Abstract
BackgroundIdentification of essential genes is not only useful for our understanding of the minimal gene set required for cellular life but also aids the identification of novel drug targets in pathogens. In this work, we present a simple and effective gene essentiality prediction method using information-theoretic features that are derived exclusively from the gene sequences.ResultsWe developed a Random Forest classifier and performed an extensive model performance evaluation among and within 15 selected bacteria. In intra-organism predictions, where training and testing sets are taken from the same organism, AUC (Area Under the Curve) scores ranging from 0.73 to 0.90, 0.84 on average, were obtained. Cross-organism predictions using 5-fold cross-validation, pairwise, leave-one-species-out, leave-one-taxon-out, and cross-taxon yielded average AUC scores of 0.88, 0.75, 0.80, 0.82, and 0.78, respectively. To further show the applicability of our method in other domains of life, we predicted the essential genes of the yeast Schizosaccharomyces pombe and obtained a similar accuracy (AUC 0.84).ConclusionsThe proposed method enables a simple and reliable identification of essential genes without searching in databases for orthologs and demanding further experimental data such as network topology and gene-expression.
Highlights
Identification of essential genes is useful for our understanding of the minimal gene set required for cellular life and aids the identification of novel drug targets in pathogens
DEG collects the list of essential genes in both eukaryotes and prokaryotes, which were identified by various gene knock-out experimental procedures such as transposon mutagenessis and RNA interference [13]
The features used in this study are: 4 entropy (E), 17 mutual information (MI), 65 conditional mutual information (CMI), 3 Kullback-Leibler divergence (KLD), and 2 Markov model (M) related
Summary
Identification of essential genes is useful for our understanding of the minimal gene set required for cellular life and aids the identification of novel drug targets in pathogens. The subset of genes which are necessary for the viability and reproduction of an organism are called essential genes. Detection of these genes is very crucial for understanding the minimal requirements for maintaining life [1, 2]. Studies on essential genes are very important in synthetic biology for re-engineering microorganisms and creating cells with a minimal genome [5].
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