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Methods

Predicting essential genes in fungal genomes

¹Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut 06520, USA; ²Department of Computer Science, Royal Holloway University of London, Egham, TW20 0EX, United Kingdom; ³Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, Connecticut 06520, USA; ⁴Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut 06520, USA; ⁵Department of Computer Science, Yale University, New Haven, Connecticut 06520, USA

Essential genes are required for an organism's viability, and the ability to identify these genes in pathogens is crucial to directed drug development. Predicting essential genes through computational methods is appealing because it circumvents expensive and difficult experimental screens. Most such prediction is based on homology mapping to experimentally verified essential genes in model organisms. We present here a different approach, one that relies exclusively on sequence features of a gene to estimate essentiality and offers a promising way to identify essential genes in unstudied or uncultured organisms. We identified 14 characteristic sequence features potentially associated with essentiality, such as localization signals, codon adaptation, GC content, and overall hydrophobicity. Using the well-characterized baker's yeast Saccharomyces cerevisiae, we employed a simple Bayesian framework to measure the correlation of each of these features with essentiality. We then employed the 14 features to learn the parameters of a machine learning classifier capable of predicting essential genes. We trained our classifier on known essential genes in S. cerevisiae and applied it to the closely related and relatively unstudied yeast Saccharomyces mikatae. We assessed predictive success in two ways: First, we compared all of our predictions with those generated by homology mapping between these two species. Second, we verified a subset of our predictions with eight in vivo knockouts in S. mikatae, and we present here the first experimentally confirmed essential genes in this species.

⁷ Corresponding author.

E-mail mark.gerstein{at}yale.edu; fax (360) 838-7861.

[Supplemental material is available online at www.genome.org. and http://www.gersteinlab.org/proj/predess/.]

Article published online before print. Article and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.5144106. Freely available online through the Genome Research Open Access option.

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