Custom bioinformatics modules

Welcome!

Welcome to my very first bioinformatics repository!

This repository contains the projects and homework assignments I completed during the one-year bioinformatics program at the Institute of Bioinformatics in Saint Petersburg, Russia. It includes a variety of custom bioinformatics modules designed to develop skills in object-oriented programming (OOP) and API usage.

I hope you find these resources helpful and inspiring!

Repo Structure

This repository is organized as follows to help you navigate and utilize the contents effectively:

• Showcases.ipynb: A Jupyter notebook that demonstrates how to use the modules listed below with practical examples.
• custom_tools_main.py: This script includes tools for interfacing with GenScan and Telegram through their respective APIs.
• bio_files_processor.py: Contains functions, classes, and a context manager designed for handling files in FASTA format.
• custom_random_forest.py: Features a custom implementation of the RandomForestClassifier class from scikit-learn, with capabilities for parallel processing.
• test_custom_tools.py: Contains eight simple tests to validate the functionality of the repository's modules.
• data/: Includes a folder with example data files needed for the examples demonstrated in the Jupyter notebook.

main.py

The main.py module serves as the core of this repository. Below is an overview of its key functions:

• filter_fastq: This function filters FASTQ files based on parameters such as GC content, sequence length, and quality threshold.

• run_genscan: Performs GENSCAN analysis using either an input sequence or a sequence file.

  • class GenscanOutput: A data class designed to store the output of GENSCAN analysis, including predicted peptides, introns, and exons.
  • def extract_exons: Extracts exon data from HTML blocks returned by GENSCAN.
  • def calculate_introns: Calculates intron positions based on the provided exon data.
  • def extract_peptides: Extracts predicted peptide sequences from HTML text blocks returned by GENSCAN.

• telegram_logger: A decorator to log function runs and send them to a Telegram chat.

  • def send_telegram_message: Sends messages or documents to a Telegram chat using a bot.
  • def make_post: Constructs and sends Telegram messages indicating the success or failure of function runs.

bio_files_processor.py

This module provides functionalities for handling FASTA and Genbank (GBK) files, including reading, writing, and converting them. Below is an overview of its key functions:

• read_fasta_file: Reads a FASTA file and saves it to a dictionary.

• write_fasta_file: Writes a dictionary containing FASTA data into a FASTA file.

• convert_multiline_fasta_to_oneline: Converts a multiline FASTA file into a one-line FASTA file.

• select_genes_from_gbk_to_list: Selects gene names and their translations from a Genbank (GBK) file and returns them as a list.

• FastaRecord: A data class representing a FASTA record containing sequence information. Provides a short string representation of the FastaRecord.

• OpenFasta: A context manager for reading FASTA files. Supports iteration over FASTA records and reading individual records or all records from the file.

custom_random_forest.py

This module introduces a custom implementation of a RandomForestClassifier, capable of parallel processing for improved performance.

Enjoy! ✨✨