KEGG Orthology Based Annotation System (KOBAS) is a Python application for bioinformatics. KOBAS assigns KEGG Orthology (KO) terms to query sequences via BLAST similarity search, and discovers enriched pathways among the annotation results by frequency analysis or statistical significance testing.

# Using pixi (recommended)
pixi install
pixi run test

# Or using pip
pip install -e .[dev]

Requires Python >= 3.10. See docs/install.txt for detailed instructions.

KOBAS provides a unified kobas command with subcommands for each task.

Annotate a protein FASTA file with KO terms:

kobas annotate input.fasta

For nucleotide sequences:

kobas annotate -p blastx input.fasta

Find the most frequent pathways among annotations:

kobas pathfind annotation.txt

Find statistically enriched pathways against a background organism (KEGG three-letter abbreviation) or file:

kobas pathfind -k h annotation.txt hsa
kobas pathfind -k b annotation.txt background.txt

Statistical methods:

Filter or translate annotation files:

kobas filter-annot [-e EVALUE] [-r RANK] [-s SIEVE] annot_file

Filter or translate FASTA files:

kobas filter-fasta [-s SIEVE] [-t TRANSLATE] fasta_file

Initialize KOBAS data:

kobas init -s /path/to/kobas-data

Run <subcommand> -h for full options.

• Authors: Xizeng Mao, Jianmin Wu, Tao Cai, Chen Xie, Liping Wei
• Email: kobas@mail.cbi.pku.edu.cn
• Web: http://kobas.cbi.pku.edu.cn