Interesting paper titled IVT-seq reveals extreme bias in RNA-sequencing was published last week. Summary of it: http://blog.genohub.com/rrna-depletion-responsible-for-coverage-bias-in-rna-seq/
After reading the paper, I have the impression that your headline focussing on rRNA depletion is very misleading, given the data. There is also bias in poly-A selection. Fig. 4B would suggest that the variation in coverage is even higher in poly-A selected samples than in rRNA depletion, because poly-A shows a much heavier tail. And the effect size is larger (Fig. 4C). On the other hand, if the variance in coverage in poly-A selection is only due to the 3' bias, then poly-A selection would gain an upper hand, because there are protocols with much less 3' bias available.
The other sources of variability (random priming and "sequencing-specific molecular biology common to all libraries") are always the same between the two methods.
The good thing is that even the "high, unpredictable coverage" (hunc) regions are reproducible between samples, so its a systematic error which should not affect differential expression analysis.
I assumed Ribo-zero has off-targets but I haven't realized it has such a tremendous effect. For bacteria, where ~90% of RNA is rRNA and no poly-A to select this issue is very big, are there any other solutions (other than sequencing ten times deeper)?
IVT-seq is nice because it identifies the problem, but I'm not aware of current tools / methods that address the problem. Will users have to perform total RNA-seq (no selection, no depletion) AND polyA / rRNA depletion RNA-seq for each sample to look at expression AND address the bias ? I'd love to hear if someone has a solution.
--Genohub
We ran into this same problem at Zymo, and we now have a solution: Probe-free depletion that is Universal (works with any species) and Unbiased. https://www.zymoresearch.com/pages/total-rna-seq-library-prep
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The major difference between the poly-A selection bias and the Ribo-zero bias is that the former can be evaluated and corrected. Both biases stem from the fact that the transcripts are partially fragmented in the first place, otherwise the entire mRNA would have been caught in the poly-A selection and the entire mRNA would have been excluded with the Ribo-zero kit. Using the pattern of slope along the gene in the poly-A library (like in Fig 4A) you can compute the fragmentation rate and get a corrected mRNA level which will be uniform along the mRNA using one parameter. In the Ribo-zero case, however, you don't know the probes they used so trying to correct the pattern is impossible.
It is true that the biases are reproducible but they will have an effect is several condition. For instance, if the amount of rRNA changes between two samples I think that the changes between different regions of mRNA will not be linear in the concentration of rRNAs, resulting with different bias for different genes, and when the fragmentation level is not uniform in two conditions, where the half-life of the mRNA is different, the effect will be more smear where then are less fragments and steep when there are more fragments.
Good point, I changed the title.