protocols & code

Please check out our Github for Protocols & Code for:

  • Population Genomics Analyses
  • RNA-Seq Data Analyses
  • Molecular Laboratory Protocols
    • e.g., Extracting RNA, DNA and proteins from reptilian and fish tissues and downstream analyses such as library prep, qPCR, microarrays & enzyme assays
  • Field & Wildlife Sample Collection Protocols
    • Sampling for genetic, physiological and contaminant analyses in skin, whole blood, RBCs, plasma, organs and carapace tissues
  • Introduction to R and graphing
  • NGS data manipulation
  • Other Data Analyses
    • qPCR data pre-processing
    • GLM/GLMM and other statistical analyses
    • mapping in R
    • data visualization
    • microarray analysis
    • synthesizing and analyzing long-term environmental monitoring data in R

*Note this is very much a work in progress as I convert files into easily shared formats, so everything above is not updated yet. Please contact me if you are having trouble finding/using any of these resources*


Additionally, some other great resources are:

  • R for Data Science – Hadley Wickham has somewhat of a cult following for a reason. Garrett Grolemund and Hadley just released this book/resource (note that ‘R for Data Science’ was formerly called Data Science with R in Hands-On Programming with R) that not only has a lot of really useful tools for wrangling data, but also introduces conceptually how we think about and approach the different components of data analyses (including visualization and communication). Looking forward to using this teaching soon!
  • We created a repository for the Fangue Laboratory fish physiological and transcriptomic protocols, which can be accessed through the lab website with a guest login (email me or Dr. Fangue).
  • Interested in eDNA? Washington State University has put together a really great informative page full of great resources here!
  • Brian Cheng has already started a very useful list of R resources that we’ve both used a lot, so check it out here. Thanks Brian!
  • Also, there are good webinars for different aspects of RStudio here-in particular, R Markdown is awesome for anyone who needs to add/adjust data & analyses and re-run longer scripts and save output, graphics, etc.
  • I’ve also been playing around with Jupyter with IPython to ease my entry into  python-so far it is helpful, in the same way that RStudio lowers the barriers for starting out in R (**though I still recommend that everyone work at least a bit with command line to make sure you understand what your code and the gui wrappers are doing!)

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