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Selecting SRM Targets Part 5 |
DNR SRM MSstats |
Emma wasn't kidding when she said sorting through proteins on Skyline takes a while. Even so, I have preliminary target transitions! My steps are laid out in a Jupyter notebook.
To come up with the transition list I sent Emma and Steven, I made a duplicate Skyline document to edit. I followed the rough instructions in Emma's Skyline tutorial slides to delete bad quality proteins, peptides and transitions.
Deletion criteria:
- Delete a protein if it only has one associated peptide
- Delete a peptide if
- There are less than three transitions
- There is too much peak intereference, so the peak isn't clearly defined (i.e. a sloppy peak)
- There is missing data for samples
- Delete a transition if
- It is a precursor ion
- It has low abundance (want to keep the three most abundant transitions)
- It is noisy
For example, the least abundant transition in this peak (highlighted in red) is noisy...
...so I deleted it.
While the above photos show the same peptide and transitions, the sample number is different, leading to this peak! For this reason, I needed to use the deletion critera for all samples. If there were more than two peaks between samples, I deleted the peptide.
I first sorted through the proteins Steven marked as "interesting" in my shortlist. Some of those interesting proteins had poor quality peaks, so I looked for proteins with similar annotations to examine instead. Overall, I narrowed 9000+ proteins down to 23 proteins, with 64 peptides and 228 transitions. I was stuck as to how to narrow it down even further, so I figured I would have Steven and Emma help out. Tomorrow, I'll use their feedback to narrow down my list down to 100-150 transitions.
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