Computational enhancer prediction: evaluation and improvements

Background: Identifying transcriptional enhancers and other cis-regulatory modules (CRMs) is an important goal of post-sequencing genome annotation. Computational approaches provide a useful complement to empirical methods for CRM discovery, but it is critical that we develop effective means to evaluate their performance in terms of estimating their sensitivity and specificity. Results: We introduce here pCRMeval, a pipeline for in silico evaluation of any enhancer prediction tools that are flexible enough to be applied to the Drosophila melanogaster genome. pCRMeval compares the result of predictions with the extensive existing knowledge of experimentally-validated Drosophila CRMs in order to estimate the precision and relative sensitivity of the prediction method. In the case of supervised prediction methods-when training data composed of validated CRMs are used-pCRMeval can also assess the sensitivity of specific training sets. We demonstrate the utility of pCRMeval through evaluation of our SCRMshaw CRM prediction method and training data. By measuring the impact of different parameters on SCRMshaw performance, as assessed by pCRMeval, we develop a more robust version of SCRMshaw, SCRMshaw_HD, that improves the number of predictions while maintaining sensitivity and specificity. Our analysis also demonstrates that SCRMshaw_HD, when applied to increasingly less well-assembled genomes, maintains its strong predictive power with only a minor drop-off in performance. Conclusion: Our pCRMeval pipeline provides a general framework for evaluation that can be applied to any CRM prediction method, particularly a supervised method. While we make use of it here primarily to test and improve a particular method for CRM prediction, SCRMshaw, pCRMeval should provide a valuable platform to the research community not only for evaluating individual methods, but also for comparing between competing methods.