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  • run cufflink, cuffcompare and cuffdiff workflow

    Hello All:

    I am not sure that I use the cufflink, cuffcompare and cuffdiff output files are corrected here.
    Here is my two work flows:
    1) work with unknow gene model:
    cufflink -p 4 -m 149 accepted_hits_s1.sam
    cufflink -p 4 -m 149 accepted_hits_s3.sam
    2) Using UCSC annotation GTF file
    cuffcompare -r refseqGene.gtf -R -s /indexes/ transcripts_s1.gtf transcripts_s3.gtf
    3) Using combined.gtf from the output file of cuffcompare:
    cuffdiff -p 4 -m 149 combined.gtf accepted_hits_s1.sam accepted_hits_s3.sam
    4) Cuffdiff output file: 0_1_splicing.diff contains about 75 records with test_stat=OK

    Workflow two with know gene model(refseqGene.gtf ):
    1) cufflink -p 4 -m 149 -G refseqGene.gtf accepted_hits_s1.sam
    cufflink -p 4 -m 149 -G refseqGene.gtf accepted_hits_s3.sam
    2) Using UCSC annotation GTF file
    cuffcompare -r refseqGene.gtf -R -s /indexes/ transcripts_s1.gtf transcripts_s3.gtf
    3) Using combined.gtf from the output file of cuffcompare:
    cuffdiff -p 4 -m 149 combined.gtf accepted_hits_s1.sam accepted_hits_s3.sam
    4) Cuffdiff output file: 0_1_splicing.diff contains about 445 records with test_stat=OK

    I should get more records in the first workflow than second workflow because the first one is run as de noval cufflink without any annotation GTF file. I get about 75 records of splicing.diff in the de noval (workflow one) and about 445 records of splicing.diff file in the know gene model (workflow two).
    Am I doing any steps wrong in these two workflows?

    Thanks for any comments!
    R

  • #2
    When you ran cufflinks without -G option, did you see that cufflinks detects more novel transcripts expression than when you ran it with the -G option?
    if the answer is no then it explains your results.

    I am interested in knowing how cufflinks assembles novel transcripts? does it look for ORF and splice sites when assembling them? It is important to know this if we want to trust its "novel" transcripts.

    I am also interested in exploring tophat/cufflinks/cuffcompare workflow parameters, so here are a few other "exploratory" parameters.

    I am using 75bp reads and want to determine differential expression between 2 states:
    1) tophat's -G parameter is set by defualt to 40 which means any read hitting less than 40 places in the genome are kept. I think this is way to high and I bring it down to 10. Although, I want to know how my results would differ if -G is set to 1. Has any one tried this?
    2) I also increase tophat's -a parameter from 8 to 10 which means splice sites having 10bases overlap on each side of the splice site will be considered. I wonder, if for 75bp reads, 10 may not be strict enough.
    3) tophat calls bowtie without its "-best" parameter. I am interested in calling bowtie (from tophat) with -best option to see if alignments differ. Anyone has any thoughts on this?
    4) I know for certain that the last 15 bases of my reads have bad quality overall. Should I trim these before aligning them? Does it make a difference by trimming or bowtie/tophat take the quality score in to consideration.
    5) Cufflinks -Q parameter is set to 0 by default. This is a critical parameter which can take the alignment quality into consideration. I don't know what is a good number to try this one.
    6) Your idea?

    I think an important discussion would be, how does one evaluate these runs? What are some plots, statistics that can give an idea of whether the program is doing a good job. some ideas:
    1) if you know certain genes will be down/up in your experimental conditions, check them
    2) Evaluate tophat's junctions.bed to determine how splice sites support changes with different parameters
    3) if you know of certain "problem" genes having complicated isoform expression or belonging to paralogous gene families, evaluate whether reads are being correctly aligned to them and if splicing changes are being detected
    4) Look at the uniformity in coverage in a gene. There should be genes that have even coverage through all exons.
    5) if you have replicate sample runs, you should theoretically see no differences in gene expression between them using your workflow. A dot plot of FPKM between replicates should be more or less a diagnol line. Ofcourse, this same plot between your control and treated sample should exhibit anomalies.
    6) Any other better ideas that have helped you
    Last edited by thinkRNA; 05-28-2010, 02:23 PM.

    Comment


    • #3
      Robin,
      You say the refseqGene.gtf file contains the UCSC annotations. Where exactly did you get the refseqGene.gtf file?

      Comment


      • #4
        Originally posted by thinkRNA View Post
        3) tophat calls bowtie without its "-best" parameter. I am interested in calling bowtie (from tophat) with -best option to see if alignments differ. Anyone has any thoughts on this?
        See if this thread can give you some help http://seqanswers.com/forums/showthr...=tophat+strata

        Dario

        Comment


        • #5
          Hi ThinkRNA,

          Did you figure out how TopHat deals with such situation?

          4) I know for certain that the last 15 bases of my reads have bad quality overall. Should I trim these before aligning them? Does it make a difference by trimming or bowtie/tophat take the quality score in to consideration.

          Comment


          • #6
            refseqGene.gtf file

            Originally posted by gtb View Post
            Robin,
            You say the refseqGene.gtf file contains the UCSC annotations. Where exactly did you get the refseqGene.gtf file?
            You go to galaxy--get data---ucsc main--set your parameters (clade,genome,assembly)--selct group: gene and genes prediction tracks and tracks: refseq genes---output format:GTF---send to galaxy---it will appear as a new job in your history

            Comment

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