omibio 0.1.4.post2

A lightweight and easy-to-use Python bioinformatics toolkit.

omiBio

- A Lightweight Bioinformatics Toolkit

Introduction

omiBio is a lightweight, user-friendly Python toolkit for bioinformatics — ideal for education, research, and rapid prototyping.

Key features:

• Robust data structures: Sequence, Polypeptide, etc., with optional validation.
• Simple I/O: Read/write bioinformatics files (e.g., FASTA) with one-liners.
• Analysis tools: GC content, ORF detection, consensus sequences, sliding windows, and more.
• CLI included: Run common tasks from the terminal .
• Basic visualization: Built-in plotting (via matplotlib & seaborn) for quick insights.
• Functional & OOP APIs: Use classes or convenient wrapper functions.

Modules Overview

The omiBio toolkit is organized into the following modules:

Module	Purpose	Key Classes / Functions
omibio.sequence	Sequence-type data structures	Sequence, Polypeptide
omibio.bio	Biological objects and data containers	SeqInterval, AnalysisResult
omibio.io	File I/O for common bioinformatics formats	read_fasta(), read_fastq()
omibio.analysis	Sequence analysis functions	gc(), sliding_gc(), find_orfs()
omibio.utils	General-purpose utility functions	truncate_repr()
omibio.viz	Simple and easy-to-use data visualization	plot_orf(), plot_sliding_gc()
omibio.cli	Command-line interfaces for common workflows	omibio random-fasta, omibio clean

Release Notes - omiBio [v0.1.4] 12/14/25

Performance & Core I/O

• Optimized FASTA parsing
  Introduced the generator-based read_fasta_iter() to improve performance, refine error handling, and add a configurable warning system.
  The existing read_fasta() API remains unchanged for external use and continues to return SeqCollections, allowing users to choose between eager and lazy parsing.
  Both read_fasta() and read_fasta_iter() now accept TextIO and PathLike objects as data sources.

• FASTQ support
  Added read_fastq() and write_fastq() with the same design philosophy as the FASTA APIs.
  A generator interface, read_fastq_iter(), is also provided.
  All FASTQ I/O functions support TextIO and PathLike inputs.

• Flexible file writing
  All sequence writing functions can now return a list of formatted strings when no output file is specified.

CLI Improvements

• Refactored and streamlined the CLI structure.
• Improved existing commands and added new ones, including:
  • omibio fasta view
  • omibio fastq to-fasta
  • omibio kmer count
• All CLI commands support stdin/stdout and can be composed in Unix-style pipelines.

API & Data Model Changes

• Removed the Gene and Genome classes, which overlapped in functionality with SeqEntry and SeqCollections.
• Made the Sequence and Polypeptide classes immutable.
• Added the at_content() method to the Sequence class.
• Applied __slots__ to SeqInterval and SeqEntry to reduce memory overhead.

Analysis & Visualization

• Enhanced plot_kmer() to support k-mer heatmaps across multiple sequences.
• Refactored AnalysisResult into an abstract base class.
• Added concrete result types:
  • IntervalResult
  • KmerResult
• Results returned by analysis functions (e.g. kmer()) can now be visualized directly via a unified .plot() interface.

Quality Assurance

• Numerous minor fixes and internal refinements.
• Comprehensive test coverage (≥ 95%).

Usage example

Creating a sliding window GC chart using omiBio:

# Load sequences from FASTA (returns dict[str, Sequence])
seqs: SeqCollections[SeqEntry] = read_fasta("examples/example.fasta")
dna: Sequence = seqs["example"]

# Compute GC content in sliding windows (window=200 bp, step=20 bp)
result: IntervalResult[SeqInterval] = sliding_gc(dna, window=200, step=20)

# Visualize easily
result.plot(show=True)  # or: plot_sliding_gc(result, show=True)

Or even a one-liner:

sliding_gc(read_fasta("examples/example.fasta")["example"]).plot(show=True)

The above code will produce results like this:

Using omiBio's Command-line interfaces:

$ omibio orf find example.fasta --min-length 100

The above CLI will produce results like this:

seq_id       start   end     strand  frame   length
example_2    70      289     -       -2      219
example_16   53      257     +       +3      204
example_13   118     301     +       +2      183
example_4    92      272     -       -1      180
example_2    157     322     +       +2      165
example_5    17      173     -       -1      156
example_16   176     332     -       -1      156
...

Installation / 安装

From PyPI:

$ pip install omibio

Requirements

• Python: >= 3.12
• Core dependencies:
  • click (for CLI)
  • numpy & pandas → analysis/plotting dependencies
  • matplotlib & seaborn → enables visualization

For complete project build and dependency configuration, please refer to pyproject.toml

Code Style

omiBio follows PEP 8 conventions for Python code.
All code is automatically formatted and checked using flake8.

License

This project is licensed under the MIT License.

Things to note

• Most of the code in this project uses 0-based indexes, half-open interval, rather than the 1-based indexes commonly used in biology.
• All code type hints in this project use PEP 585 generic syntax in Python 3.9+.
• This project is still under development and not yet ready for production. Please use it with caution. If you have any suggestions, please contact us:
  • gmail: [e-mail]
  • qq: 2658592119