Last week we finally got our results in MaxQuant and were very excited. Since then we have been thinking of how to examine and analyze them, and how to make sense of what we have got.
1) intensity based approach:
After determining the iBAQ average and sorting out the 100 proteinIDs with the highest intensities we ran them in UniProt to find out more information about what we had. I ran all the data in Phobius as well, to see what it would predict for signal peptides (SP) and transmembrane (TM) regions. To count the entries with specific information, like “proteaome” or “signal peptide”, I used the Excel-function COUNTIF to see how many out of all entries that belonged to the category I was interested in.
For example, to count all entries that were predicted to contain signal peptides according to Phobius, I would type:
Some statistics for the iBAQ-based results:
Proteins with transmembrane regions: 2
Proteins with signal peptides: 19
SPs predicted by Phobius: 21
2) R2-based approach
We also sorted out the proteins that had an R2 ≥ 0.8 and got 27 rows, all in all 33 proteinIDs that we ran in UniProt and Phobius. Magnus had written a Python code to a) check for linear trends over time and b) looking at differences between the first half of the experiment (time points 1-8) and the second half of the experiment (time point 10-17). We used the results from this to sort out the proteins with the largest increase/decrease in intensity.
Some statistics for the R2-based approach:
Proteins with transmembrane regions: 5 – 4 with 1 TM region, 1 with 2 TM regions
Proteins with signal peptides: 3
SPs predicted by Phobius: 4
3) Ratio of secreted proteins over time
Magnus has been programming in Python to write code for studying how the ratio of secreted proteins to all proteins vary over time. Over time, cells will die and then leak intracellular proteins and with the help of Magnus’ program we can study this trend. Check out what Magnus have been working on here, on his blog (here you can also read the code for the program studying linear regression).