Selecting SRM Targets Part 5

I have draft transitions!

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.

Written on July 7, 2017