Closed mmiladi closed 3 years ago
Hi @mmiladi , Thanks for your question. To date, I have only tested it on albacore2.1.7 and guppy3.1.5 and I did not see significant (not probability score based) change. The mod-unm sharp contrast in terms of different variants remains. It would be nice to test it on the latest guppy base-callers. If you are interested, you can download the curlcakes data from SRA and re-train some models with data from the new basecallers.
HI @mmiladi - just to add to @Huanle's answer: we have not tested systematically a trained EpiNano model across Guppy versions, but it should be a bit affected if the RNA model changes across versions (some Guppy upgrades maintain the RNA model but others change it).
If you want to compare datasets, I would recommend that they are all base-called with same base-caller (i.e. I wouldn't compare data base-called with 3.2.10 and 4.0.9, which use different RNA models, although differences are subtle). Also, please note that EpiNano-SVM was trained on Guppy 3.1.5 base-called data, but EpiNano-DiffErr should be independent of Guppy base-caller version. So you could analyze for example your data basecalled with Guppy 4.0.9 with EpiNano-DiffErr.
Hope this helped clarify your doubts!
Hi @Huanle and @enovoa , Many thanks for your prompt and detailed replies. Following your suggestion, I would continue with re-base calling the smaller subset which was called by version 4.x using version 3.x, before comparing the analysis. Best, Milad
Hello,
How sensitive is EpiNano m6A trained model to the Guppy version and subversion? I have data called using Guppy 4.0.9 and 3.2.10. Do they expect to work with the latest version of EpiNano? Would you recommend to re-base call them with a specific version of Guppy to get a reliable performance?
Thanks