/pySCA

A python implementation of the Statistical Coupling Analysis (SCA)

Primary LanguageJupyter NotebookBSD 3-Clause "New" or "Revised" LicenseBSD-3-Clause

pySCA

Version 03.2015

Original Author: Reynolds Copyright (C) 2015 Olivier Rivoire, Rama Ranganathan, Kimberly

This repository is a Fork of Reynoldsk, if you wish to view the original repository click here.

This program is free software distributed under the BSD 3-clause license, please see the file LICENSE for details.

The current version of the Statistical Coupling Analysis (SCA) analysis is implemented in python. This directory contains the necessary code for running the SCA calculations, as well examples/tutorials for the dihydrofolate reductase (DHFR) enzyme family, the S1A serine proteases, the small G-protein family and the Beta-lactamase enzyme family. The tutorials are distributed as iPython notebooks.

For installation instructions, I recomend a virtual enviroment in Python3

Make a Virtualenv

Content and Scripts

  • Inputs/: Directory containing input files (including those needed for the tutorials)
  • Outputs/: Directory for output files (empty at install)
  • html_docs/: Directory containing html documentation
  • annotate_MSA.py: Python script that annotates alignments with phylogenetic/taxonomic information
  • scaProcessMSA.py: Python script that conducts some initial processing of the sequence alignment
  • scaCore.py: Python script that runs the core SCA calculations
  • scaSectorID.py: Python script that defines sectors given the results of the calculations in scaCore
  • scaTools.py: The SCA toolbox - contains all functions needed for the SCA calculations
  • SCA_DHFR.ipynb: Python notebook example for DHFR
  • SCA_G.ipynb: Python notebook example for the small G proteins
  • SCA_betalactamase.ipynb: Python notebook example for the beta-lactamases
  • SCA_S1A.ipynb: Python notebook example for the S1A serine protease