Park PJ, Butte AJ, Kohane IS.
Comparing expression profiles of genes with similar promoter regions. Bioinformatics 2002;18(12):1576-84.
Abstract
MOTIVATION: Gene regulatory elements are often predicted by seeking common sequences in the promoter regions of genes that are clustered together based on their expression profiles. We consider the problem in the opposite direction: we seek to find the genes that have similar promoter regions and determine the extent to which these genes have similar expression profiles. RESULTS: We use the data sets from experiments on Saccharomyces cerevisiae. Our similarity measure for the promoter regions is based on the set of common mapped or putative transcription factor binding sites and other regulatory elements in the upstream region of the genes, as contained in the Saccharomyces cerevisiae Promoter Database. We pair up the genes with high similarity scores and compare their expression levels in time-course experiment data. We find that genes with similar promoter regions on the average have significantly higher correlation, but it can vary widely depending on the genes. This confirms that the presence of similar regulatory elements often does not correspond to similarity in expression profiles and indicates that finding transcription factor binding sites or other regulatory elements starting with the expression patterns may be limited in many cases. Regardless of the correlation, the degree to which the profiles agree under different experimental conditions can be examined to derive hypotheses concerning the role of common regulatory elements. Overall, we find that considering the relationship between the promoter regions and the expression profiles starting with the regulatory elements is a difficult but useful process that can provide valuable insights.
pdf Kuruvilla FG, Park PJ, Schreiber SL.
Vector algebra in the analysis of genome-wide expression data. Genome Biol 2002;3(3):RESEARCH0011.
Abstract
BACKGROUND: Data from thousands of transcription-profiling experiments in organisms ranging from yeast to humans are now publicly available. How best to analyze these data remains an important challenge. A variety of tools have been used for this purpose, including hierarchical clustering, self-organizing maps and principal components analysis. In particular, concepts from vector algebra have proven useful in the study of genome-wide expression data. RESULTS: Here we present a framework based on vector algebra for the analysis of transcription profiles that is geometrically intuitive and computationally efficient. Concepts in vector algebra such as angles, magnitudes, subspaces, singular value decomposition, bases and projections have natural and powerful interpretations in the analysis of microarray data. Angles in particular offer a rigorous method of defining 'similarity' and are useful in evaluating the claims of a microarray-based study. We present a sample analysis of cells treated with rapamycin, an immunosuppressant whose effects have been extensively studied with microarrays. In addition, the algebraic concept of a basis for a space affords the opportunity to simplify data analysis and uncover a limited number of expression vectors to span the transcriptional range of cell behavior. CONCLUSIONS: This framework represents a compact, powerful and scalable construction for analysis and computation. As the amount of microarray data in the public domain grows, these vector-based methods are relevant in determining statistical significance. These approaches are also well suited to extract biologically meaningful information in the analysis of signaling networks.
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