Grace’s Notebook: July 7, 2017

(1) Re-submitted the Heare et. al paper entitled, “Evidence of Ostrea lurida Carpenter, 1864 population structure in Puget Sound, WA” to Marine Ecology. After contacting the editor, we were able to get an extension on the resubmission deadline as we missed the original deadline of June 26th.

(2) Played around with Rhonda’s geoduck OA data and summarized the data and results:

Morphology

Over the course of two weeks, the average sizes (microns) of geoduck larvae were recorded. The larvae were separated into six conicals. Three conicals were at pH 8.2, and three were at pH 7.1.

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Figure 1. The larvae exposed to the lower pH (7.1) were smaller on average by about 90 microns at the end of the experiment on 5/28/17. (x-axis: date; y-axis: size in microns; key: conical numbers and the pH treatment value→ chart made by Rhonda – personally am unable to add chart features such as axes labels, title, etc.)
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Figure 2. On average across all sampling dates, the geoduck exposed to pH 8.2 were larger by about 25 microns than those exposed to the lower pH (pH 8.2 average lengths – 230, 227, 228 microns; pH 7.1 average lengths – 198, 202, 201 microns).

 

Mortality

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Figure 3. The total mortality observed in each conical and treatment across all sample days. Total number of larvae sampled per treatment and conical = 350 larvae (50 larvae per day and 7 days of sampling).
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Figure 4. Across the entire experiment, 350 larvae were sampled from each conical. To determine percent mortality: total number dead divided by 350 total larvae sampled times 100. pH 7.1 and 8.2 weren’t different for conicals 4,6,7,and 9. But conical 8 (pH 8.2) had more than double the mortality of conical 10 (pH 7.1)

 

Kaitlyn’s Notebook: Unique Expression

I have continued working with Rhonda’s data and did some gene enrichment analysis on any proteins that had abundance on any day of the experiment after 0 abundance on day 1. I used Animal Genome for GO terms and produced a graph based on those GO terms.

I also made a graph for GO terms that had 0 abundance on any day of the experiment after some abundance on day 1.

I also thought it would be worthwhile examining what process seemed to change overall. Therefore I combined the data and produced the following graph:

Although biological process isn’t descriptive, for many proteins that was the only GO term which can be seen in screenshots and charts here. Furthermore, of the proteins I identified, many were not enriched which is why I choose to analyze gene enrichment for proteins that appeared or disappeared at any point in the experiment.

However I have now produced excel sheets that can identify proteins that were expressed only 1, 2, 3, 4 or all 5 days after no abundance on the first day. In other words we can now look at proteins based on the number of days they appeared after 0 abundance. This is also separated by silo as the analysis was before.

You can see that the original data was converted to a dichotomy using R and then based on the sum of those columns, we can identify proteins that were abundant for 1, 2 , 3, 4 or all 5 days. I included the original data so that we could identify if any of those proteins were abundant at very high levels such as the protein in the second photo that was expressed on only 1 day but at almost 40 abundance. I also included some annotations that we can look through as well.

Silo 2- unique expression based on days abundance

Here are the links for the other silos:

Silo 3- unique expression based on days abundance

Silo 9- unique expression based on days abundance