NetGenes: A Database of Essential Genes Predicted Using Features From Interaction Networks

Essential gene prediction models built so far are heavily reliant on sequence-based features, and the scope of network-based features has been narrow. Previous work from our group demonstrated the importance of using network-based features for predicting essential genes with high accuracy. Here, we apply our approach for the prediction of essential genes to organisms from the STRING database and host the results in a standalone website. Our database, NetGenes, contains essential gene predictions for 2,700+ bacteria predicted using features derived from STRING protein-protein functional association networks. Housing a total of over 2.1 million genes, NetGenes offers various features like essentiality scores, annotations, and feature vectors for each gene. NetGenes database is available from https://rbc-dsai-iitm.github.io/NetGenes/.

Automated summary

Essential gene prediction models traditionally rely on sequence-based features, but this study showed the importance of network-based features for accurate predictions. The NetGenes database, containing essential gene predictions for over 2,700 bacteria, offers various features for each gene, including essentiality scores, annotations, and feature vectors.