false negative results in rna sequencing data sets

[K1K]

Dear experts,

I just started working in the field of RNA sequencing and would be very grateful to get an advice from you.

I started with a set of two patients and two wild types in the hope of identifying a gene, which is not expressed in the patients. I used the pipeline and recipe of the genepattern workspace (http://recipes.genomespace.org/view/6) and I finally get a list with the FPKMs of the four different samples. There were two genes, which were virtually not expressed in both patients (FPKM = 0) while both wild types showed high expression (gene 1: FPKM > 8000; gene 2: > 3000). The status was marked as "High Data". Moreover, no errors occured during the execution. The problem is now, that these two genes were actually expressed in the patients (verified by qRT-PCR, mass spec and western blotting). So, do you have any idea, how this false result emerges?

Thanks a lot!

[Bukowski]

Far too many reasons to possibly speculate on from incorrectly running the analysis, to low read coverage in your patient samples to QC differences between the samples to differences is sample collection.

But you have hit the very clear problem of not having anywhere near enough samples to make an informed decision about this question.

[K1K]

Thanks a lot for your reply!
I prepared all samples together and according to the same protocol. Also, I´ve run the analysis multiple times with the same result. The raw reads in the patients looked good, so that the read coverage should not be a problem.
You are absolutely right, that 2 samples are not enough, but the two patients are siblings from highly consanguineous parents and we are searching for a complete loss of a protein, not only differential differences.

[Bukowski]

So have you visualised the read aligments in the region in the patients?

[K1K]

Do you mean a visualization for example with the igv tool? Or do you prefer an other one?

[Bukowski]

IGV would be fine - I'm just interested in whether there are any reads mapping to that region

[K1K]

Hi,

I´m so sorry for the late reply, but I decided to start the analysis again and then have a look at the igv, as you recommended.

Now, I have checked both genes in the igv and there are reads for every exon actually. But, I don´t understand, why I get a FPKM = 0, if there are so many reads. I don´t know, what to change in future analyses.

Best,

K1K

[Bukowski]

I just looked at the GenePattern workflow. I can think of a number of reasons why this might be happening still, but this workflow is EXTREMELY dated. It uses Tophat, which was superseded by Tophat2, which was superseded by HISAT which was superseded by HISAT2. There's insufficient detail on how it manages the cuffdiff workflow.

I'd seriously consider another route for this, perhaps looking for a documented HISAT2 > cuffdiff analysis workflow on Galaxy if you can't run the analysis via the command line.

[K1K]

Thanks a lot for your help. In the meantime, I also read that tophat is that dated.

I´ll try to run the analysis with the HISAT2 > cuffdiff analysis pipeline and hope to get more reliable results.