Abstract
BackgroundThe reconstruction of gene regulatory networks from high-throughput "omics" data has become a major goal in the modelling of living systems. Numerous approaches have been proposed, most of which attempt only "one-shot" reconstruction of the whole network with no intervention from the user, or offer only simple correlation analysis to infer gene dependencies.ResultsWe have developed MINER (Microarray Interactive Network Exploration and Representation), an application that combines multivariate non-linear tree learning of individual gene regulatory dependencies, visualisation of these dependencies as both trees and networks, and representation of known biological relationships based on common Gene Ontology annotations. MINER allows biologists to explore the dependencies influencing the expression of individual genes in a gene expression data set in the form of decision, model or regression trees, using their domain knowledge to guide the exploration and formulate hypotheses. Multiple trees can then be summarised in the form of a gene network diagram. MINER is being adopted by several of our collaborators and has already led to the discovery of a new significant regulatory relationship with subsequent experimental validation.ConclusionUnlike most gene regulatory network inference methods, MINER allows the user to start from genes of interest and build the network gene-by-gene, incorporating domain expertise in the process. This approach has been used successfully with RNA microarray data but is applicable to other quantitative data produced by high-throughput technologies such as proteomics and "next generation" DNA sequencing.
Highlights
The reconstruction of gene regulatory networks from high-throughput “omics” data has become a major goal in the modelling of living systems
RNA species that vary together under a range of conditions are likely to be under common regulation, and sets of “co-expressed” genes generated by clustering of microarray expression values have proven useful for identifying potential regulatory elements and transcription factor binding sites [1,2,3,4,5]. This type of analysis has been extended to look for patterns of expression correlation between genes resulting from regulatory relationships, for example increased RNA levels for a transcription factor leading to an increase in the RNA levels of the genes whose transcription is activated by this factor
Interactive exploration of potential regulatory relationships MINER is a web-based framework that analyses microarray data to suggest likely hypotheses regarding regulatory relationships between genes surveyed in the dataset
Summary
The reconstruction of gene regulatory networks from high-throughput “omics” data has become a major goal in the modelling of living systems. RNA species that vary together under a range of conditions are likely to be under common regulation, and sets of “co-expressed” genes generated by clustering of microarray expression values have proven useful for identifying potential regulatory elements and transcription factor binding sites [1,2,3,4,5]. This type of analysis has been extended to look for patterns of expression correlation between genes resulting from regulatory relationships, for example increased RNA levels for a transcription factor leading to an increase in the RNA levels of the genes whose transcription is activated by this factor. Methods that integrate multiple sources of information (expression levels, biological annotation, protein levels etc) [16,17,18] are promising but face difficulty in capturing and integrating all the relevant biological information, and their complexity can be prohibitive for the biologist user
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