In Silico Evaluation of Predicted Regulatory Interactions in Arabidopsis thaliana

Abstract

<p>Abstract</p> <p>Background</p> <p>Prediction of transcriptional regulatory mechanisms in <it>Arabidopsis </it>has become increasingly critical with the explosion of genomic data now available for both gene expression and gene sequence composition. We have shown in previous work <abbrgrp><abbr bid="B1">1</abbr></abbrgrp>, that a combination of correlation measurements and <it>cis</it>-regulatory element (CRE) detection methods are effective in predicting targets for candidate transcription factors for specific case studies which were validated. However, to date there has been no quantitative assessment as to which correlation measures or CRE detection methods used alone or in combination are most effective in predicting TF→target relationships on a genome-wide scale.</p> <p>Results</p> <p>We tested several widely used methods, based on correlation (Pearson and Spearman Rank correlation) and <it>cis-</it>regulatory element (CRE) detection (≥1 CRE or CRE over-representation), to determine which of these methods individually or in combination is the most effective by various measures for making regulatory predictions. To predict the regulatory targets of a transcription factor (TF) of interest, we applied these methods to microarray expression data for genes that were regulated over treatment and control conditions in wild type (WT) plants. Because the chosen data sets included identical experimental conditions used on TF over-expressor or T-DNA knockout plants, we were able to test the TF→target predictions made using microarray data from WT plants, with microarray data from mutant/transgenic plants. For each method, or combination of methods, we computed sensitivity, specificity, positive and negative predictive value and the F-measure of balance between sensitivity and positive predictive value (precision). This analysis revealed that the ≥1 CRE and Spearman correlation (used alone or in combination) were the most balanced CRE detection and correlation methods, respectively with regard to their power to accurately predict regulatory-target interactions.</p> <p>Conclusion</p> <p>These findings provide an approach and guidance for researchers interested in predicting transcriptional regulatory mechanisms using microarray data that they generate (or microarray data that is publically available) combined with CRE detection in promoter sequence data.</p