ePath: an online database towards comprehensive essential gene annotation for prokaryotes

Area of Science:

• Microbiology
• Bioinformatics
• Computational Biology

Background:

• Experimental identification of essential genes (EGs) in prokaryotes is costly and time-consuming.
• Existing in silico methods for EG prediction often require substantial computational resources and lack biological interpretability.

Purpose of the Study:

• To develop a new computational algorithm for predicting essential genes in prokaryotes.
• To create an accessible online database (ePath) for rapid retrieval of EG prediction results for over 4,000 prokaryotes.
• To link gene essentiality to biological functions using KEGG Ortholog (KO) annotations.

Main Methods:

• Developed two novel scoring systems, E_score and P_score, for evaluating gene essentiality.
• E_score assesses KO appearance and essentiality in existing experimental data.
• P_score evaluates essentiality based on roles in genetic information processing, cell envelope maintenance, or energy production.

Main Results:

• The ePath database provides EG predictions for over 4,000 prokaryotes.
• The new prediction algorithm achieved accuracy rates between 75% and 91% when validated against five experimental studies.
• Gene essentiality is effectively linked to biological functions via KO annotations.

Conclusions:

• ePath offers a reliable and comprehensive resource for gene essentiality annotation.
• The developed algorithm and database facilitate the study of prokaryotes lacking experimental essential gene data.
• This tool supports broader research in prokaryotic biology and genetics.