We apologize for not being clear enough within the text and we thank the reviewer to point it out. In Appendix we now relate our metric to current ones, together with Psi. Splicing yield can't be computed from steady-state RNA-seq information because synthesis and degradation are entangle in regular-state information. We have additionally edited the text extensively to ensure that is now easier to understand and we belief the reviewer agrees. […] 1) This manuscript is both technically and biologically dense, with the mathematical modeling and structurally based interpretation of the ensuing kinetic information.

Thank you for submitting your article 'Global donor and acceptor splicing site kinetics in human cells' for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by Douglas Black as Reviewing Editor and James Manley as the Senior Editor. 6) I actually don't understand the splicing yield measurement and, notably, how it differs or compares to a standard percent-spliced-in worth that's commonly used to judge splicing patterns in RNA-seq datasets.

We furthermore hope that we could persuade this reviewer of the difficulties of utilizing extra refined fashions as a result of they endure from poor identifiability of the parameters. A paper shouldn't be rejected by eLife based mostly on notion that it is an “train”. Instead, the group should decide over the years to come back how useful our data and conclusions are for transferring the sphere forward.

We thank the reviewer for the positive comment about splicing yield. However,splicing yield just isn't instantly accessible without kinetic modeling. We have now clarified that we're not estimating immediately the charges of the individual transesterification step, which would certainly lead to these complications. Also, we've used branchpoint predictions later within the manuscript for predicting the charges from sequence , which is a sensible approach to move ahead as maps of branchpoints are nonetheless lacking. These turned out to have very cheap predictive power. Taken together, our conclusions are not compromised by the truth that multiple branchpoints might contribute.

4) The use of simulations to estimate the robustness of the mannequin fitting, throughout each the primary order kinetic model and different fashions is a useful addition to the paper. It must be highlighted extra in the primary textual content how this helps the validity of the strategy. An evaluation also needs to be supplied for a way the strategy performs and the place it doesn't work well. For example, the authors point out that 'the robustness of our method… in keeping with high accuracy of the becoming procedure on simulated knowledge.' However, all fashions or experimental methods are underpowered in sure instances.

The authors ought to embody a short dialogue of where their model falls brief (extremely-quick charges, length issues, etc.) and thus extra totally inform the reader in regards to the organic interpretations that may be drawn from the info. Simulations are sometimes the easiest way to push the model and check these extreme circumstances.

What happens when solely take a look at learn abundance over these sites – is using the charges telling us something different? Would be useful to place this in context of a metric that readers may be more used to thinking about so as to give it the correct interpretation.

The cut-off of 100 reads is the one we now have utilized to the true data. To check the shortest and longest bond half-lives that our experimental strategy is able to seize and model, we stratified the results by bond half-live. We then proceeded equally with the bond synthesis charges. As a result, we found that the majority of our modelled information lies within a range during which the median relative error of synthesis rates and half-lives is beneath 100% or 30%, respectively (Appendix—figures 24, 25). We added a paragraph to the appendix (Appendix subsection “4sU incorporation”) to clarify this additional. We strongly disagree that our paper is a “bioinformatic exercise”. As demonstrated at various points all through the manuscript, we obtain organic insights and may explain structural data.