Hi,

Another approach you can try is to use DESeq or EdgeR from Bioconductor to
assess differential expression.

I personally like these two methods LOTS better than Cuff* mainly because
they are a lot closer to tried and true statistical methods developed for
microarrays. I esp. like how both methods let you test different factors.
For example, if you are testing a treatment (drug or no drug) and a
genotype (mutant vs. wildtype) you can find out which genes' expression
depends on having a wild-type copy of the gene by testing an "interaction
term."

Both methods start with simple counts - numbers of reads overlapping
annotated genes.

Probably there is a Galaxy workflow that can calculate counts of reads per
gene, but I don't know if Galaxy currently incorporates R/Bioconductor
tools. If you can get Galaxy to calculate reads per gene, then you can
then download the file and run it through edgeR or DESeq.

R is free but it does take some time to master it. But it is incredibly
powerful and well worth the effort!

To get started with R, I recommend doing the free-of-charge O'Reilly Press
"try R" tutorial which is on-line here: http://tryr.codeschool.com/

I hope this will be helpful!

Best wishes,

Ann Loraine

-------------------------------
Ann Loraine, Ph.D.
Associate Professor
Department of Bioinformatics and Genomics
University of North Carolina at Charlotte
North Carolina Research Campus
600 Laureate Way
Kannapolis, NC 28081
704-250-5750
alora...@uncc.edu
http://www.transvar.org
http://www.bioviz.org
http://www.uncc.edu





On 1/7/13 8:27 PM, "Jennifer Hillman-Jackson" <j...@bx.psu.edu> wrote:

>Hello Wei,
>
>The contents of the reference GTF files (original, before analysis) will
>probably provide some explanation. My guess is that GTF files have
>different contents and are not directly comparable - RefSeq with full
>transcripts and Ensembl with full transcripts + potentially partial
>predictions and/or predicted splice sites. Alternative versions of each
>may be available. When possible, you most likely will want to be using a
>reference GTF file that represents complete transcripts.
>
>I don't know what genome you are using, but you can check the source
>notes at Ensembl (& NCBI) to find out what each annotation build
>contains. A raw count on the number of entries in the GTF files can also
>be a clue - if greatly different, then you very likely have different
>populations in the two files.
>
>Good luck with your project!
>
>Jen
>Galaxy team
>
>On 1/7/13 1:47 PM, Wei Liao wrote:
>> Hi all,
>>
>> I am analyzing significant differential expressed genes for a pair of
>> normal V.S tumor, using Cuffdiff 2.0.2.
>> I noticed that by using ensemble GTF and refseq GTF, the results showed
>> a big difference on the number of genes being significant expressed.
>>
>> For ensemble GTF, there are only 250 genes differential expressed.
>> But for refseq GTF, there are about 1000 genes.
>>
>> I am running these data on Galaxy server and with the same workflow.
>>
>> Can anyone explain what is going on here? so which result should I
>>trust?
>>
>> Thanks.
>>
>>
>> --
>> Wei Liao
>> Research Scientist,
>> Brentwood Biomedical Research Institute
>> 16111 Plummer St.
>> Bldg 7, Rm D-122
>> North Hills, CA 91343
>> 818-891-7711 ext 7645
>>
>>
>> ___________________________________________________________
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>
>-- 
>Jennifer Hillman-Jackson
>Galaxy Support and Training
>http://galaxyproject.org
>___________________________________________________________
>The Galaxy User list should be used for the discussion of
>Galaxy analysis and other features on the public server
>at usegalaxy.org.  Please keep all replies on the list by
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>please use the interface at:
>
>  http://lists.bx.psu.edu/


___________________________________________________________
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