Metadata-Version: 2.1
Name: gtAI
Version: 1.0.2
Summary: To estimate the tRNA adaptation index (tAI)
Home-page: https://github.com/AliYoussef96/gtAI
Author: Ali Mostafa
Author-email: aliali.mostafa99@gmail.com
License: GPLv3
Description: 
        # Genetic tRNA Adaptation index (gtAI)
        
        [![](https://img.shields.io/badge/doi-https%3A%2F%2Fdoi.org%2F10.1016%2Fj.jprot.2019.103613-red)]()
        [![Documentation Status](https://readthedocs.org/projects/gtai/badge/?version=latest)](https://gtai.readthedocs.io/en/latest/?badge=latest)
        
        **gtAI is a new package implemented in python to effectively estimate the tRNA adaptation index (tAI).**
        
        - For more information about the gtAI: Not yet published 
        
        - For more information about the tAI: [Mario dos Reis et. al.,](https://academic.oup.com/nar/article/32/17/5036/1333956).
        
        ## Python Support
        
        Python >=3.7 is required.
        
        ## Dependencies
        
        1. Biopython
        
        2. pandas
        
        3. numpy
        
        4. gaft
        
        ## Installation Instructions
        
        **Using pip**
        
        ```python
        pip install gtAI
        ```
        
        ## 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](https://github.com/AliYoussef96/gtAI/pulls)
        How to create a Pull Request? [documentation on pull requests](https://help.github.com/en/articles/about-pull-requests)
        
        ## Usage
        
        ```python
        from gtAI import Run_gtAI
        df_tai, dict_wi, rel_values = Run_gtAI.gtai_analysis(main_fasta, GtRNA, genetic_code_number, size_pop, generation_number=50, ref_fasta= ref_fasta, bacteria=False)
        ```
        
        Where:
        
        ```
        
        main_fasta (str): A main fasta file containing the genes to be analyzed.
        GtRNA (dict): The tRNA genes count
        ref_fasta (str): Reference genes with the highest gene expression in a genome.
        genetic_code_number (int): default = 1, The Genetic Codes number described by NCBI (https://www.ncbi.nlm.nih.gov/Taxonomy/Utils/wprintgc.cgi)
        size_pop (int): A parameter for the genetic algorithm to identify the population size containing the possible solutions to optimize Sij-values. (default = 60)
        generation_number (int): A parameter for the genetic algorithm to identify the generation number. (default = 100)
        bacteria (bool): True If the tested organism is prokaryotic or archaeans, else equal to False (default = False)
        
        
        ```
        
        **Note: for ref_fasta parameter, the user is able to use a reference set of interest (in fasta format). Otherwise, the package will automatically generate a reference set based on the ENc values of the tested genome. For more information: [API documentation](https://gtai.readthedocs.io/en/latest/?badge=latest).**
        
        
        **Note: Population size must be an even number**
        
        Returns:
        
        ```
        df_tai (dataframe): Contains each gene id and its gtAI value.
        final_dict_wi (dict): Contains each codon and its absolute adaptiveness value.
        rel_values (dict): Contains each codon and its relative adaptiveness values.
        ```
        
        ## Example
        
        1- Import gtAI functions.
        
        ```python
        
        from gtAI import Run_gtAI
        from gtAI import gtAI 
        ```
        
        2- In this example, we will use [Saccharomyces cerevisiae S288C](https://www.ncbi.nlm.nih.gov/genome/browse/#!/eukaryotes/15/Saccharomyces%20cerevisiae%20S288c) coding sequences.
        
        3- Prepare the tRNA gene copy number of the tested genome.
        
        The user has two options;  a) input the tRNA gene copy number as python dictionary or, b) using GtRNAdb() function, the user can get it automatically from the GtRNA database, using the link to the tested genome (In our case Saccharomyces cerevisiae S288C). 
        Or by tRNADB_CE() function to get the tRNA gene copy number from tRNADB_CE database using also the link to the tested genome. 
        
        In this example, the second option (b) will be used.
        
        ```python
        
        url_GtRNAdb = "http://gtrnadb.ucsc.edu/genomes/eukaryota/Scere3/"
        #### From GtRNAdb
        GtRNA = gtAI.GtRNAdb(url_GtRNAdb)
        
        ```
        
        for more infromation about GtRNAdb() as well as tRNADB_CE(); [API documentation](https://gtai.readthedocs.io/en/latest/?badge=latest).
        
        4- Parameter settings for gtai_analysis() function.
        
        ```python
        genetic_code_number = 1
        ref_fasta = ""
        bacteria = False
        size_pop = 60
        generation_number = 100
        ```
        
        for more information about gtai_analysis() and the parameters; [API documentation](https://gtai.readthedocs.io/en/latest/?badge=latest).
        
        5- Run gtAI.
        
        ```python
        df_tai , final_dict_wi, rel_values = Run_gtAI.gtai_analysis(main_fasta,GtRNA,genetic_code_number,bacteria=bacteria, size_pop=size_pop,generation_number=generation_number)
        ```
        
        Returns:
        
        ```python
        df_tai (dataframe): Contains each gene id and its gtAI value 
        final_dict_wi (dict): Contains each codon and its absolute adaptiveness value
        rel_values (dict): Contains each codon and its relative adaptiveness values
        ```
        
        6- To save the gtAI result as a CSV file.
        
        
        ```python
        import pandas as pd
        
        df_tai.to_csv("test.csv", header=True)
        ```
        
        [**Output example**](https://github.com/AliYoussef96/gtAI/blob/master/Saccharomyces%20cerevisiae%20S288c.csv)
        
        ## API Documentation
        
        You can access the API documentation from here: [gtAI Documentation](https://gtai.readthedocs.io/en/latest/?badge=latest)
        
        
        ## Citation
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 3
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3.7.0
Description-Content-Type: text/markdown
