The availability of whole-genome sequences and associated multi-omics data sets, combined with advances in gene knockout and knockdown methods, has enabled large-scale annotation and exploration of gene and protein functions in eukaryotes. Knowing which genes are essential for the survival of eukaryotic organisms is paramount for an understanding of the basic mechanisms of life, and could assist in identifying intervention targets in eukaryotic pathogens and cancer. Here, we studied essential gene orthologs among selected species of eukaryotes, and then employed a systematic machine-learning approach, using protein sequence-derived features and selection procedures, to investigate essential gene predictions within and among species. We showed that the numbers of essential gene orthologs comprise small fractions when compared with the total number of orthologs among the eukaryotic species studied. In addition, we demonstrated that machine-learning models trained with subsets of essentiality-related data performed better than random guessing of gene essentiality for a particular species. Consistent with our gene ortholog analysis, the predictions of essential genes among multiple (including distantly-related) species is possible, yet challenging, suggesting that most essential genes are unique to a species. The present work provides a foundation for the expansion of genome-wide essentiality investigations in eukaryotes using machine learning approaches.
Keywords: CRISPR, Clustered regularly interspaced short palindromic repeats; Essential genes; Essentiality prediction; Eukaryotes; GBM, Gradient boosting method; GI, Genetic interaction; GLM, Generalised linear model; GO, Gene ontology; ML, Machine-learning; Machine-learning; NN, Artificial neural network; OGEE, Online GEne essentiality database; PPI, Protein-protein interaction; PR-AUC, Area under the precision-recall curve; RF, Random Forest; RNAi, RNA interference; ROC-AUC, Area under the receiver operating characteristic curve; SPLS, Sparse partial least squares; SVM, Support-Vector machine.