ipyrad includes a suite of analysis tools that are designed to make it easy to run inference programs (e.g., STRUCTURE, BUCKy, BPP) on the data in an efficient way by sampling distributions of loci or SNPs from your RAD data, grouping individuals into populations, filtering for missing data, and parallelizing computation.
In this section of the documentation we have a number of example analyses in the form of Jupyter notebooks, which is a useful tool for doing reproducible science. In fact, ipyrad has been designed since its inception for the goal of working seamlessly within jupyter. Check out the tutorials below on using Jupyter notebooks, and using ipyrad in notebooks. Then check out the analysis tools notebooks.
This is an optional tool to use with ipyrad, but one that we strongly recommend learning. See the video and link below to learn about notebooks and how to run them locally or on an HPC cluster.
- Intro to Jupyter Notebooks (Video)
- Running jupyter on a HPC cluster
- More on parallelization with ipyparallel
These notebooks show example usage of the ipyrad API.
- Pedicularis API (run in jupyter-notebook)
- Finch API (run in jupyter-notebook)
These notebook show how to do parallelized downstream analyses in Jupyter-notebooks, and to generate advanced input files for many programs using the ipyrad analysis tools.
- TETRAD quartet species tree inference
- RAxML concatenation tree inference
- BPP species tree and delimitation
- BUCKY concordance tree inference
- STRUCTURE population structure
- STRUCTURE with pop assignments
- ABBA BABA admixture
- TREEMIX admixture graph inference
These pages discuss further information about some command-line analysis tools that are frequently used with RAD-seq data.
- You can contribute here. Let us know.