Multiplexed Shotgun Genotyping (MSG). https://github.com/JaneliaSciComp/msg
MSG is a pipeline of scripts to assign ancestry to genomic segments using next-gen sequence data. This method can identify recombination breakpoints in a large number of individuals simultaneously at a resolution sufficient for most mapping purposes, such as quantitative trait locus (QTL) mapping and mapping of induced mutations.  The original paper (Andolfatto et al. 2011) used a reduced representation MseI libraries, but these days, we just use Tn5-tagmentation.

Andolfatto P, Davison D, Erezyilmaz D, Hu TT, Mast J, Sunayama-Morita T, Stern DL. 2011. Multiplexed Shotgun Genotyping for Rapid and Efficient Genetic Mapping. Genome Research, 21: 610-7.

simMSG. https://github.com/melop/simMSG
simMSG is computational experimental design tool for MSG experiments written by former PhD student Molly Schumer (now professor at Stanford U). MSG can be a powerful tool for low cost genotyping of artificial and natural hybrids but accuracy depends on the density of ancestry informative sites between the parental genomes, the number of generations of recombination, polymorphism levels, as well as features of the experimental design such as coverage. simMSG allows users to predict MSG’s accuracy in their system and determine the amount any type of sequencing effort required.

Schumer M, Cui R, Rosenthal GG, Andolfatto P. 2015. simMSG: an experimental design tool for high-throughput genotyping of hybrids. Mol Ecol Resour. 16:183-92

YourePrettyGood. https://github.com/YourePrettyGood
YourePrettyGood is a genotyping pipeline written by graduate student Patrick Reilly (now a Research Associate with Prof. Serena Tucci at Yale). We’ll post a description of this on bioRXiv soon.

Spatial Clustering of Protein Divergence https://github.com/andrewtaverner/clustering
This site hosts a collection of scripts written by graduate student Andrew Taverner, used to quantify lineage specific spatial clustering of amino acid substitutions using protein coding sequence multi-alignments. The associated paper is posted on bioRXiv here.

A. M. Taverner, P. Andolfatto, L. J. Blaine, 2020. Epistasis and physico-chemical constraints contribute to spatial clustering of amino acid substitutions in protein evolution. bioRxiv.

Raw RNA-seq data from Milkweed-feeding insects (and outgroups): http://genomics-pubs.princeton.edu/insect_genomics/data.shtml (Zhen et al. 2012).