orsum: a Python package for filtering and comparing enrichment analyses using a simple principle.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Gene enrichment analyses are crucial for interpreting gene lists.
• Current methods often produce long, redundant annotation terms, hindering interpretation and comparison.
• Existing solutions like down-sized collections or complex filtering tools have limitations.

Purpose of the Study:

• To introduce orsum, a novel Python package for filtering gene enrichment results.
• To address the challenges of redundancy and complexity in enrichment analysis outputs.
• To provide a user-friendly method for refining and comparing enrichment findings.

Main Methods:

• Developed orsum, a Python package implementing a novel filtering principle.
• The core principle: discard a term if a more significant term annotates the same genes.
• Representative terms are selected from the original enrichment results, ensuring study-specific relevance.

Main Results:

• orsum effectively filters multiple enrichment results simultaneously.
• The package highlights common and specific annotation terms across different analyses.
• Application to neurodegenerative disease gene lists demonstrated its utility in reducing redundancy while preserving biological information.

Conclusions:

• orsum offers a comprehensible and effective solution for filtering and comparing gene enrichment results.
• The package simplifies the interpretation of complex biological data.
• It is readily available for use in bioinformatics and computational biology research.