This method has now been published in PNAS!

A null model for microbial diversification
Timothy J. Straub and Olga Zhaxybayeva
PNAS July 3,2017 vol. 114 no. 27 E5414-E5423
10.1073/pnas.1619993114
http://www.pnas.org/content/114/27/E5414.abstract

• Python 2.7, including Biopython and Numpy (https://www.python.org/)
• R (https://www.r-project.org/)
• MCL (https://micans.org/mcl/)
• Mothur (https://mothur.org/)
• MUSCLE (http://drive5.com/muscle/)
• Seq-Gen (http://tree.bio.ed.ac.uk/software/seqgen/)

1. Run the pipeline on empirical data. See pipeline/pipeline.sh for details.
   a. Fast alignment-free similarity comparisons of all vs all protein sequences using afree
   b. Clustering on similarity using MCL to generate gene families
   c. Filtering on gene families to generate near single-copy core
   d. Furthest-neighbor clustering within gene families using Mothur
2. Simulate neutral gene families based on empirical data. See sim/simulations.sh for details.
   a. Simulate phylogenies using a Moran model
   b. Generate DNA sequences based on these phylogenies using Seq-Gen
   c. Clustering with Mothur
3. Comparisons between simulated and empirical data using R. See statistics.R for details.
   a. Load in both empirical and simulated clustering results
   b. Compare empirical gene family clustering patterns to simulated null model
   c. Investigate gene families that are significantly different from null model