Beyond synexpression relationships: local clustering of time-shifted and inverted gene expression profiles identifies new, biologically relevant interactions

Abstract

The complexity of biological systems provides for a great diversity of relationships between genes. The current analysis of whole-genome expression data focuses on relationships based on global correlation over a whole time-course, identifying clusters of genes whose expression levels simultaneously rise and fall. There are, of course, other potential relationships between genes, which are missed by such global clustering. These include activation, where one expects a time-delay between related expression pro®les, and inhibition, where one expects an inverted relationship. Here, we propose a new method, which we call local clustering, for identifying these time-delayed and inverted relationships. It is related to conventional gene-expression clustering in a fashion analogous to the way local sequence alignment (the Smith-Waterman algorithm) is derived from global alignment (Needleman-Wunsch). An integral part of our method is the use of random score distributions to assess the statistical signi®cance of each cluster. We applied our method to the yeast cellcycle expression dataset and were able to detect a considerable number of additional biological relationships between genes, beyond those resulting from conventional correlation. We related these new relationships between genes to their similarity in function (as determined from the MIPS scheme) or their having known protein-protein interactions (as determined from the large-scale two-hybrid experiment); we found that genes strongly related by local clustering were considerably more likely than random to have a known interaction or a similar cellular role. This suggests that local clustering may be useful in functional annotation of uncharacterized genes. We examined many of the new relationships in detail. Some of them were already well-documented examples of inhibition or activation, which provide corroboration for our results. For instance, we found an inverted expression pro®le relationship between genes YME1 and YNT20, where the latter has been experimentally documented as a bypass suppressor of the former. We also found new relationships involving uncharacterized yeast genes and were able to suggest functions for many of them. In particular, we found a timedelayed expression relationship between J0544 (which has not yet been functionally characterized) and four genes associated with the mitochondria. This suggests that J0544 may be involved in the control or activation of mitochondrial genes. We have also looked at other, less extensive datasets than the yeast cell-cycle and found further interesting relationships. Our clustering program and a detailed website of clustering results is available at

Altered gene expression in acute systemic inflammation detected by complete coverage of the human liver transcriptome

“…Third, mRNA changes can be studied in an integrative context in which the so-called "guilt-by-association" approach aims at identification of all the participants in a given signaling cascade or metabolic pathway. [1][2][3] Progress in any of these areas with array technology 4 depends on probe diversity. Therefore, the use of a pan-genomic set of probes is the ideal option.…”

“…However, in practice, such an array is impractical given the current uncertainties about the number and identity of human genes, 5,6 the exponentially growing complexity of data analysis that results from a linearly increasing number of probes, 7 and the cost of a pan-genomic probe set. Nevertheless, maintaining a high probe diversity for studies in a given cell type or tissue context is required for an integrative approach and should also help discover many candidate genes whose hallmark 3 Service de Chirurgie Générale et Digestive, and 4 Département de Pathologie,appears to be a tissue-restricted expression. 8 Overall, a probe selection that results in a virtually complete coverage of the transcriptome in a given cell type or tissue is likely to be the best choice for the time being.…”

Bayesian Analysis of Curves Shape Variation Through Registration and Regression

“…Specifically, time course genomics data, consist of measurements from a common set of genes collected at different time points and provide new opportunities into the understanding of gene regulation. In particular, clues about the temporal structure of expression may be informative about co-regulation and gene-gene relationships (Qian et al 2001;Leng and Müller 2006). In this section we discuss the approach of Telesca et al (2009), who introduced a model-based selection of differentially expressed genes, and a probabilistic framework for the investigation of regulatory relationships between genes.…”

Network Inference, Analysis, and Modeling in Systems Biology

“…Clustering tools such as the Arabidopsis coexpression tool, based on microarray data from the Nottingham Arabidopsis Stock Centre (Craigon et al, 2004), allow users to quantify gene coexpression across selected experiments or the complete data set (Jen et al, 2006). These methods give insight into groups of genes that respond in a similar manner to varying conditions and that might therefore be coregulated (Qian et al, 2001); however, that two nodes belong to the same group does not imply a causal relationship among them. The ability to extract meaning from clustering depends on the user's prior biological understanding of the objects that are organized.…”