How to learn about gene function: text-mining or ontologies?

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• The rapid increase in genomic data necessitates efficient, automated gene function annotation methods.
• High-throughput technologies generate gene lists requiring functional interpretation, a persistent challenge.
• Existing tools often rely on enrichment analysis for quick biological insights, but lack systems-level analysis.

Purpose of the Study:

• To survey and overview current computational tools and methods for automated gene list interpretation.
• To analyze functional annotation approaches from an epistemological perspective.
• To identify limitations and propose future directions for gene list annotation.

Main Methods:

• Survey of existing computational tools for gene list annotation.
• Epistemological analysis of annotation methods, categorizing them into ontology-based and literature-inferred approaches.
• Discussion of current challenges and promising future directions.

Main Results:

• Current functional annotation methods are broadly categorized into expert-curated ontology annotations (e.g., Gene Ontology) and literature-inferred annotations (e.g., text-mining).
• Both approaches have limitations in providing detailed, accurate, and systems-level biological insight.
• Deriving comprehensive understanding from gene lists remains a significant challenge.

Conclusions:

• Improved methods are needed for more holistic insight into molecular systems.
• Future annotation tools should facilitate better experimental follow-up and treatment options.
• Enhanced management of gene lists, especially for understudied organisms, is required, potentially through extended biomedical concept dictionaries and annotation benchmarks.