Fast Genome-Wide Functional Annotation through Orthology Assignment by eggNOG-Mapper

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Orthology assignment is key for functional inference but computationally intensive.
• Current methods often rely on less precise homology-based transfer for (meta-)genome annotation.
• Existing pipelines can be inaccessible, limiting timely functional predictions.

Purpose of the Study:

• To develop eggNOG-mapper, a tool for rapid functional annotation of large sequence sets.
• To leverage precomputed orthologous groups and phylogenies from the eggNOG database for efficient annotation.
• To validate eggNOG-mapper's performance against established methods like BLAST and InterProScan.

Main Methods:

• Developed eggNOG-mapper for fast orthology assignments.
• Benchmarked Gene Ontology (GO) predictions against BLAST and InterProScan.
• Evaluated performance on both whole-genome and metagenomic datasets.
• Assessed speed and prediction accuracy, including experimentally validated terms.

Main Results:

• Orthology filtering with BLAST reduced false positives by 11% and improved validated term recovery by 15%.
• eggNOG-mapper showed comparable proteome coverage and precision to InterProScan, predicting more terms per protein (+41 on average).
• eggNOG-mapper increased validated term recovery by 35% over InterProScan and outperformed it in metagenomic annotation.
• The tool runs significantly faster: ~15x faster than BLAST and 2.5x faster than InterProScan.

Conclusions:

• eggNOG-mapper provides a fast and accurate solution for large-scale functional annotation.
• It improves the precision and recall of functional predictions, especially for experimentally validated terms.
• The tool enhances (meta-)genome annotation efficiency and accessibility, outperforming existing methods.