I find it more helpful these days to simply point out the failure rate of transcript level measurements (because just about every wet bench scientist out there has ran into it), it is relatively cheap and easy to get those transcript measurements. (However, I've still never been offered a $100 genome. Have you? I hear it's a thing, but it still seems like $100s plural).
What if it still had some value (besides finding point mutations in variant call files, of course!)?
These authors suggest straight out heresy and suggest implying that you could integrate these data to group those peptide IDs into actual protein data better. Proteoform data thanks to RNA?
All you need to do is -
Okay, but since my hiragana is not good nor has it ever been good, I have to just skip over this in every paper. It's just impressive to see this much written out in one block (I didn't even capture it all). AND you can get all the code at this Github.
What matters is that they demonstrate that peptide level TMT data integrated in this approach improves their analysis. It also lends support to a hypothesis that 2 proteins are very differential in this disease. Two proteins that are so close together in sequence homology that your standard proteomics pipelines probably just lump them into a single group!
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