BCAWT 1.0.2

Manages a complete workflow to analysis the codon usage bias

BCAW: Automated tool for codon usage bias analysis for molecular evolution

Statement of Need

There are no tools available enable users to run a whole automated workflow for codon usage bias analysis. Using python 3.7 BCAW Tool ( Bio Codon Analysis Workflow Tool ) was developed to address this problem. BCAW Tool manages a complete automated workflow to analyze the codon usage bias for genes and genomes of any organism. With minimum coding skills.

For more details about codon usage bias , and the equations used in BCAWT see.

Dependencies

1- Biopython

2- pandas

3- CAI

4- scipy

5- matplotlib

6- numpy

7- prince

Installation Instructions

Using pip

pip install BCAWT

Note: Python >=3.7 is required.

Contribution Guidelines

Contributions to the software are welcome

For bugs and suggestions, the most effective way is by raising an issue on the github issue tracker. Github allows you to classify your issues so that we know if it is a bug report, feature request or feedback to the authors.

If you wish to contribute some changes to the code then you should submit a pull request How to create a Pull Request? documentation on pull requests

Usage

Auto testing

Note here we try to test the result of BCAW tool and not the modules, for testing the modules in the package use test.py

First download fasta file containing the coding sequence ( you can download any fasta file containing gene sequences to be analyzed from NCBI database).

or just download that file Test file

then run ( It will automatically run a test on the results files ):

from BCAWT import BCAWT_auto_test
path = "Test_folder" # or any!!
BCAWT_auto_test.auto_test(path)
#processing....
BCAWT_auto_test.auto_check_files(path)

Main Usage

from BCAWT import BCAWT
BCAWT.BCAW(['Ecoli.fasta'],'save_path',genetic_code_=11,Auto=True)

Input

main_fasta_file (list): list of string of the file's path or file-like object

save_path (str): absolute path to the directory to save the result in, default = the current directory

ref_fasta_file (list): list of string of the file's path or file-like object, default = None

Auto (bool): default = False, if ref_fasta_file not None.

genetic_code_ (int) : default = 1, The Genetic Codes number described by [NCBI](https://www.ncbi.nlm.nih.gov/Taxonomy/Utils/wprintgc.cgi)

Important Note: BCAW tool expect coding sequences as input and not genes, for more information about what the difference between them you can take a look here

To obtain such fasta file for a species of interest

Say that the species of interest is Escherichia coli str. K-12 substr. MG1655:

1- Go to the NCBI database.

2- In the search bar write ( Escherichia coli str. K-12 substr. MG1655, complete genome ).

3- choose one of the results ( depending on what you want in your analysis ).

3- On the right of the page, you will find send to option. From sent to select Coding Sequences then FASTA nucleotides Finally, press on Create File

For NCBI Genomes Download (FTP) FAQ

Output

The expected CSV files output

CSV file name	Description
ATCG	contains ; gene id, GC, GC1, GC2, GC3, GC12, AT, AT3 A3, T3, C3, G3, GRAVY, AROMO and, Gene Length
CA_RSCU	contains ; each RSCU result for each codon in each genes
CA_RSCUcodons	contains ; correspondence analysis first 4 axis for each codon
CA_RSCUgenes	contains ; correspondence analysis first 4 axis for each gene
CAI	contains ; gene id and CAI index
ENc	contains ; gene id and ENc index.
P2-index	contains ; gene id and P2 index
optimal codons	contains; putative optimal codons detected

All output plots from BCAW tool analysis for coding sequence from Escherichia coli

Documentations

1. An intro to the codon usage bias >> CUB introduction
2. For more information about the equations used to analyze CUB in the BCAW tool >> Equations
3. For more information about the output >> Output
4. For more information about the abbreviations used >> Abbreviations table