In Silico Evaluation of Predicted Regulatory Interactions in Arabidopsis thaliana

Damion Nero, Manpreet S. Katari, Jonathan Kelfer, Daniel Tranchina, Gloria M. Coruzzi

Research output: Contribution to journalArticle

Abstract

Background: Prediction of transcriptional regulatory mechanisms in Arabidopsis 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 1, that a combination of correlation measurements and cis-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.Results: We tested several widely used methods, based on correlation (Pearson and Spearman Rank correlation) and cis-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.Conclusion: 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.

Original languageEnglish (US)
Article number435
JournalBMC Bioinformatics
Volume10
DOIs
StatePublished - Dec 21 2009

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Arabidopsis Thaliana
Microarrays
Arabidopsis
Computer Simulation
Transcription factors
Evaluation
Microarray Data
Interaction
Transcription Factors
Genes
Transcription Factor
Correlation methods
Target
Prediction
Gene expression
Explosions
Gene
Gene Expression
Spearman's coefficient
DNA

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Structural Biology
  • Applied Mathematics

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In Silico Evaluation of Predicted Regulatory Interactions in Arabidopsis thaliana. / Nero, Damion; Katari, Manpreet S.; Kelfer, Jonathan; Tranchina, Daniel; Coruzzi, Gloria M.

In: BMC Bioinformatics, Vol. 10, 435, 21.12.2009.

Research output: Contribution to journalArticle

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