Abstract
Motivation: Identification of modules of co-regulated genes is a crucial first step towards dissecting the regulatory circuitry underlying biological processes. Co-regulated genes are likely to reveal themselves by showing tight co-expression, e.g. high correlation of expression profiles across multiple time series datasets. However, numbers of up- or downregulated genes are often large, making it difficult to discriminate between dependent co-expression resulting from co-regulation and independent co-expression. Furthermore, modules of co-regulated genes may only show tight co-expression across a subset of the time series, i.e. show condition-dependent regulation.Results: Wigwams is a simple and efficient method to identify gene modules showing evidence for co-regulation in multiple time series of gene expression data. Wigwams analyzes similarities of gene expression patterns within each time series (condition) and directly tests the dependence or independence of these across different conditions. The expression pattern of each gene in each subset of conditions is tested statistically as a potential signature of a condition-dependent regulatory mechanism regulating multiple genes. Wigwams does not require particular time points and can process datasets that are on different time scales. Differential expression relative to control conditions can be taken into account. The output is succinct and non-redundant, enabling gene network reconstruction to be focused on those gene modules and combinations of conditions that show evidence for shared regulatory mechanisms. Wigwams was run using six Arabidopsis time series expression datasets, producing a set of biologically significant modules spanning different combinations of conditions.Availability and implementation: A Matlab implementation of Wigwams, complete with graphical user interfaces and documentation, is available at: warwick.ac.uk/wigwams.Contact: k.j.denby@warwick.ac.ukSupplementary Data: Supplementary data are available at Bioinformatics online.
Highlights
Elucidating the regulatory mechanisms mediating biological processes is a key challenge in many eukaryotic organisms
We demonstrate that gene modules identified by Wigwams are often enriched for Gene Ontology (GO) terms (Ashburner et al, 2000) and known transcription factor (TF) binding motifs indicating biological relevance
The module identification procedure uses one gene at a time (‘seed gene’) and each combination of conditions in turn and tests whether the expression pattern of the seed gene across these time series may be driven by a regulatory mechanism acting on a number of genes under more than one condition
Summary
Elucidating the regulatory mechanisms mediating biological processes is a key challenge in many eukaryotic organisms. Much regulation occurs at the transcriptional level; despite our ability to profile genome-wide gene expression and the availability of bioinformatics tools to analyze sequence information, our understanding of gene regulatory networks underlying biological processes is still relatively basic. Regulatory interactions are often common, meaning that the ability to understand the regulation of a response requires a mathematical or computational network model. Underlying these network models is the knowledge of regulatory mechanisms. Techniques to identify regulatory mechanisms, such as genome-wide chromatin immunoprecipitation sequencing (Robertson et al, 2007) and matrixbased yeast one-hybrid (Y1H) (Deplancke et al, 2006), have improved, but these techniques are not high-throughput. It is crucial to be able to make high-quality predictions of regulatory mechanisms using existing data; these predictions can be tested in focused experimental and modelling efforts
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