The tuxedo suite you have used does consider biological variability. And I don't think it's right to change methods just because you think there is biological variability. This biological variability might be true. Another problem might also be that, you are using only two replicates. Ideally you would like to have three or more to represent true biological variability.

Two see if the analysis was performed correctly, look at a few control genes. I mean, look at the FPKM values of few genes, you know for sure should be expressed high or low based on your experiment.

Alternatively, you can also try using cummerbund. A data visualization tool by cufflinks. It's fairly simple to use and they have a good documentation.

Here's my 2 cents.... You've got a slightly more complex design than the tuxedo pipeline is able to handle (in my experience), that is, if your samples are from two different donors, I'd expect one of the major sources of variability in your samples, to be the 'donor effect' - which you can look for using a PCA plot.

​Providing that the main source of variation is donor, then I'd do as @Geek_y suggests, Tophat, htseq_count, DESeq2. The main reason for this is the fact that you'd need to account for Donor in your model design and as far as I'm aware, the model design in DESeq2 is much more flexible than Tuxedo.

If you're now thinking "Can I do 'Novel Discovery' with this method?", the answer would be sort of. You could do Tophat, Cufflinks, Cuffmerge, then use that GTF as your reference for HTSeq_Count, but that's a whole other area for discussion.

Hi,

To merge several fastq files, you can use:

cat input1.fq input2.fq > output.fq