I am familiar with the fact that paired-end reads are used to generate FPKM, and single-end reads are used to make RPKM. I recently received a sample report from a third party RNA-seq service which provided me with FPKM normalized read counts for each transcript and sample in spreadsheet.
That was fine until I examined the .fastQ files they gave me, I found the following format for each read
@XX100011323L1C001R014_28 GAAAAACTCAAATCGCCTCTAAGAAAAGACGAAGTCGAAGAAAGAGACAA + eeeeeeeeeeeeeeeeee\eeeeeeeeedeeeeeefeeZeeeeeeeefZc
Given that there is no /1 or /2 at the end of the @ identifier, and further that all those @IDs in the .fastQ file contain an "R" (suggesting reverse?), I am wondering how in the hell they generated FPKM-- and most importantly whether or not these people have just given me the runaround. Is this an interleaved .fastQ?
In their report they provided the parameters they would use for mapping both PE and SE reads in Bowtie2 and HISAT, which seems odd, since they only seem to provide FPKM to everyone. Here are the arguments:
Bowtie2 parameters for PE reads: -q --phred64 --sensitive --dpad 0 --gbar 99999999 --mp 1,1 --np 1 -- score-min L,0,-0.1 -I 1 -X 1000 --no-mixed --no-discordant -p 16 -k 200 Bowtie2 parameters for SE reads: - q --phred64 --sensitive --dpad 0 --gbar 99999999 --mp 1,1 --np 1 --score-min L,0,-0.1 -p 16 -k 200 HISAT parameters for PE reads: -p 8 --phred64 --sensitive --no-discordant --no-mixed -I 1 -X 1000 HISTA parameters for SE reads: -p 8 --phred64 --sensitive -I 1 -X 1000
After that, they said they used RSEM to calculate FPKM.
Then, laughably, they said,
The FPKM method is able to eliminate the influence of different gene length and sequencing discrepancy on the calculation of gene expression. Therefore, the calculated gene expression can be directly used for comparing the difference of gene expression among samples.
Which I think we all know isn't quite true unless you're using a trimmed mean of M adjustment anyway because total FPKM/sample is always a little different.
If anyone can give me some insight on this one, I'd be much obliged.