Laura’s Notebook: An alternative look at proteins

Following up on my Proteins of Interest, Part II analysis, I am taking a second, simpler look at the peak area data. This time, I am simply taking the total peak area for each protein and averaging across each treatment:

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Then, using Galaxy, join the proteins that are up/down regulated by 5x with the annotations:

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Added column names:

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And counted the number of proteins in this list, which is 76. This file is found in the DNR_Geoduck repo

Then, using Galaxy I joined my two datasets to identify which proteins were found to be diff. expressed in both the treatment/site analysis and the treatment-only analysis. Using Revigo I did a quick visualization:

Expressed more in Bare patches:

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Expressed more in Eelgrass patches:

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Laura’s Notebook: Downloaded T & pH Data

The Avtech system allows me to download 30 days of data; I did so today, capturing pH & T data from 4/24 -> 5/16 @ ~1:30pm.

Notes:

  • Temp-w-3 probe is in the header tank
  • wish-Internal Temperature is recording the room temp
  • Durafet 3 is on the top shelf in a K population bucket. The top shelf houses HL & K groups, and receives a large amount of air bubbles, which I believe is driving the pH up to ~8 as it mixes with ambient air.

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Yaamini’s Notebook: Skyline Error Checking

Who wants to manually quality check peptides?!

Took 8.5 hours on a Sunday night, but I calculated an error rate for Skyline! I randomly selected 100 proteins in Skyline, and for each protein I checked one peptide. Ideally, these peptides had data availabe for every replicate. I evaluated whether or not Skyline chose the right peak, and if the peak boundaries were correctly deliniated.

For each peak, I assigned a “0” if Skyline picked the wrong peak and a “1” if it picked the peak correctly. For some peptides, there was no data available for a replicate. This would be a blank screen, so I gave it the value “N/A.” I then calculated error rates per replicate and peptide. You can find my spreadsheet here.

Here are some examples of Skyline spectra.

1: Skyline picked the right peak

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0: I assigned this value when Skyline picked the wrong peak. I also assigned this value when Skyline picked a peak where there was just noise.

Wrong peak:

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Noise:

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Changing peak boundaries:

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Laura’s Notebook: Interesting Proteins, part II

To determine over/under-expressed proteins eelgrass vs. bare treatments I did the following:

1) Replaced all “N/A” values with blanks:

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2) Created a pivot table to sum the total peak area for each protein, broken down by sample #.

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3) I copied the pivot table results and pasted into a new tab; I did this b/c oddly referencing the pivot table in a subsequent formula was not working. Entered the Total Ion Current (TIC) values into

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4) On a new tab, I assigned each sample # its corresponding site & treatment, then normalized the protein sum peak area by the TIC: %TIC = [peak area / TIC]*100 I highlighted cells with %TIC between 20%-99% in green, and those >100% in red. I’ll need to investigate why I have some proteins with a peak area than the TIC.

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5) On a new tab, I averaged technical replicates’ %TIC (i.e. same sample was run twice on Lumos), then calculated the fold change in eelgrass beds compared to bare beds, by SITE. For example: %TIC @ Case Inlet = Average %TIC CI-Eelgrass / %TIC CI-Bare

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6) On a new tab, created another pivot table showing fold change for each protein in eelgrass beds compared to bare, organized by site. I highlighted proteins over-expressed by 5x in green, and under-expressed by 5x in red.

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7) Then, I did some more re-organization and extracted a list of proteins in each site that were over/under expressed 5x:

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8) I moved this list to a new (much smaller) file. Next task: identify which proteins are consistently expressed differently in the two treatments. I re-structured the list of proteins & associated fold change into 1 column, and assigned each data point a site. Then, I created yet another pivot table to organize these candidate “interesting” proteins by site; As shown in this screen shot, in total there are 2382 proteins that were over/under expressed by a factor of 5 in at least one site: 866 in Case Inlet, 1153 in Fidalgo Bay, 910 in Port Gamble, and 666 in Skokomish.

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TBD …

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