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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