Hello,

It looks like the data is mapping as novel - not linked with the reference annotation. There can be a few factors that can cause this to occur for part of a dataset (often desirable) but when it occurs for an entire dataset, there is often a data mismatch or parameter issue.

The first item I always check is that the reference genomes are a match between inputs. Do this by confirming that the identifiers in the reference GFF file are the same as those in the Tophat BAM output (convert to SAM, with headers, to see the chromosome names). For the GFF file, the tool " Join, Subtract and Group -> Group" on the first column, chromosome name, with the action "count distinct" will isolate these.

But the real problem could be in the parameters, see below:

On 1/11/14 10:43 PM, Yang Bi wrote:
Dear all:

I am new to Galaxy and I followed online tutorials/tips to analyze my RNA seq data for alternative splicing. I used "tophat for illumina" to align my sequencing data after QC/filtering. Other than setting min intron to 20, I used the default settings. Then I feed the accepted hit files to cufflink. I set Min isoform fraction to 0, use annotation (tair10 gff3) as guide and choose yes for perform bias correction (locally cached tair10). 
My guess is that this Cufflinks run had the same issue - have you checked it? The 'Min isoform fraction' set to "0" may be problematic (I have never run Cufflinks this way). It may seem that this is a setting that is permissive - to capture even very small expression levels - but it may have had the reverse effect of not assigning any reads.

(The Tophat run with min intron at 20 is pretty low/sensitive - but with a smaller genome this probably will not cause memory issues with the mapping. Was this set based on the genome having transcripts with known, characterized introns this short? I didn't check, but you can in the reference GFF file.).

Maybe double check the above Cufflinks run, confirm the results were as expected, then try the default in Cufflinks to see how that works out ("0.1")? As a first pass test? If you want to make this more sensitive in subsequent run, you could try "0.01" - although how significant those results are, given this genome and your specific input data, would need to be evaluated.

After that, if you are still having trouble, please feel free to share a history link and we can try to help (copy and email a share link from the public server, direct to me, to keep your data private). Here is how:
https://wiki.galaxyproject.org/Support#Shared_and_Published_data

Hopefully the parameter change works, or a reference genome issue is found and corrected, but if not, I'll watch for your email,

Jen
Galaxy team

I merged the assembled transcripts with cuffmerge and use cuffcompare to compare the resultant merged assembled transcript to the reference annotation file tair10 gff3. I choose yes for "use sequence data" and locally cached tair10 as the "reference list". I get this for the transcript accuracy analysis:

# Cuffcompare v2.1.1 | Command line was:
#cuffcompare -o cc_output -r /galaxy-repl/main/files/007/386/dataset_7386886.dat -s /galaxy/data/Arabidopsis_thaliana_TAIR10/sam_index/Arabidopsis_thaliana_TAIR10.fa ./input1
#

#= Summary for dataset: ./input1 :
#     Query mRNAs :   72778 in   51779 loci  (57559 multi-exon transcripts)
#            (12679 multi-transcript loci, ~1.4 transcripts per locus)
# Reference mRNAs :   42163 in   33350 loci  (30127 multi-exon)
# Corresponding super-loci:          33140
#--------------------|   Sn   |  Sp   |  fSn |  fSp  
        Base level: 	100.0	 62.7	  - 	  - 
        Exon level: 	104.6	 59.5	100.0	 60.5
      Intron level: 	100.0	 55.5	100.0	 56.5
Intron chain level: 	 98.3	 51.5	100.0	 60.3
  Transcript level: 	 98.7	 57.2	 94.8	 54.9
       Locus level: 	 99.4	 64.0	100.0	 64.1

     Matching intron chains:   29618
              Matching loci:   33147

          Missed exons:       1/169820	(  0.0%)
           Novel exons:  128021/298149	( 42.9%)
        Missed introns:       0/127896	(  0.0%)
         Novel introns:  102614/230568	( 44.5%)
           Missed loci:       1/33350	(  0.0%)
            Novel loci:    2962/51779	(  5.7%)

 Total union super-loci across all input datasets: 51779

For the tmap file, all my FPKMs are 0:

ref_gene_id	ref_id	class_code	cuff_gene_id	cuff_id	FMI	FPKM	FPKM_conf_lo	FPKM_conf_hi	cov	len	major_iso_id	ref_match_len
AT1G01010	AT1G01010.1	=	AT1G01010	TCONS_00000001	0	0.000000	0.000000	0.000000	0.000000	1688	TCONS_00000001	1688
AT1G01040	AT1G01040.1	=	AT1G01040	TCONS_00000002	0	0.000000	0.000000	0.000000	0.000000	6251	TCONS_00000002	6251
AT1G01040	AT1G01040.2	=	AT1G01040	TCONS_00000003	0	0.000000	0.000000	0.000000	0.000000	5877	TCONS_00000002	5877
AT1G01046	AT1G01046.1	=	AT1G01046	TCONS_00000004	0	0.000000	0.000000	0.000000	0.000000	207	TCONS_00000004	207
AT1G01073	AT1G01073.1	=	AT1G01073	TCONS_00000005	0	0.000000	0.000000	0.000000	0.000000	111	TCONS_00000005	111
AT1G01110	AT1G01110.2	=	AT1G01110	TCONS_00000006	0	0.000000	0.000000	0.000000	0.000000	1782	TCONS_00000006	1782
AT1G01110	AT1G01110.1	=	AT1G01110	TCONS_00000007	0	0.000000	0.000000	0.000000	0.000000	1439	TCONS_00000006	1439
AT1G01115	AT1G01115.1	=	AT1G01115	TCONS_00000008	0	0.000000	0.000000	0.000000	0.000000	117	TCONS_00000008	117
AT1G01160	AT1G01160.1	=	AT1G01160	TCONS_00000009	0	0.000000	0.000000	0.000000	0.000000	1045	TCONS_00000010	1045
AT1G01160	AT1G01160.2	=	AT1G01160	TCONS_00000010	0	0.000000	0.000000	0.000000	0.000000	1129	TCONS_00000010	1129
AT1G01180	AT1G01180.1	=	AT1G01180	TCONS_00000011	0	0.000000	0.000000	0.000000	0.000000	1176	TCONS_00000011	1176
AT1G01210	AT1G01210.1	=	AT1G01210	TCONS_00000012	0	0.000000	0.000000	0.000000	0.000000	616	TCONS_00000012	616
AT1G01220	AT1G01220.1	=	AT1G01220	TCONS_00000013	0	0.000000	0.000000	0.000000	0.000000	3532	TCONS_00000013	3532

The FPKMs were normal in the assembled trancripts produced by cufflink.

Please enlighten me on the possible mistakes that i have made. I really appreciate your help.

Best
Yang 
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