The precision, sensitivity and specificity in relation to the cutoff rank.

<p>The vertical dashed line represents the default cutoff of 0.15.</p

The distribution of predicted essential gene numbers.

<p>The distribution of predicted essential gene numbers.</p

Geptop: A Gene Essentiality Prediction Tool for Sequenced Bacterial Genomes Based on Orthology and Phylogeny

<div><p>Integrative genomics predictors, which score highly in predicting bacterial essential genes, would be unfeasible in most species because the data sources are limited. We developed a universal approach and tool designated Geptop, based on orthology and phylogeny, to offer gene essentiality annotations. In a series of tests, our Geptop method yielded higher area under curve (AUC) scores in the receiver operating curves than the integrative approaches. In the ten-fold cross-validations among randomly upset samples, Geptop yielded an AUC of 0.918, and in the cross-organism predictions for 19 organisms Geptop yielded AUC scores between 0.569 and 0.959. A test applied to the very recently determined essential gene dataset from the <i>Porphyromonas gingivalis</i>, which belongs to a phylum different with all of the above 19 bacterial genomes, gave an AUC of 0.77. Therefore, Geptop can be applied to any bacterial species whose genome has been sequenced. Compared with the essential genes uniquely identified by the lethal screening, the essential genes predicted only by Gepop are associated with more protein-protein interactions, especially in the three bacteria with lower AUC scores (<0.7). This may further illustrate the reliability and feasibility of our method in some sense. The web server and standalone version of Geptop are available at <a href="http://cefg.uestc.edu.cn/geptop/" target="_blank">http://cefg.uestc.edu.cn/geptop/</a> free of charge. The tool has been run on 968 bacterial genomes and the results are accessible at the website.</p></div

Training workflow based on 18 groups of essential genes and using <i>Ecol</i> as test.

<p>Training workflow based on 18 groups of essential genes and using <i>Ecol</i> as test.</p

AUC scores from cross-organism tests using the integrative compositional information predictor.

<p>The range of AUC is from 0.5 to 1.</p

AUC scores from cross-organism Geptop prediction.

<p>The range of AUC is from 0.5 to 1.</p

AUC scores of jackknife test.

<p>We randomly picked 10 times of 3, 6, 9, 12, 15, 17 genomes from the remaining 18 genomes and computed the AUC scores of predicting <i>Ecol</i>. Error bars are representing 90% confidence intervals on the estimates of the means.</p

The connectivity distributions of four classes of genes in three bacteria with AUC lower than 0.7.

<p>(a) <i>H. influenza</i>; (b) <i>H. pylori</i>; (c) <i>V. cholera</i>.</p

Cross-organism test accuracies of the Geptop.

<p>Cross-organism test accuracies of the Geptop.</p

Comparison of features between the experimental and the Geptop groups.

a<p>“ = ” denotes significance of the Mann-Whitney test was within 5%; “>>” denotes that the experimental group was greater than the Geptop group using the Mann-Whitney test at a 5% level; “<<” denotes converse case.</p>b<p>“ = ” denotes that the difference between two groups was within 5%; “>>” denotes that the difference at a significance level of 5% for the experimental group was greater than that for the Geptop group; “<<” denotes converse case.</p