Unconfigured Ad

Collapse
X
 
  • Filter
  • Time
  • Show
Clear All
new posts
  • qiongyi
    Member
    • Nov 2010
    • 10

    Test status in Cufflinks and Cuffdiff

    Hi, I ran Cufflinks and Cuffdiff on some RNA-Seq datasets, and I got the result files "isoform_exp.diff" and "gene_exp.diff". When I looked at these two list, I found something interesting. Here I take gene "Phactr1" as an example:

    gene level estimation:
    Code:
    test_id	gene_id	gene	locus	sample_1	sample_2	status	value_1	value_2	log2(fold_change)	test_stat	p_value	q_value	significant
    Phactr1	Phactr1	Phactr1	chr13:42680622-43138512	KB	NB	OK	1.0429	1.49812	0.522548	-1.36759	0.171441	0.677408	no
    isoform level estimation:
    Code:
    test_id	gene_id		locus	sample_1	sample_2	status	value_1	value_2	log2(fold_change)	test_stat	p_value	q_value	significant
    NM_001005740	Phactr1	Phactr1	chr13:42680622-43138512	KB	NB	NOTEST	1.0429	1.49812	0.522548	-1.36759	0.171441	1	no
    NM_001005748	Phactr1	Phactr1	chr13:42680622-43138512	KB	NB	NOTEST	0	0	0	0	1	1	no
    NM_198419	Phactr1	Phactr1	chr13:42680622-43138512	KB	NB	NOTEST	0	0	0	0	1	1	no
    Gene "Phactr1" has three isoforms (NM_001005740, NM_001005748, NM_198419).
    Why the gene is given status "OK" while the isoform "NM_001005740" is given "NOTEST". It seems strange that cufflinks/cuffdiff extends different treatment to isoform and gene with the same expression value.
    I wonder what the criteria to set the status tags for genes and isoforms? Different criteria?
    Last edited by qiongyi; 08-24-2012, 04:43 AM.
  • qiongyi
    Member
    • Nov 2010
    • 10

    #2
    Hope someone here could help to clarify this...

    Comment

    • drosoform
      Junior Member
      • Apr 2012
      • 6

      #3
      I was wondering the exact same thing in my RNA-seq data from Cuffdiff. If anyone could explain why this happens, it would be very helpful!

      Comment

      • bhootnaath
        Junior Member
        • Jul 2009
        • 5

        #4
        Here is a somewhat related behavior of Cuffdiff / cummerbund that gives erroneous results.

        A) Ran Tophat v2.0.4 for 9 samples (3 conditions c1, c2, and c3 each with 3 biological replicates _1, _2 and _3), for example for c1_1:

        Code:
        tophat -p 8 -G genes.gtf -o tophat.output.c1_1 --no-novel-juncs --solexa1.3-quals bowtie_index c1_1.fq
        Repeated the above for c1_2, c1_3, c2_1, c2_2, c2_3, c3_1, c3_2, and c3_3.

        B) Ran Cuffdiff v2.0.2 (3522) for differential expression analysis for known list of genes/junctions:

        Code:
        cuffdiff -p 8 -b genome.fa -u genes.gtf -o cuffdiff.out -L c1,c2,c3 tophat.output.c1_1/accepted_hits.bam,tophat.output.c1_2/accepted_hits.bam,tophat.output.c1_3/accepted_hits.bam tophat.output.c2_1/accepted_hits.bam,tophat.output.c2_2/accepted_hits.bam,tophat.output.c2_3/accepted_hits.bam tophat.output.c3_1/accepted_hits.bam,tophat.output.c3_2/accepted_hits.bam,tophat.output.c3_3/accepted_hits.bam
        C) Then issued the following commands in R/cummerbund (v. 1.99.2) to write an output file containing differentially expressed genes for c1 vs. c2 with alpha = 0.05:

        Code:
        > cuff <- readCufflinks()
        > C1vsC2.sigGeneIds<-getSig(cuff,"c1","c2",alpha=0.05,level="genes")
        > C1vsC2.sigGenes <- getGenes(cuff,C1vsC2.sigGeneIds)
        > write.table(diffData(C1vsC2.sigGenes),"C1vsC2sigGenes.diff")
        Now here is one of many such erroneously reported significant genes in the C1vsC2sigGenes.diff file. The following gene *fails* the alpha = 0.05 threshold for c1 vs c2 and yet gets reported!

        Code:
        gene_id	sample_1	sample_2	status	value_1	value_2	log2_fold_change	test_stat	p_value	q_value	significant
        Gene_X	c1	c2	OK	1.12696	0.0921556	-3.61222	3.44126	0.000579016	[COLOR="Red"]0.0751255[/COLOR]	[COLOR="Red"]no[/COLOR]
        Gene_X	c1	c3	OK	1.12696	0.264807	-2.08942	1.30123	0.19318	0.999998	no
        Gene_X	c2	c3	OK	0.0921556	0.264807	1.5228	-0.990653	0.321855	0.999998	no
        Added later:

        This phenomenon has possibly been explained here and here.
        Last edited by bhootnaath; 10-17-2012, 12:20 PM. Reason: Found possible answer

        Comment

        • sdriscoll
          I like code
          • Sep 2009
          • 436

          #5
          I've seen similar inconsistencies. I've had a gene knocked out and it was not found significant at the gene level but at the isoform level it was. The thing is at the isoform level there was only one isoform with expression and its expression was the same as the gene level expression. Seems like a flaw in gene's estimated variance verses the isoform variance. Whatever the case it makes no biological sense which is always gonna be the point.
          /* Shawn Driscoll, Gene Expression Laboratory, Pfaff
          Salk Institute for Biological Studies, La Jolla, CA, USA */

          Comment

          Latest Articles

          Collapse

          • SEQadmin2
            Advanced Sequencing Platforms Tackle Neuroscience’s Toughest Genomics Problems
            by SEQadmin2



            Genomics studies in neuroscience face a special challenge due to the brain’s complexity and scarcity of samples. Mapping changes in cell type and state using conventional next-generation sequencing methods remains challenging. Advances in technologies like single-cell sequencing, spatial transcriptomics, and long-read sequencing have opened the door to deeper studies of the brain and diseases like Alzheimer’s, amyotrophic lateral sclerosis (ALS), and schizophrenia.
            ...
            07-09-2026, 11:10 AM
          • SEQadmin2
            Cancer Drug Resistance: The Lingering Barrier to Rising Survival
            by SEQadmin2



            Cancer survival rates have significantly increased in the last few decades in the United States, reaching a combined 70% 5-year survival rate by 2021. Behind this number, there are years of research to find new therapies, drug targets, and early detection methods. But there is one core challenge that keeps slowing down these advances, and it’s about drug resistance.

            There is no single reason why many patients don’t respond to treatment as expected. Cancer is...
            07-08-2026, 05:17 AM
          • GATTACAT
            Reply to Nine Things a Sample Prep Scientist Thinks About Before Sequencing
            by GATTACAT
            Love this - good data definitely starts from good input, and poor input can only give relatively poor data. I particularly like the mention of Nanodrop/absorbance based methods for quantification. It's such a toss up if you'll get an accurate reading or what amounts to a randomly generated number, and a lot of library/sequencing related issues can be traced back to poor quant.
            07-01-2026, 11:43 AM

          ad_right_rmr

          Collapse

          News

          Collapse

          Topics Statistics Last Post
          Started by SEQadmin2, Today, 10:26 AM
          0 responses
          9 views
          0 reactions
          Last Post SEQadmin2  
          Started by SEQadmin2, 07-09-2026, 10:04 AM
          0 responses
          24 views
          0 reactions
          Last Post SEQadmin2  
          Started by SEQadmin2, 07-08-2026, 10:08 AM
          0 responses
          16 views
          0 reactions
          Last Post SEQadmin2  
          Started by SEQadmin2, 07-07-2026, 11:05 AM
          0 responses
          33 views
          0 reactions
          Last Post SEQadmin2  
          Working...