Announcement

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

  • Cufflinks workflows

    Hi,

    I’m running a few workflows through Cufflinks/Cuffcompare/Cuffdiff in Galaxy (only because I can’t get it to install into my 32bit Ubuntu).
    I have 3 types of analyses, but only one seems to be working. Can you help me understand what I am doing wrong?

    I have 2 time points: CON (control) and REG (regenerated), 3 age cohorts (J-juvenille, A-adult, G-Geriatric), 3 biological replicates per time point/age (J-CON(1-3), J-REG(1-3), A-CON(1-3),etc).

    I’m using the Ensembl gene track from UCSC for Zebrafish. The GTF looks like this:

    chr1 danRer7_ensGene exon 25135496 25135824 0.000000 - . gene_id "ENSDART00000141301"; transcript_id "ENSDART00000141301";
    chr1 danRer7_ensGene exon 25177126 25177291 0.000000 - . gene_id "ENSDART00000141301"; transcript_id "ENSDART00000141301";

    Workflow 1: I’m trying to test each replicate against itself for differentially expressed genes (CON1 vs REG1).

    1. Run Cufflinks on each sample, with the Ensembl GTF as the reference annotation.
    2. Use the GTFs from cufflinks and Ensembl GTF in Cuffcompare
    3. Use combined transcripts from cuffcompare, with TopHat output of CON and REG.

    My output looks like this:

    test_id gene_id gene locus sample_1 sample_2 status value_1 value_2 ln(fold_change) test_stat p_value q_value significant
    XLOC_000001 XLOC_000001 ENSDART00000127092 Zv9_NA10:29174-34103 CON REG NOTEST 0 0 0 0 1 1 no
    XLOC_000002 XLOC_000002 ENSDART00000130683 Zv9_NA101:39833-39948 CON REG NOTEST 0 0 0 0 1 1 no
    XLOC_000003 XLOC_000003 ENSDART00000130536 Zv9_NA105:12113-12230 CON REG NOTEST 0 0 0 0 1 1 no
    XLOC_000004 XLOC_000004 ENSDART00000123528 Zv9_NA109:103-187 CON REG NOTEST 0 0 0 0 1 1 no
    XLOC_000005 XLOC_000005 ENSDART00000128305 Zv9_NA109:280-364 CON REG NOTEST 0 0 0 0 1 1 no
    XLOC_000006 XLOC_000006 ENSDART00000123913 Zv9_NA109:457-541 CON REG NOTEST 0 0 0 0 1 1 no

    That appears to be working right.

    Workflow 2: I’m trying to test each age cohort CON vs REG against itself for differentially expressed genes (CON(1-3) vs REG(1-3)).
    1. run Cufflinks on each sample, with the Ensembl GTF as the reference annotation.
    2. Use the GTFs (total of 6) from cufflinks and Ensembl GTF in Cuffcompare
    3. Use combined transcripts from cuffcompare, with TopHat output of CON and REG in Cuffdiff, with 2 groups of replicates (CON and REG)

    test_id gene_id gene locus sample_1 sample_2 status value_1 value_2 ln(fold_change) test_stat p_value q_value significant
    XLOC_000001 XLOC_000001 - Zv9_NA10:29174-34103 CON REG NOTEST 0 0 0 0 1 1 no
    XLOC_000002 XLOC_000002 - Zv9_NA101:39833-39948 CON REG NOTEST 0 0 0 0 1 1 no
    XLOC_000003 XLOC_000003 - Zv9_NA105:12113-12230 CON REG NOTEST 0 0 0 0 1 1 no
    XLOC_000004 XLOC_000004 - Zv9_NA109:103-187 CON REG NOTEST 0 0 0 0 1 1 no
    XLOC_000005 XLOC_000005 - Zv9_NA109:280-364 CON REG NOTEST 0 0 0 0 1 1 no
    XLOC_000006 XLOC_000006 - Zv9_NA109:457-541 CON REG NOTEST 0 0 0 0 1 1 no

    Notice how there is nothing in ‘gene’? There is nothing in that column other than ‘-‘ . How do I get it to output the Ensembl ID like in the first analysis?

    Workflow 3: I’m trying to test each age cohort CON against the other age CON for differentially expressed genes (A-CON(1-3) vs G-CON(1-3) vs J-CON(1-3)).
    • I ran this two different ways. One with 2 replicates ( A vs G, G vs J, A vs J) and one with all three replicates (A vs G vs J)
    1. run Cufflinks on each sample, with the Ensembl GTF as the reference annotation.
    2. Use the GTFs (total of 9 ) from cufflinks and Ensembl GTF in Cuffcompare
    3. Use combined transcripts from cuffcompare, with TopHat output of CON Cuffdiff, with 2 groups of replicates (A-CON(1-3) vs G-CON(1-3) vs J-CON(1-3)) and with 3 groups : A vs G vs J

    The output from 2 replicate groups

    test_id gene_id gene locus sample_1 sample_2 status value_1 value_2 ln(fold_change) test_stat p_value q_value significant
    XLOC_000001 XLOC_000001 - Zv9_NA10:29174-34103 A G NOTEST 0 0 0 0 1 1 no
    XLOC_000002 XLOC_000002 - Zv9_NA101:39833-39948 A G NOTEST 0 0 0 0 1 1 no
    XLOC_000003 XLOC_000003 - Zv9_NA105:12113-12230 A G NOTEST 0 0 0 0 1 1 no
    XLOC_000004 XLOC_000004 - Zv9_NA109:103-187 A G NOTEST 0 0 0 0 1 1 no
    XLOC_000005 XLOC_000005 - Zv9_NA109:280-364 A G NOTEST 0 0 0 0 1 1 no
    XLOC_000006 XLOC_000006 - Zv9_NA109:457-541 A G NOTEST 0 0 0 0 1 1 no


    The output from 3 replicate groups:

    test_id gene_id gene locus sample_1 sample_2 status value_1 value_2 ln(fold_change) test_stat p_value q_value significant
    XLOC_000001 XLOC_000001 - Zv9_NA10:29174-34103 G J NOTEST 0 0 0 0 1 1 no
    XLOC_000002 XLOC_000002 - Zv9_NA101:39833-39948 G J NOTEST 0 0 0 0 1 1 no
    XLOC_000003 XLOC_000003 - Zv9_NA105:12113-12230 G J NOTEST 0 0 0 0 1 1 no
    XLOC_000004 XLOC_000004 - Zv9_NA109:103-187 G J NOTEST 0 0 0 0 1 1 no
    XLOC_000005 XLOC_000005 - Zv9_NA109:280-364 G J NOTEST 0 0 0 0 1 1 no
    XLOC_000006 XLOC_000006 - Zv9_NA109:457-541 G J NOTEST 0 0 0 0 1 1 no


    Only the first workflow gives the Ensembl gene in the output. Can anyone please help me understand why? Am I doing the analysis wrong or do I need to merge this output file with another file to get the Ensembl gene names? Is there a better way of doing this analysis?

    Thanks!

Latest Articles

Collapse

  • seqadmin
    Multiomics Techniques Advancing Disease Research
    by seqadmin


    New and advanced multiomics tools and technologies have opened new avenues of research and markedly enhanced various disciplines such as disease research and precision medicine1. The practice of merging diverse data from various ‘omes increasingly provides a more holistic understanding of biological systems. As Maddison Masaeli, Co-Founder and CEO at Deepcell, aptly noted, “You can't explain biology in its complex form with one modality.”

    A major leap in the field has
    ...
    02-08-2024, 06:33 AM
  • seqadmin
    The 3D Genome: New Technologies and Emerging Insights
    by seqadmin


    The study of three-dimensional (3D) genomics explores the spatial structure of genomes and their role in processes like gene expression and DNA replication. By employing innovative technologies, researchers can study these arrangements to discover their role in various biological processes. Scientists continue to find new ways in which the organization of DNA is involved in processes like development1 and disease2.

    Basic Organization and Structure
    Understanding...
    01-22-2024, 03:25 PM

ad_right_rmr

Collapse

News

Collapse

Topics Statistics Last Post
Started by seqadmin, Today, 08:57 AM
0 responses
8 views
0 likes
Last Post seqadmin  
Started by seqadmin, 02-14-2024, 09:19 AM
0 responses
42 views
0 likes
Last Post seqadmin  
Started by seqadmin, 02-12-2024, 03:37 PM
0 responses
401 views
0 likes
Last Post seqadmin  
Started by seqadmin, 02-09-2024, 03:36 PM
0 responses
644 views
0 likes
Last Post seqadmin  
Working...
X