Topological REcombination Estimator

Authors: Devon P. Humphreys, Melissa R. McGuirl, Michael Miyagi, Andrew J. Blumberg

For questions/comments please contact Melissa R. McGuirl at [email protected].

This software takes as input either (1) a collection of genomes in FASTA format or (2) a distance matrix and predicts the underlying recombination rate from topological summary statistics of the data. The supported distance matrix formats are those formats that are currently supported by Ripser:

• comma-separated values lower triangular distance matrix (preferred)
• comma-separated values upper triangular distance matrix (MATLAB output from the function pdist)
• comma-separated values full distance matrix

The user has the option getting recombination rate estimates over a sliding window analysis and can specify a normalization factor (default = 1/1000).

This software is based upon the work presented in Humphreys, D.P., McGuirl, M.R., Miyagi, M., and Blumberg, A.J. Fast Estimation of Recombination Rates Using Topological Data Analysis (2018). Preprint: https://www.biorxiv.org/content/early/2018/08/20/395210

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

• Ripser.py v0.3.2 (https://ripser.scikit-tda.org/index.html),
• Python

• ripser
• matplotlib
• numpy

	pip install Cython
	pip install Ripser

      cd 
      git clone https://github.com/MelissaMcguirl/TREE
      cd TREE
      pip install -r requirements.txt

      python TREE.py -i INPUT_FILE [-t INPUT_TYPE] [-s SLIDING_WINDOW_FLAG] [-w WINDOW_SIZE] [-o OUTPUT_DIRECTORY] [-N NORMALIZATION_FACTOR] [-n FILENAME_IDENTIFIER] [-b BASE_FLAG] [-f OFFSET_VALUE] [-g PLOT_NAME]

Note, the default input type is a FASTA file and the default normalization factor is 1/1000.

      cd src
      1) python TREE.py -i ../examples/seq_example.fasta (input = FASTA file)   
      2) python TREE.py -i ../examples/hamming_example -t DIST (input = distance matrix)
      3) python TREE.py -i ../examples/seq_example.fasta -s -w 20 -o ../examples/outputs -n test -g ../examples/outputs/outputPlt (sliding window analysis over SNPs)
      4) python TREE.py -i ../examples/seq_example.fasta -s -b -w 20 -f 10 -o ../examples/outputs -n test -g ../examples/outputs/outputPlt (sliding window analysis over raw bases)

  Sample expected output plot and text file of predictions for the sliding window analysis is provided in examples/outputs/

  0) Compute Hamming distance matrix
  1) Feed Hamming distance matrix into Ripser to compute dimension 0 and dimension 1 persistent homology barcodes
  2) Extract topological summary statistics (psi, b1, phi)
  3) Predict recombination rate using TREE
  4) Plot results and save predictions to text file (for sliding windows)

  python TREE.py -h

This software has been tested with python 2.7 and 3.6.1.