
                            # [2] Biostrings_2.30.1
                            # [3] TxDb.Hsapiens.UCSC.hg19.knownGene_2.10.1
                            # [4] GenomicFeatures_1.14.5
                            # [5] AnnotationDbi_1.24.0
                            # [6] Biobase_2.22.0
                            # [7] GenomicRanges_1.14.4
                            # [8] XVector_0.2.0
                            # [9] IRanges_1.20.7
                            # [10] BiocGenerics_0.8.0
                            # [11] BiocInstaller_1.12.0

                            # loaded via a namespace (and not attached):
                            # [1] biomaRt_2.18.0 bitops_1.0-6 BSgenome_1.30.0
                            # [4] DBI_0.2-7 RCurl_1.95-4.1 RSQLite_0.11.4
                            # [7] rtracklayer_1.22.5 stats4_3.0.2 tools_3.0.2
                            # [10] XML_3.95-0.2 zlibbioc_1.8.0

                            Comment


                            • #44
                              May I know what node, gene, pathway and what species you are talking about?

                              Originally posted by shriram View Post
                              Thanks for the quick reply.
                              In above example GeneA is the only gene [shown green in original kegg] on that node for that species as other genes on the node are not present in the given species.
                              Thanks,
                              Shriram

                              Comment


                              • #45
                                You don’t even have pathview package loaded based on your sessionInfo().


                                Originally posted by sindrle View Post
                                Im having problems with Pathview. I can only get native KEGG, the kegg.native=F does not work.

                                Also the native KEGG only has green color, not red (up regulated) and green (down regulated).

                                Why am I having these two problems?

                                # > sessionInfo()
                                # R version 3.0.3 (2013-09-25)
                                # Platform: x86_64-apple-darwin10.8.0 (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] Rsamtools_1.14.3
                                # [2] Biostrings_2.30.1
                                # [3] TxDb.Hsapiens.UCSC.hg19.knownGene_2.10.1
                                # [4] GenomicFeatures_1.14.5
                                # [5] AnnotationDbi_1.24.0
                                # [6] Biobase_2.22.0
                                # [7] GenomicRanges_1.14.4
                                # [8] XVector_0.2.0
                                # [9] IRanges_1.20.7
                                # [10] BiocGenerics_0.8.0
                                # [11] BiocInstaller_1.12.0

                                # loaded via a namespace (and not attached):
                                # [1] biomaRt_2.18.0 bitops_1.0-6 BSgenome_1.30.0
                                # [4] DBI_0.2-7 RCurl_1.95-4.1 RSQLite_0.11.4
                                # [7] rtracklayer_1.22.5 stats4_3.0.2 tools_3.0.2
                                # [10] XML_3.95-0.2 zlibbioc_1.8.0

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