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  • #46
    Thanks for your tutorial
    Last edited by tarmac; 05-16-2011, 08:22 AM.

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    • #47
      case practice help

      This summary is so awesome!!! Thanks so much!!!!!!!!!!
      I am wondering whether we can get RPKM starting from the reads from here? Can the annotation file here give the length of gene used here? Thanks!
      Last edited by lewewoo; 05-17-2011, 02:00 PM.

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      • #48
        Originally posted by flyyuan View Post
        Thanks Matt for this nice guide, now, I am tring to analysis some soybean rna-seq data following this article. However, I am very new to this work, could anybody give me some suggestions to solve following problems:

        1. I try to use makeTranscriptDbFromBiomart to get the information of soybean in phytozome database, but it seems there many organisms in phytozome database, how can I select the G.max which I need?

        2.bowtie software map the RNA-seq tag to reference gene, what is the criterion for match or does not match.

        thanks in advance!
        Hi Flyyuan: I am having the same concern, Matt is using Human transcript from UCSC, I want to do it for Soybean from Phytozome, I was wondering how you figured it out?

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        • #49
          Hi Flyyuan: I am having the same concern, Matt is using Human transcript from UCSC, I want to do it for Soybean from Phytozome, I was wondering how you figured it out?

          Comment


          • #50
            Hi,
            Can you download gff file from Phytozome for soybean? If so, you can use "Bedtool"s "CoverageBed" to extract the read counts mapping to the transcripts which you may use for DE testing in "edgeR".

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            • #51
              I ran this commond
              > txdb=makeTranscriptDbFromUCSC(genome='G.max', tablename='ensGene')
              Output was;
              Download the ensGene table ... OK
              Download the ensGtp table ... OK
              Extract the 'transcripts' data frame ... OK
              Extract the 'splicings' data frame ... OK

              Every thing was OK till this step;

              Download and preprocess the 'chrominfo' data frame ... Error in download.file(url, destfile, quiet = TRUE) :
              cannot open URL 'http://hgdownload.cse.ucsc.edu/goldenPath/G.max/database/chromInfo.txt.gz'
              In addition: There were 50 or more warnings (use warnings() to see the first 50)

              I don't know how to handle that.

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              • #52
                I had a problem when running this workflow and was wondering if someone could help me solve this problem. It appears as if the chromosome lengths for chromosomes 14 and 10 in the yeast genome are off by a single base between the two reference sequences used when trying to generating the overlaps (read counting). Can anyone assist me with the issue below?

                > counts=countOverlaps(tx_by_gene,reads)
                Error in queryHits(findOverlaps(query, subject, maxgap = maxgap, minoverlap = minoverlap, :
                error in evaluating the argument 'x' in selecting a method for function 'queryHits': Error in mergeNamedAtomicVectors(seqlengths(x), seqlengths(y), what = c("sequence", :
                sequences chrXIV, chrX have incompatible seqlengths:
                - in 'x': 784333, 745742
                - in 'y': 784334, 745741

                All help is greatly appreciate. Thanks.

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                • #53
                  Excellent tutorial. Helped a great deal in connecting the many dots that I had . Thank you very much!

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                  • #54
                    Matt,
                    I tried to run the Prostate cancer data set explained in the tutorial following the same process using the same tools mentioned. I get the error below when running bowtie


                    Error: reads file does not look like a FASTQ file
                    Command: bowtie -v 3 --best --sam /usr/local/bowtie/indexes/hg19 s1.fa s1_test.sam

                    Any help would be great, I am trying to setup a NGS workflow like the one mentioned in the paper

                    vic

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                    • #55
                      Originally posted by ssvictor View Post
                      Matt,
                      I tried to run the Prostate cancer data set explained in the tutorial following the same process using the same tools mentioned. I get the error below when running bowtie


                      Error: reads file does not look like a FASTQ file
                      Command: bowtie -v 3 --best --sam /usr/local/bowtie/indexes/hg19 s1.fa s1_test.sam

                      Any help would be great, I am trying to setup a NGS workflow like the one mentioned in the paper

                      vic

                      It looks like bowtie is expecting fastQ format and your reads are in fastA format
                      Try adding -f
                      bowtie -v 3 --best --sam /usr/local/bowtie/indexes/hg19 -f s1.fa s1_test.sam

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                      • #56
                        colin, thanks that worked .......

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                        • #57
                          Excellent post! Many thanks for sharing.

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                          • #58
                            Small code issue

                            Hi Matt,
                            First of all, fantastic tutorial--very thorough. Just wanted to let you know of a small error in your code segments in your latest update--a missing terminal ')' in your prostate data example when building a TOC.

                            toc=data.frame(rep(NA,length(tx_by_gene))

                            Are you currently adding examples or interested in working on a technical report for other DEG software packages (DEGseq, myrna, etc)? I would be interested in working on such a tutorial or wiki.

                            Originally posted by MDY View Post
                            Hello,

                            I've written a guide to the analysis of RNA-seq data, for the purpose of differential expression analysis. It currently lives on our internal wiki that can't be viewed outside of our division, although printouts have been used at workshops. It is by no means perfect and very much a work in progress, but a number of people have found it helpful, so I thought it would useful to have it somewhere more publicly accessible.

                            I've attached a pdf version of the guide, although really what I was hoping was that someone here could suggest somewhere where it could be publicly hosted as a wiki. This area is so multifaceted and fast-moving that the only way such a guide can remain useful is if it can be constantly extended and updated.

                            If anyone has any suggestions about potential hosting, they can contact me at [email protected]

                            Cheers

                            Matt

                            Update: I've put a few extra things on our local Wiki and seeing as people here seem to be finding this useful I thought I'd post an updated version. I'm also an author on a review paper on Differential Expression using RNA-seq which people who find the guide useful, might also find relevant...

                            RNA-seq Review

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                            • #59
                              Hello,

                              Sorry, but after a while I am not able to figure out how this regular expression works:

                              new_read_chr_names=gsub("(.*)[T]*\\..*","chr\\1",rname(reads))

                              and can convert these chromosome names:"10.1-129993255" , "11.1-121843856" ,"1.1-197195432" , "12.1-121257530", "13.1-120284312"

                              Into this new format:

                              "chr10", "chr11", "chr1", "chr12", "chr13"

                              I would be very grateful if someone could give me a more detailed explanation about it because I am not able to understand this regular expression.

                              Thanks in advance!

                              Comment


                              • #60
                                Hi,

                                I find the script great and very helpful.
                                I don't understand the part with the GO enrichment.

                                I am trying to do it with drosophila genes, which I converted into entrez annotations.
                                Code:
                                > head(pwf)
                                       DEgenes bias.data         pwf
                                43072        0      2002 0.018493247
                                40191        0     14212 0.044941512
                                318077       0       493 0.009297322
                                32941        0      1918 0.017987442
                                42674        0      1182 0.012241218
                                42675        0       566 0.009468054
                                But than I get this:
                                Code:
                                > GO.pvals <- goseq(pwf, "dm3", "refGenes")
                                Fetching GO annotations...
                                Error in getgo(rownames(pwf), genome, id, fetch.cats = test.cats) : 
                                  Couldn't grab GO categories automatically.  Please manually specify
                                Is it because I can't work with goseq on drosophila, or did I miss something in the workflow?

                                Thanks
                                Assa

                                Comment

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