TY - JOUR
T1 - Robust data-driven incorporation of prior knowledge into the inference of dynamic regulatory networks
AU - Greenfield, Alex
AU - Hafemeister, Christoph
AU - Bonneau, Richard
N1 - Funding Information:
Funding: NIH grants (RC1 AI087266, RC4 PN2 EY016586, IU54CA143907-01, EY016586-06).
PY - 2013/4/15
Y1 - 2013/4/15
N2 - 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 (490%erroneous interactions). We find that BBSR outperforms MEN at inferring GRNs from expression data and noisy structure priors.
AB - 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 (490%erroneous interactions). We find that BBSR outperforms MEN at inferring GRNs from expression data and noisy structure priors.
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U2 - 10.1093/bioinformatics/btt099
DO - 10.1093/bioinformatics/btt099
M3 - Article
C2 - 23525069
AN - SCOPUS:84876207916
SN - 1367-4803
VL - 29
SP - 1060
EP - 1067
JO - Bioinformatics
JF - Bioinformatics
IS - 8
ER -