Regarding your paper entitled "3V: cavity, channel and cleft volume calculator and extractor", which I read carefully.
I’ve a question for you. In the abstract, it is written the following:"It rapidly finds internal volumes by taking the difference between two rolling-probe solvent-excluded surfaces,…", but I think you mean "two imaginary rolling-probe solvent-excluded surfaces" because after looking at your code, I haven’t seen any analytic SES formulation therein. I guess you are just using two probe spheres of distinct radii to account for cavities, not the analytic SES themselves. Am I right?
I am not certain about your use of the term "imaginary", but I would say my method is a "discrete approximation" to the SES. And because it is discrete (i.e. a 3D grid) one can simply subtract one grid from another. See attached figures.
With small grid sizes (0.2 A), I see very little discrepancy to the analytical solution.
I read about the recently published software for deconvoluting pervasive and autonomous retrotransposons. Could another calculation be added to the software’s output which estimates the abundance of ORF1 and ORF2, the parts of the retrotransposon which are translated into protein? I’m not experienced in this research area, so I am unsure of how feasible that is. I would like to make an approximation to the ORF1 and ORF2 protein abundances using RNA-seq.
Thanks for reaching out here and on GitHub. This is an interesting question and suggestion. Unfortunately, estimating the rate of protein abundance of ORF1 and ORF2 from RNA-seq is extremelly hard. There are essentially two factors that make it difficult to estimate protein abundance from transcriptome data. The first is technical. RNA-seq has a strong bias to overrepresenting the 3′ or transcripts, therefore, ORF2 would most likely be overestimated. This is issue is easily addressable.
The second one is more biological: LINE-1 is tightly regulated at many different levels. No only LINE-1 transcription is regulated but there are also many post-transcription mechanisms that either boost or stop LINE-1 translation. This is not only true for LINE-1, in general, estimating protein abundance from RNA is a hard problem (https://www.nature.com/articles/nrg3185).
That said, I’m really interested in this question. In theory, we could use machine learning algorithms to predict ORF1 and ORF2 protein levels based on RNA-seq if we had enough data. This could be an interesting followup work after TeXP
I would like to run your new SVFX method on some structural variants. For full disclosure, I’m working on a method to assess the pathogenicity of germline SVs, and would like to compare with yours. Based on reading your preprint, I believe our methods are quite distinct in terms of training data. I think it’s great you’ve already put code on github, but I’m not sure what data files are needed to run the code. Could you put me in touch with one of your students to help me run SVFX locally?
Thanks for your interest in SVFX. We have reported our feature list in supplement table1.
Overall, our feature list is extracted from a bunch of genomic annotations and various functional genomics/epigenomics signal files.
You can download signal files from iHEC or epigenome roadmap data portal. As you might have noticed, we created multiple tissue-specific models for our analysis.
For the germline model, we also built a feature matrix based on the h1HESC cell line, which performed quite well. On the SVFX GitHub page, we have uploaded the bed file for different annotations (under the data folder) used in our study.