Pathway Processor: A Tool for Integrating Whole-Genome Expression Results into Metabolic Networks

Abstract

We have developed a new tool to visualize expression data on metabolic pathways and to evaluate which metabolic pathways are most affected by transcriptional changes in whole-genome expression experiments. Using the Fisher Exact Test, the method scores biochemical pathways according to the probability that as many or more genes in a pathway would be significantly altered in a given experiment by chance alone. This method has been validated on diauxic shift experiments and reproduces well known effects of carbon source on yeast metabolism. The analysis is implemented with Pathway Analyzer, one of the tools of Pathway Processor, a new statistical package for the analysis of whole-genome expression data. Results from multiple experiments can be compared, reducing the analysis from the full set of individual genes to a limited number of pathways of interest. The pathways are visualized with OpenDX, an open-source visualization software package, and the relationship between genes in the pathways can be examined in detail using Expression Mapper, the second program of the package. This program features a graphical output displaying differences in expression on metabolic charts of the biochemical pathways to which the open reading frames are assigned.[Supplementary materials are available at

Global gene expression analysis in liver of obese diabetic db/db mice treated with metformin

“…The methods used to extract biological meaning from the extensive gene expression data obtained from DNA microarray analysis study are still in progress; however, approaches using multivariate analysis, such as hierarchical clustering and principal component analysis, have been shown to be useful especially for data showing large differences in expression of numerous genes between samples [18][19][20][21][22]. When the number of differentially expressed genes identified is too numerous to extract an overall interpretation from the biological function of individual genes, genes belonging to each metabolic pathway or a 'gene ontology' class are treated as a cohort, and can be used to understand the biological significance of the observed changes of expression [23][24][25][26].…”

Functional genomics of neural and behavioral plasticity

“…They also have developed tools that enable the mapping of microarray data onto these databases. These tools enable the user to determine instantly which pathways (and which of their genes) were affected by a particular experimental treatment (Dahlquist et al, 2002;Grosu et al, 2002). Such gene expression maps can be used as a gene discovery tool to identify coregulated genes or to uncover previously unknown genetic functions.…”

What can whole genome expression data tell us about the ecology and evolution of personality?

“…In many cases, individual genes that are part of an important pathway that is related to the phenotype are not statistically differentially expressed. The statistical probability that several components of the pathway change in expression owing to chance alone can be estimated, allowing researchers to detect significant changes at the pathway level (for examples in model systems, see Grosu et al (2002) and Draghici et al (2007)). Furthermore, hypotheses can be made a priori for specific pathways based on knowledge of that pathway's function (Villeneuve et al 2007).…”