Today I process these samples:
As you can see, there is a comment for each one saying “contaminated” because at the end of the isolation process when I was putting 50µL of 0.1% DEPC-treated water in each tube, I noticed by the fourth sample that the bottle with the water had a cloudy substance floating in it. I must have accidentally discarded the supernatant during the final alcohol wash into the bottle containing the water instead of the designated waste bottle.
The samples from the first sampling date for each of the three crabs was likely contaminated with the water, as such the remaining tubes are not usable either becuase they only encompass the second and third sampling dates for the crabs that I selected.
Huge bummer and now I know that I should always put the lid over the bottle even if it is just for a second so as to avoid making that mistake again. When you’re doing a protocol such as this that takes a long time and involves a lot of repetitive motion, you have to be super diligent.
I am going to be working from home tomorrow – organizing data sheets and editing and publishing the podcast – but since this weekend looks rainy, I’m going to come in to lab both days for a few hours at a time to get some more isolations done before the next crab meeting on Thursday!
Here is the set that I have chosen to replace the set that I messed up today:
Luckily there are still a lot more samples to choose from!
Spits out a file that joined the info based on the “tube_number” in common between the two spreadhseets. This was mostly done as a practice and tomorrow and over the weekend I’ll work more on my actual spreadsheets in GitHub crab project repo.
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Time to analyze the C. virginica data
Now that my two papers do not require my constant attention, I can start analyzing the MBDSeq data from the C. virginica project. The goal is to see if experimental ocean acidfication drove differential gonad methylation in adult oysters. This lab notebook entry will outline my plan and link to important information I’ll need down the road.
Sam received the FASTQ files and saved them here. The sample IDs follow numerical order, and are non-directional.
Here’s how I will process these samples:
- FastQC I previously used FastQC with some O. lurida transcriptome data, so I can follow the general steps in this Jupyter notebook.
- Bismark The purpose of Bismark is to align my sample files with the C. virginica genome, then extract data from methylated areas. I will first test my Bismark pipeline with a subset of one data file. Once I know it works, I will run all my samples.
Now that I know what I’m doing, I should probably do it…
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I have water chemistry data!
Sam ran samples from the beginning, middle, and end of my Manchester adult pH exposure on the titrator. He then calculated total alkalinity for these samples. I saved the information in this .csv file. More information is available in his notebook post.
For my paper, Steven suggested I make a table with average total alkalinity values for each sampling period for both control and experimental treatments. I used this R script to average total alkalinity values and calculate standard errors, and exported the data in this .csv file. It’s something I could have just done in Excel, but this way I have functional for loops in case I need to run more water samples and perform these calculations on a larger dataset.
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After discussing the preliminary DNA isolation attemp with Steven & Emma, we decided to proceed with DNA isolations on the remaining 0.22μm filters.
Isolated DNA from the following five filters:
DNA was isolated with the DNeasy Blood & Tissue Kit (Qiagen), following a modified version of the Gram-Positive Bacteria protocol:
- filters were unfolded and unceremoniously stuffed into 1.7mL snap cap tubes
- did not perform enzymatic lysis step
- filters were incubated with 400μL of Buffer AL and 50μL of Proteinase K (both are double the volumes listed in the kit and are necessary to fully coat the filter in a 1.7mL snap cap tube)
- 56oC incubations were performed overnight
- 400μL of 100% ethanol was added to each after the 56oC incubation
- samples were eluted in 50μL of Buffer AE
- all spins were performed at 20,000g
Samples were quantified with the Roberts Lab Qubit 3.0 and the Qubit 1x dsDNA HS Assay Kit.
Used 5μL of each sample for measurement (see Results for update).
Raw data (Google Sheet): 20180426_qubit_metagenomics_filters
|Filter #10 pH 7.1 5/15/17
|Filter #7 pH 8.2 5/15/17
|Filter #7 pH 8.2 5/1917
|Filter #10 pH 7.1 5/22/17
|Filter #10 pH 7.1 5/26/17
Samples were stored Sam gDNA Box #2, positions G8 – H3. (FTR 213, #27 (small -20oC frezer))
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I merged the Uniprot annotated table with each silo that had the quantitative and qualitative tags I previously made: new table .
I want to note this table includes proteins that are not abundant in each silo. I choose to include this for now since they are easily removable. I was thinking that some Revigo plots with the 0 abundance proteins might reveal some differences between the silos… There is ~1000 to 1500 proteins not expressed in each table (out of about 8400 proteins).
I’m making a new scree plot since my last scree plots weren’t with the right code (nhclus.scree(x, max.k=#)), however it has not been successful yet because of the amount of time it’s taking. I’ve let it run over 4 hours with no results produced. I am trying it one more time and am planning on letting it run overnight, however if it takes that long it may not be feasible since I need to do it 3 times and then run kmeans which takes a few hours itself…
Also, this came up lab meeting: changing max.print options in R .