Seqanswers Leaderboard Ad

Collapse

Announcement

Collapse
No announcement yet.
X
 
  • Filter
  • Time
  • Show
Clear All
new posts

  • Cuffcompare not showing all query transcripts

    Hi,

    I have 3 reference annotation GTF files and 3 query GTF files which were generated by Tophat-Cufflinks combination.

    I am using cuffcompare to compare the 3 query GTF files against each reference annotation GTF file.

    While doing this I noticed that the resulting combined.gtf and .tracking file are not reporting all the transcripts from the query GTF files.

    When I checked the .stats file I noticed in 'Summary for dataset' section that only a subset of the transcripts are reported.

    Example:
    GTF_A - 265244 transcripts
    GTF_B - 267648 transcripts

    .stats file for GTF_A reports always the same number against all references
    #= Summary for dataset: GTF_A.gtf :
    # Query mRNAs : 216023 in 57092 loci (188694 multi-exon transcripts)

    .stats file for GTF_B also reports always the same number against all references
    #= Summary for dataset: GTF_B.gtf :
    # Query mRNAs : 218034 in 58267 loci (189378 multi-exon transcripts)

    And even though the reference annotation is different the list of missing transcripts from query GTF is exactly the same.
    So they must have been skipped when importing the transcripts from query GTF and it is independent of the reference GTF.

    When comparing GTF_B (Query) vs GTF_A (Reference).
    .stats file reports

    #= Summary for dataset: GTF_B.gtf :
    # Query mRNAs : 218034 in 58267 loci (189378 multi-exon transcripts)
    # (21371 multi-transcript loci, ~3.7 transcripts per locus)
    # Reference mRNAs : 238139 in 45259 loci (223605 multi-exon)

    One thing to notice above is that the statistics for GTF_A (Reference) are different when it is used a reference from earlier when it was used in query.

    I tried to find any trend for the missing transcripts but cannot identify anything.

    Can anyone help me understand the issue at hand.

    Regards,

    Veer

  • #2
    I suspect this may be related to cuffcompare excluding "contained" transcripts. Maybe using the -C flag will give less mysterious results.

    From the manual:
    "-C Enables the "contained" transcripts to be also written in the <outprefix>.combined.gtffile, with the attribute "contained_in" showing the first container transfrag found. By default, without this option, cuffcompare does not write in that file isoforms that were found to be fully contained/covered (with the same compatible intron structure) by other transfrags in the same locus."

    Comment


    • #3
      I am sorry I should have posted this earlier. I later found out the reason for this behavior, it was because there were predicted transcripts in the GTF which did not have any strand information and these transcripts without strand information were skipped.

      So what I did is... I assigned them as + strand :-p. I decided to check for the correct strand info later.

      Comment

      Latest Articles

      Collapse

      • seqadmin
        Genetic Variation in Immunogenetics and Antibody Diversity
        by seqadmin



        The field of immunogenetics explores how genetic variations influence immune responses and susceptibility to disease. In a recent SEQanswers webinar, Oscar Rodriguez, Ph.D., Postdoctoral Researcher at the University of Louisville, and Ruben Martínez Barricarte, Ph.D., Assistant Professor of Medicine at Vanderbilt University, shared recent advancements in immunogenetics. This article discusses their research on genetic variation in antibody loci, antibody production processes,...
        11-06-2024, 07:24 PM
      • seqadmin
        Choosing Between NGS and qPCR
        by seqadmin



        Next-generation sequencing (NGS) and quantitative polymerase chain reaction (qPCR) are essential techniques for investigating the genome, transcriptome, and epigenome. In many cases, choosing the appropriate technique is straightforward, but in others, it can be more challenging to determine the most effective option. A simple distinction is that smaller, more focused projects are typically better suited for qPCR, while larger, more complex datasets benefit from NGS. However,...
        10-18-2024, 07:11 AM

      ad_right_rmr

      Collapse

      News

      Collapse

      Topics Statistics Last Post
      Started by seqadmin, 11-08-2024, 11:09 AM
      0 responses
      36 views
      0 likes
      Last Post seqadmin  
      Started by seqadmin, 11-08-2024, 06:13 AM
      0 responses
      28 views
      0 likes
      Last Post seqadmin  
      Started by seqadmin, 11-01-2024, 06:09 AM
      0 responses
      32 views
      0 likes
      Last Post seqadmin  
      Started by seqadmin, 10-30-2024, 05:31 AM
      0 responses
      23 views
      0 likes
      Last Post seqadmin  
      Working...
      X