I have a database of 10 distinct biological samples. Each of these samples was sequenced (RNA-Seq) using paired-end reads and 6 barcodes. Thus, for each of the ten biological samples, I have 12 .fastq files, with names like *ATACTC_1, *ATACTC_2, *GTGCTC_1, *GTGCTC_2...and so on.

I would like to follow this pipeline:

1. Analyze data quality with FastQC
2. Trim data with Trim Galore!
3. Align with TopHat2
4. ??? generate counts, differential expression analysis, etc.

Here is my question: at what point in this pipeline can I (should I) combine all of the .fastq data together? If each sample has 12 files associate it, at what point do I collapse the 12 files into a single file representing the RNA-Seq data for a single biological sample that I can analyze for counts in step #4?

I'm guessing I combine all of the .fastq files up front (re-multiplex?) with `cat file1...file12`. I could also do steps #1-2 or steps #1-3 completely, and then combine the output of step #3.

Thank you for any help you can provide! This board has already been tremendously helpful to me.
I think it's most typical to combine raw reads if they correspond to the same library and original sample (so merging technical replicates, with respect to the sequencer, and not biological replicates). [This paper][1] was one of the early ones to test and validate this assumption.

So for your outlined process, that could be anywhere in steps 1 to 3. Personally, I would make count tables for all the 6*10 runs separately and then combine the count tables at the very end, before any clustering or differential analysis (so in the middle of your step 4). This is because all the earlier steps can be performed in parallel, and you might save some time by processing many chunks at once.

And just for completeness, you'll need to combine the two matching paired end fastq files (e.g. `X_1` and `X_2`) before aligning. You might need to combine them or analyze them together for the adapter trimming, or possibly trim adapters for each read end separately.

 [1]: http://www.ncbi.nlm.nih.gov/pubmed/18550803