IGCexpansion

IGC paralog divergence development folder. Here, we provide different approach to detect relationship between paralog divergence and IGC rate tau.

Dependent software:

jsonctmctree package (powerful likelihood calculation engine by Alex Griffing, modified by Xiang Ji)

Biopython networkx numpy scipy numdifftools

(you could install them by

pip install --user Biopython networkx numpy scipy numdifftools)

Coding Language

Python 3.6 or higher

Preparation

Mac OS / Linux

1. To install python packages, you need to use pip (package management).

2. You might need to install git.

Windows

1. I recommand using Anaconda on Windows that ships with pip functionality and many more useful features.

2. You still need to install git

Install python packages

1. Install jsonctmctree package by Alex Griffing (slightly modified for version updates):

pip install --user git+https://github.com/xji3/jsonctmctree.git

2. Install paralog_divergence package:

pip install --user git+https://github.com/Tanchumin/Commonancestor.git

3. Similarly install any other python packages (they should have been installed with IGCexpansion already)

pip install --user networkx

pip install --user Biopython

To uninstall:

pip uninstall IGCexpansion

Getting a local copy of the package

git clone https://github.com/Tanchumin/Commonancestor.git

You can now run the tutorial file or edit it to perform analyses.

cd IGCexpansion/tutorials/IS_IGC

python Run_IS_IGC.py

Tutorials

The latest update introduces several new features. The addition includes enhancements to various modules. The [em_pt1] module with class [Embrachtau1] now incorporates the number of paralog divergences, allowing for the investigation of how the IGC rate changes with paralog identity levels. The [joint_ana] module, featuring the [JointAnalysis] class, enables the analysis of multiple genes by utilizing a shared IGC rate and K. The [gls_seq] module with the [GSseq] class, facilitates the simulation of sequences based on assigned parameters.