Abstract
Motivation: Inferring global regulatory networks (GRNs) from genome-wide data is a computational challenge central to the field of systems biology. Although the primary data currently used to infer GRNs consist of gene expression and proteomics measurements, there is a growing abundance of alternate data types that can reveal regulatory interactions, e.g. ChIP-Chip, literature-derived interactions, protein–protein interactions. GRN inference requires the development of integrative methods capable of using these alternate data as priors on the GRN structure. Each source of structure priors has its unique biases and inherent potential errors; thus, GRN methods using these data must be robust to noisy inputs.Results: We developed two methods for incorporating structure priors into GRN inference. Both methods [Modified Elastic Net (MEN) and Bayesian Best Subset Regression (BBSR)] extend the previously described Inferelator framework, enabling the use of prior information. We test our methods on one synthetic and two bacterial datasets, and show that both MEN and BBSR infer accurate GRNs even when the structure prior used has significant amounts of error (>90% erroneous interactions). We find that BBSR outperforms MEN at inferring GRNs from expression data and noisy structure priors.Availability and implementation: Code, datasets and networks presented in this article are available at http://bonneaulab.bio.nyu.edu/software.html.Contact: bonneau@nyu.eduSupplementary information: Supplementary data are available at Bioinformatics online.
Highlights
Understanding how global regulatory networks (GRNs) coordinate systems-level response of a cell or organism to a new environmental state or perturbation is a key problem in systems biology, with applications spanning biofuels (Bonneau et al, 2007), novel therapeutic targets (Carro et al, 2010) and the discovery of novel pathways involved in cellular differentiation (Ciofani et al, 2012)
For this initial investigation of parameter sensitivity, we used the entire gold standard as input, and assessed performance over the set of gold standard interactions (GSIs). Though this design is circular, the purpose was to characterize the sensitivity of our method to the choice of and g, the parameters that control the relative influence of the structure prior for Modified Elastic Net (MEN) and Best Subset Regression (BBSR) respectively
To test the robustness of MEN and BBSR to incorrect prior information, for each network, we considered half of the GSIs as true prior interactions (TPIs), and added a varying number of random false prior interactions (FPIs)
Summary
Understanding how global regulatory networks (GRNs) coordinate systems-level response of a cell or organism to a new environmental state or perturbation is a key problem in systems biology, with applications spanning biofuels (Bonneau et al, 2007), novel therapeutic targets (Carro et al, 2010) and the discovery of novel pathways involved in cellular differentiation (Ciofani et al, 2012). Recent advances in the quality and availability of high-throughput technologies enable measurement of different components of the GRN including mRNA transcript levels, protein levels, post-translational modifications, as well as DNA characteristics such as transcription factor-binding regions and open chromatin locations (ENCODE Project Consortium, 2012). These multi-level and multi-scale datasets have made the inference of integrative GRNs possible. As high-throughput data capturing the abundance of mRNA transcripts are the most mature and readily available, many methods focus only on this single level of regulation, learning transcriptional regulatory networks. The priors we use in this work provide information about connectivity but do not provide any information about the relative strength, importance or dynamic properties of each known regulatory edge (these we atempt to learn from the data)
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