Today I worked on the revisions sent to us from the editors of Marine Ecology. There were 62 revision requests, with the vast majority being grammatical.
I also am learning more about BLAST and Jupiter notebooks. Mostly have been reviewing what I’ve learned in the past. The goal is to blast Rhonda’s data.
Tomorrow I will practice running BLAST (link to GitHub practice repo: here ), and read through the methods of a paper, “Influence of temperature on larval Pacific oysters (Crassostrea gigas) protein expression”. Link here .
The following is data on Oly survival from larvae to juvenile. Data is adjusted for a few events where larvae were lost due to overflowed bucket, etc. Data was adjusted by calculating the mean % larvae accounted for from one bucket count to the next (pretty good, 94%), then taking the difference between the actual # counted during screening after loss event and what would be predicted by the 94% accountability rate.
Charts include survival % within each population, as well as the total number of larvae stocked in culture buckets, for reference. As a reminder, larvae were continually stocked in culture buckets, with the maximum density of 200,000 larvae / bucket at any time. Continual stocking was possible due to a) graduating 224um larvae up to setting silos, and b) mortality. Also, reminder that I “culled” morts via a 2-bucket flow through system where all larvae were stocked in the first bucket, and only live larvae (which swim) flow into the second bucket, and the contents of the first bucket was discarded. Note: Hood Canal juveniles have yet to be counted, since they are too small, and therefore are not included in these charts.
Treatment groups are color coded as well as identified on the x-axis, where:
- Orange: High T (10degC), Ambient pH (7.8)
- Red: High T (10degC), Low pH (7.2)
- Blue: Ambient T (6degC), Ambient pH (7.8)
- Green: Ambient T (6degC), Low pH (7.2)
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I took over for Yaamini at Manchester on Friday, August 11th and Sunday, August 13th for her gigas OA larvae screening and sampling.
On Friday, a volunteer named Stephanie helped with screening the larvae on 80 and 60 micron screens. Halfway through the day, Kelsey was able to help. Kelsey screened and Stephanie did the bucket and screen cleaning, replacing of air stones, and feeding.
I sampled larvae from each screen size from each of the 24 buckets. I counted the larvae from three samples of 250µl killed with googols and took a fourth sample and placed in a tube with ethanol for freezing in the -80. I then restocked the larvae.
Sunday was a similar day, and Kaitlyn came and helped. She was in charge of screening and cleaning and shuffling the buckets. I did the sampling and counting again.
On both days, I also drained and refilled the header tank to prevent anoxic conditions, and centrifuged and removed the ethanol from the larvae samples for the -80 freezer.
One of the first steps in processing SRM data is to confirm that the selected peaks actually represent the peptides, aka that our assay works. To do this, we use linear regression between PRTC retention times in DIA and SRM to calculate predicted transition RTs in collected SRM data. Then, we calculate the R^2 for PRTC and experimental peptides compared to predicted.
Copied PRTC peptides from another Skyline project file, pasted into my Geoduck assay Skyline file.
The method file used in mass spec run resulted in us not collecting all PRTC peptides, so I removed those from the Skyline document.
Some of the PRTC peptides had no signal in several of my replicates. These peptides eluted at ~14 or 18 minutes. I removed the peaks from all reps which had no signal at the predicted RT. In this screen shot the peptides with lots of signal missing are unfolded on the left side, and one of the cruddy reps’ chromatogram is pulled up. There should be a peak @ around 17mins.
Notice that the PRTC peptide signals vary, which is due to me using different PRTC mixes. I documented which mix I used, and the concentrations of each, so I will need to account for that when I normalize my assay daya using PRTC data. More on that later.
Export retention times from Skyline via File -> Export -> Report -> Peptide RT Results. Saved in my Geoduck-DNR Repo as 2017-08-11-SRM-Retention-Times.csv
more in a minute…
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Before I export my data from Skyline for data analysis, I have the following final things to do:
- Adjust peak boundaries
- Determine if any .raw files should be discarded, based on poor-quality data and those that I re-ran & re-made
- Look @ all blank runs to see if there are any weird signals
- Check out blank samples
Adjusting peak boundaries
Some helpful keyboard shortcuts:
- Scroll between replicates: Ctrl+Up or Ctrl+Down
- Auto-zoom to best peak: F11
- Un-autozoom out from best beak: Shift+F11
Some transition peaks look poor, but they are present. Here is an example of replicate data for the Superoxide Dismutase protein, showing the overall view and the zoomed view:
This is in comparison to the following replicate, where there is no peak present betwen RT 14-15:
Not sure what to do in the situation where a peak is split into 2 peaks (as below); Skyline opted for the boundaries to encompass both peaks. I will do the same, as the total RT for both peaks appears to be similar to that of other reps:
- Poor quality reps: 178, 254, 208, 212, 213, 297_170728020436,
- A peptide with RT ~18 must be co-eluting, as it pops up in a few res/peptides. Perhaps it is the [PEPTIDE W/ 18], that gets stuck in the column and pops up. For example, the following is a zoomed-out view of Ras-related protein peptide, which should have it’s peak around 22.7. This is rep #254, but it pops up in lots of reps: A couple peptides elute @ ~18min, and could be the culprit: Sodium/potassium-transporting ATPase subunit alpha-4, MVTGDNVNTAR; Catalase, LYSYSDTHR;
Total actions performed on SRM data in Skyline:
- Removed peaks from replicates where no peak was present @ designated retention time (as per DIA/SRM regression). Replications w/ no peaks for peptides will be represented as #N/A when data is exported.
- ID’d peptides with very poor data across multiple replicates; I may not use these peptides in my analysis; TBD. See peptides in red
- Adjusted retention time boundaries for all reps all peptides. I erred on NOT adjusting boundaries if they looked OK to maintain consistency.
- ID’d and deleted 2 transitions that do not align with other transitions at designated RT. Transitions are:
- Superoxide dismutase, TIVVHADVDDLGK, y4
- Ras-related protein Rab-11B VVLVGDSGVGK, y4
- Documented peptides and transitions w/ poor quality over several samples here
Exported data from Skyline:
Export -> Report, then I edited the Transition Results report with the following metrics: Protein Name, Transitions, Peptide Sequence, Fragment Ion, Peptide Retention Time, Area; I selected “Pivot Replicate Name”. Here’s a preview of the report:
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