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This article is part of the supplement: Selected articles from the Eighth Asia-Pacific Bioinformatics Conference (APBC 2010)

Open Access Open Badges Research

Knowledge-based analysis of microarrays for the discovery of transcriptional regulation relationships

Junhee Seok12*, Amit Kaushal1, Ronald W Davis1 and Wenzhong Xiao13*

Author Affiliations

1 Stanford Genome Technology Center, 955 California Avenue, Palo Alto, California, 94305, USA

2 Department of Electrical Engineering, Stanford University, 350 Serra Mall, Stanford, California, 94305, USA

3 Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, Massachusetts, 02114, USA

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BMC Bioinformatics 2010, 11(Suppl 1):S8  doi:10.1186/1471-2105-11-S1-S8

Published: 18 January 2010



The large amount of high-throughput genomic data has facilitated the discovery of the regulatory relationships between transcription factors and their target genes. While early methods for discovery of transcriptional regulation relationships from microarray data often focused on the high-throughput experimental data alone, more recent approaches have explored the integration of external knowledge bases of gene interactions.


In this work, we develop an algorithm that provides improved performance in the prediction of transcriptional regulatory relationships by supplementing the analysis of microarray data with a new method of integrating information from an existing knowledge base. Using a well-known dataset of yeast microarrays and the Yeast Proteome Database, a comprehensive collection of known information of yeast genes, we show that knowledge-based predictions demonstrate better sensitivity and specificity in inferring new transcriptional interactions than predictions from microarray data alone. We also show that comprehensive, direct and high-quality knowledge bases provide better prediction performance. Comparison of our results with ChIP-chip data and growth fitness data suggests that our predicted genome-wide regulatory pairs in yeast are reasonable candidates for follow-up biological verification.


High quality, comprehensive, and direct knowledge bases, when combined with appropriate bioinformatic algorithms, can significantly improve the discovery of gene regulatory relationships from high throughput gene expression data.