ipyrad includes a suite of analysis tools that are designed to make it easy to run inference programs (e.g., STRUCTURE, TREEMIX, 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 ipyrad in Jupyter using its Python API. Then check out the analysis tools notebooks.
Using Jupyter notebooks¶
This is an optional tool to use with ipyrad, but one that we strongly recommend.
ipyrad API full example notebooks¶
These notebooks show example usage of the ipyrad API.
ipyrad API analysis tools¶
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.
command line programs¶
These pages discuss further information about some command-line analysis tools that are frequently used with RAD-seq data.
Other jupyter notebooks (ipyrad in the wild)¶
- You can contribute here. Let us know.