This repository is the official implementation of the Analyzing-hCov-genome-sequence pipeline.

If you use any part of this repository, we shall be very grateful if you cite our paper Analyzing hCov genome sequences: Applying Machine Intelligence and beyond

Usage

Installation

1. Please install Tensorflow version: 2.2.0. (Other 2.x versions should work, but have not been tested. Use gpu for better performance)
2. Please install Keras version: 2.3.1.
3. Other python libraries used for this project can be installed by running the following command

pip install -r requirements.txt

Input Preprocessing and Labeling

1. Keep the input sequence fasta file (file used in this analysis can be downloaded from here ) and the info file (sample file present in the input directory) in the Input directory
2. The input_processing.py script handles the input processing and labelling task. It requires 3 mandatory parameters and 1 optional parameters. They are:
   • input
   • info_file
   • label
   • old
3. There are four options for labelling:
   • Death
   • CFR_Recovery
   • CFR_confirmed_cases
   • CFR_Infrastructure
4. Set old parameter to 1 to use the old Training/Testing Accession ID's for input preprocessing and labelling. Don't use this parameter for generating new Training/Testing set. The models are pre-trained with Death Labelling.
5. Sample command:

python input_processing.py --input <input_fasta_file_name> --info_file <info_file_name> --label <label_option> --old 1(optional)

Country-wise Representative Sequence Identification and Phylogenetic Analysis

1. Navigate to the <Clustering_and_Phylogenetic_Analysis> directory.

2. The controller.py script is a one-stop service for all of related analysis. It requires 2 parameters:

   • label (Same as the Input Folder Options)
   • method

3. There are 4 options for method:

   Method	Description
   Euclidean	Simple Euclidean distance-based method among the 3-mers of the genome sequence
   Novel_Fast_Vector	18-dimensional Novel Fast Vector Sequence Comparison Analysis
   Accumulated_Fast_Vector	18-dimenasional Accumulated Fast Vector Sequence Comparison Analysis
   MAW	Minimum Absent Word Analysis

4. Only MAW requires additional 4 parameters:

   • Minimum_MAW_Length
   • Maximum_MAW_Length
   • Distance_Method
   • Fasta File Name (Must be kept in the Input Directory)

5. Sample Command:

python controller.py --label <label_option> --method <method_option>