Yaamini’s Notebook: Virgnica MBDSeq Day 2

A fulfilling day of labwork!

JK.

This morning I was prepared to start my labwork, but when I talked to Sam about my time restrictions for the day, we both decided it would be better to start working tomorrow instead. I went through the MethylMiner protocol and calculated the amount of DNA I would need from each sample, and the amount of a 1X Wash/Buffer needed for tomorrow. You can find my calculations here.

Tomorrow’s work entails preparing the beads that bind to the DNA. I need to wash the beads, then bind the MBD-Biotin protein to them. Finally, I’ll incubate the beads and protein to my sample DNA overnight. I’m ready to get started!

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Laura’s Notebook: Oly Genetics 104, NF GenePop Analysis

Tried to do the html to .md trick for this notebook, but it did not function. No biggie, since there are no pretty plots in this notebook. Original notebooks: R markdown version, NF-GenePop-Analysis.Rmd; HTML version, NF-GenePop-Analysis.html

In this notebook I will use the GenePop R program to analyze the 2016/2017 Fidalgo Bay (NF) Ostrea lurida microsatellite data; the results from each analysis are housed in a series of .txt files.

Prior to importing the data, prepared the 2016/2017 NF data in Excel and exported into GenePop format; resulting file is available on 2018-01-22-Preparing-for-Genepop.md

First, install GenePop program;

  install.packages("genepop") library(genepop)  

Pull basic information on allele and genotype frequencies per locus and per sample

  basic_info(inputFile="Data/Oly2016NFH+2017NFW_Merged.txt", outputFile = "Analyses/NF-Basic-Info.txt", verbose=T)  

Resulting file: “NF-Basic-Info.txt” Hetero- and homozygosity info pasted here:

NF Wild

Loci Olur10 Olur11 Olur12 Olur13 Olur15 Olur19
Expected Homozygotes 4.99 24.59 11.83 6.68 6.66 6.64
Observed Homozygotes 2 28 12 7 6 7
Expected Heterozygotes 92.01 70.41 86.17 91.32 91.34 90.36
Observed Heterozygotes 95 67 86 91 92 90

NF Hatchery

Loci Olur10 Olur11 Olur12 Olur13 Olur15 Olur19
Expected Homozygotes 5.02 23.03 13.44 7.14 7.22 7.10
Observed Homozygotes 7 27 5 6 6 7
Expected Heterozygotes 92.98 70.97 86.56 88.86 90.78 88.90
Observed Heterozygotes 91 67 95 90 92 89

Assess whether loci are in Hardy-Weinberg Equilibrium

  test_HW(inputFile = "Data/Oly2016NFH+2017NFW_Merged.txt", which="Proba", outputFile = "Analyses/NF-HWE.txt", enumeration = FALSE, dememorization = 10000, batches = 500, iterations = 2000, verbose = interactive())  

Resulting file: “NF-HWE.txt” All P-values across loci in each population are »0.05, do not reject the null hypothesis that all loci are in HWE.

 Pop : NFW-2017

Laura’s Notebook: Oly Genetics 103, preparing microsat data for analysis in GenePop

New day, new genetics analysis work flow. This time I’m going to use GenePop, a standard program that (apparently) does everything I need it to do!

Checking 2016/2017 Fidalgo Bay raw data for correct binning

Crystal rounded the raw microsat data for the Fidalgo Bay 2016-hatchery and 2017-wild data. She provided both raw and rounded data. Before moving forward with the rounded data, I’ll check out the binning method she used.

In the Excel file Olympic Oyster NFH_NFW (1).xlsx she includes data from both wild and hatchery NF samples. She houses raw data for each locus in separate tabs, creates a list of “bins” at 0.2 increments, calculates frequencies for each bin and visualizes with histograms.

image

Then, using the frequency distributions she assigned alleles, for example:

image image

One question I have is regarding the assignment of all even-numbered alleles for Oly10, Oly11 & Oly12, while alleles are odd for Oly13, Oly15 & Oly19.

I also noticed that Oly18 data was initially processed, then not completed nor included in the “rounded” tab. I emailed Crystal to see what’s up (I presume it was an oversight).

Next step is to export the data into a GenePop format. GenePop is one of the most commonly used programs used to analyze microsatellite data. There are several ways to use GenePop: on the web, at the command line, and in R. I like to work in R. I could not find an R-based function to convert .csv format to GenePop format, however thre is an Excel plug-in caled GenAlEx that one can use. I download version 6.503 (Dec 5, 2016). Then, I merged the wild and hatchery data into one spreadsheet. I also found online that the commonly used “genind” format has a few key formatting requirements, which I point out in the following screenshot:

snip20180122_24

With this merged file open, I also opened the GenAlEx program. Then, I used the GenAlEx plug-in to export the file as a GenPop formatted .txt file:

image

A window pops up, which should automatically ID the #loci & #samples if you formatted the spreadsheet like I did; I edited the “Title” to include 2016/2017 info.

image

Saved the file as a .txt file under Oly2016NFH+2017NFW_Merged.txt; here’s what the resulting PopGen formatted file looks like:

image

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Yaamini’s Notebook: Comments and Tags

I pimped out my notebook!

I finally sat down and enabled Disqus commenting for my lab notebook posts and figured out how to tag my lab notebook entries. A quick how-to on both:

Comments:

  • Register on Disqus
  • At the top of each entry, add the text “comments: true”
  • Copy and paste the following code at the end of the lab notebook entry between an and
 
/** * RECOMMENDED CONFIGURATION VARIABLES: EDIT AND UNCOMMENT THE SECTION BELOW TO INSERT DYNAMIC VALUES FROM YOUR PLATFORM OR CMS. * LEARN WHY DEFINING THESE VARIABLES IS IMPORTANT: https://disqus.com/admin/universalcode/#configuration-variables*/ /* var disqus_config = function () { this.page.url = PAGE_URL; // Replace PAGE_URL with your page's canonical URL variable this.page.identifier = PAGE_IDENTIFIER; // Replace PAGE_IDENTIFIER with your page's unique identifier variable }; */ (function() { // DON'T EDIT BELOW THIS LINE var d = document, s = d.createElement('script'); s.src = 'https://the-responsible-grad-student.disqus.com/embed.js'; s.setAttribute('data-timestamp', +new Date()); (d.head || d.body).appendChild(s); })(); <noscript>Please enable JavaScript to view the <a href="https://disqus.com/?ref_noscript">comments powered by Disqus.</a></noscript>
  • Use the following code to count comments
 //the-responsible-grad-student.disqus.com/count.js  

Tags:

  • Add “tags: “ at the top of each lab notebook entry
  • List some tags after the colon! Helps if they are lowercase. Use a space between words to differentiate between tags (ex. “DNR labwork” sets two tags: “DNR” and “labwork”). Use a hyphen for multiword tags (ex. “mass-spec,” not “mass spec”).

Yet another thing I can cross off of my to-do list.

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Laura’s Notebook: Oly Genetics 102, preparing microsat data for analysis in GenePop

New day, new genetics analysis work flow. This time I’m going to use GenePop, a standard program that (apparently) does everything I need it to do!

Checking 2016/2017 Fidalgo Bay raw data for correct binning

Crystal rounded the raw microsat data for the Fidalgo Bay 2016-hatchery and 2017-wild data. She provided both raw and rounded data. Before moving forward with the rounded data, I’ll check out the binning method she used.

In the Excel file Olympic Oyster NFH_NFW (1).xlsx she includes data from both wild and hatchery NF samples. She houses raw data for each locus in separate tabs, creates a list of “bins” at 0.2 increments, calculates frequencies for each bin and visualizes with histograms.

image

Then, using the frequency distributions she assigned alleles, for example:

image image

One question I have is regarding the assignment of all even-numbered alleles for Oly10, Oly11 & Oly12, while alleles are odd for Oly13, Oly15 & Oly19.

I also noticed that Oly18 data was initially processed, then not completed nor included in the “rounded” tab. I emailed Crystal to see what’s up (I presume it was an oversight).

Next step is to export the data into a GenePop format. GenePop is one of the most commonly used programs used to analyze microsatellite data. There are several ways to use GenePop: on the web, at the command line, and in R. I like to work in R. I could not find an R-based function to convert .csv format to GenePop format, however thre is an Excel plug-in caled GenAlEx that one can use. I download version 6.503 (Dec 5, 2016). Then, I merged the wild and hatchery data into one spreadsheet. I also found online that the commonly used “genind” format has a few key formatting requirements, which I point out in the following screenshot:

snip20180122_24

With this merged file open, I also opened the GenAlEx program. Then, I used the GenAlEx plug-in to export the file as a GenPop formatted .txt file:

image

A window pops up, which should automatically ID the #loci & #samples if you formatted the spreadsheet like I did; I edited the “Title” to include 2016/2017 info.

image

Saved the file as a .txt file under Oly2016NFH+2017NFW_Merged.txt; here’s what the resulting PopGen formatted file looks like:

image

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Laura’s Notebook: Oly Genetics Meeting Recap

Met with Brent to discuss initial analysis of Oly genetics data, interpret results and develop a new to-do list. Here’s what I learned:

  • Hardy-Weinberg Equilibrium: I had interpreted that all loci are not in HWE. Need to re-interpret results, as they should be. I possibly mis-interpreted the analysis.
  • Linkage disequilibrium: I found evidence for linked loci, particularly between 13, 15 & 19. From Brent: these loci were tested and selected because they weren’t linked. I should use another program/function to re-assess linkage. If they are confirmed as linked then I’ll need to throw out all but one of those that are linked.
  • Null alleles: The analysis that I performed was confusing, need to research further to figure out how to interpret results. Brent suggested that I use MicroChecker, which is easy and is the standard, as a secondary analysis.
  • Genetic diversity: often the metric for this is “Observed heterozygosity” vs. expected.
  • Allelic richness: can still report this, but since my sample sizes are very similar (99 vs. 100), don’t need to focus on it.
  • Fst stat: I found values between population to both be 0. This is good (full gene transfer), however shouldn’t use this stat as the standard.
  • Effective population size: not easy to calculate, wouldn’t add anything to this analysis.
  • Relatedness: could calculate level of relatedness (Brent suggested Co-ancestry program); this would be interesting, but isn’t completely necessary for the conclusions we are trying to draw.
  • Need to perform Fischer’s Exact Test, which is a powerful way to determine allelic divergence. GenePop does everything I need it to do.
  • Need to do a power analysis to determine what kind of power I have to detect differences with this # loci and alleles. Brent suggested PowSim.
  • For Crystal’s data: generate a quick plot of the raw data to make sure binning/rounding was performed correctly.
  • For presentations:
    • show plots with allele frequency distributions for each wild/hatchery population comparison
    • these are neutral markers, so we can’t draw conclusions regarding non-neutral markers of adaptive significance
    • these data provide a snapshot, and show that there is no strong divergence in these neutral markers
    • but, this is only 6 microsatellite markers. Questions of hatchery selection remains, and can really only be answered via higher resolution data (SNPs).

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