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

BMC Bioinformatics

Research

Ronald W Davis¹  Amit Kaushal¹  Junhee Seok²  Wenzhong Xiao³ 

[1] Stanford Genome Technology Center, 955 California Avenue, 94305, Palo Alto, California, USA;Stanford Genome Technology Center, 955 California Avenue, 94305, Palo Alto, California, USA;Department of Electrical Engineering, Stanford University, 350 Serra Mall, 94305, Stanford, California, USA;Stanford Genome Technology Center, 955 California Avenue, 94305, Palo Alto, California, USA;Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, 02114, Boston, Massachusetts, USA;

关键词: Support Vector Machine;    True Positive Rate;    Regulatory Relationship;    Regulatory Relation;    Support Vector Machine Method;   

DOI  :  10.1186/1471-2105-11-S1-S8

来源: Springer

【 摘 要 】

BackgroundThe 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.ResultsIn 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.ConclusionHigh 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.

【 授权许可】

CC BY   
© Seok et al; licensee BioMed Central Ltd. 2010