09.2020
Copyright (C) 2019 Olivier Rivoire, Rama Ranganathan, and Kimberly Reynolds
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 Jupyter notebooks; for details please see: https://jupyter.org/.
For installation instructions, and an introduction to using the toolbox, please refer to the website:
https://ranganathanlab.gitlab.io/pySCA
or look through the RST files included with the pySCA distribution.
| bin/ | Executables for running SCA analysis functions |
| data/ | Input data (including those needed for the tutorials) |
| docs/ | HTML documentation (generated by Sphinx) |
| figs/ | Figures used for the notebooks and documentation |
| notebooks/ | Example SCA notebooks |
| output/ | Output files (empty at install, use runAllNBCalcs.sh) |
| pysca/ | Python code for SCA |
| scripts/ | Utility scripts used to generate example data |
| annotateMSA | Annotates alignments with phylogenetic/taxonomic information |
| scaProcessMSA | Conducts some initial processing of the sequence alignment |
| scaCore | Runs the core SCA calculations |
| scaSectorID | Defines sectors given the results of the calculations in scaCore |
| scaTools.py | The SCA toolbox - functions for the SCA calculations |
| settings.py | Global configuration settings for the analysis |
| SCA_DHFR.ipynb | Example for DHFR |
| SCA_G.ipynb | Example for the small G proteins |
| SCA_betalactamase.ipynb | Example for the beta-lactamases |
| SCA_S1A.ipynb | Example for the S1A serine protease |
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