Abstract
We present a novel approach, the Local Edge Machine, for the inference of regulatory interactions directly from time-series gene expression data. We demonstrate its performance, robustness, and scalability on in silico datasets with varying behaviors, sizes, and degrees of complexity. Moreover, we demonstrate its ability to incorporate biological prior information and make informative predictions on a well-characterized in vivo system using data from budding yeast that have been synchronized in the cell cycle. Finally, we use an atlas of transcription data in a mammalian circadian system to illustrate how the method can be used for discovery in the context of large complex networks.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-1076-z) contains supplementary material, which is available to authorized users.
Highlights
Dynamic gene expression programs have been observed in a wide variety of organisms
It is believed that the observed temporal dynamics are an emergent property of underlying transcription networks, which consist of interacting collections of transcription factors (TFs) [1,2,3]
Description of the method Given a set of genes deemed to be potentially important for network function, Local Edge Machine (LEM) takes a Bayesian approach to answer the following question: of all possible regulators, which regulator and regulatory logic best models the expression dynamics of each gene? Here, we provide a brief description of how the LEM algorithm models the gene expression of each node and scores each possible regulation in the network
Summary
Dynamic gene expression programs have been observed in a wide variety of organisms. It is difficult to assay such transcription networks directly, high-throughput technologies allow the measurement of transcription levels in time-course experiments [4, 5]. Using such time-course transcriptome data to infer the structure of transcription networks is considered a major problem in computational biology [6, 7]. The network inference problem persists in systems biology, despite an abundance of regulatory evidence in the form of TF binding experiments, genetic screens for candidate nodes, and mutant expression profiling experiments
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