Calibrating Thresholds to Improve the Detection Accuracy of Putative Transcription Factor Binding Sites.
- Author:
Young Jin KIM
1
;
Gil Mi RYU
;
Chan PARK
;
Kyu Won KIM
;
Bermseok OH
;
Young Youl KIM
;
Man Bok GU
Author Information
1. Center for Genome Science, National Institute of Health, KCDC, Seoul 122-701, Korea. youngyk@nih.go.kr
- Publication Type:Original Article
- Keywords:
combinatorial TFBS;
regulatory conservation score;
regulatory element;
transcription factor binding site
- MeSH:
Binding Sites*;
Calibration;
Regulatory Sequences, Nucleic Acid;
Transcription Factors*
- From:Genomics & Informatics
2007;5(4):143-151
- CountryRepublic of Korea
- Language:English
-
Abstract:
To understand the mechanism of transcriptional regulation, it is essential to detect promoters and regulatory elements. Various kinds of methods have been introduced to improve the prediction accuracy of regulatory elements. Since there are few experimentally validated regulatory elements, previous studies have used criteria based solely on the level of scores over background sequences. However, selecting the detection criteria for different prediction methods is not feasible. Here, we studied the calibration of thresholds to improve regulatory element prediction. We predicted a regulatory element using MATCH, which is a powerful tool for transcription factor binding site (TFBS) detection. To increase the prediction accuracy, we used a regulatory potential (RP) score measuring the similarity of patterns in alignments to those in known regulatory regions. Next, we calibrated the thresholds to find relevant scores, increasing the true positives while decreasing possible false positives. By applying various thresholds, we compared predicted regulatory elements with validated regulatory elements from the Open Regulatory Annotation (ORegAnno) database. The predicted regulators by the selected threshold were validated through enrichment analysis of muscle-specific gene sets from the Tissue-Specific Transcripts and Genes (T-STAG) database. We found 14 known muscle-specific regulators with a less than a 5% false discovery rate (FDR) in a single TFBS analysis, as well as known transcription factor combinations in our combinatorial TFBS analysis.
