Abstract
The Dialogue for Reverse Engineering Assessments and Methods (DREAM) project was initiated in 2006 as a community-wide effort for the development of network inference challenges for rigorous assessment of reverse engineering methods for biological networks. We participated in the in silico network inference challenge of DREAM3 in 2008. Here we report the details of our approach and its performance on the synthetic challenge datasets. In our methodology, we first developed a model called relative change ratio (RCR), which took advantage of the heterozygous knockdown data and null-mutant knockout data provided by the challenge, in order to identify the potential regulators for the genes. With this information, a time-delayed dynamic Bayesian network (TDBN) approach was then used to infer gene regulatory networks from time series trajectory datasets. Our approach considerably reduced the searching space of TDBN; hence, it gained a much higher efficiency and accuracy. The networks predicted using our approach were evaluated comparatively along with 29 other submissions by two metrics (area under the ROC curve and area under the precision-recall curve). The overall performance of our approach ranked the second among all participating teams.Electronic supplementary materialThe online version of this article (doi:10.1186/s13637-014-0012-3) contains supplementary material, which is available to authorized users.
Highlights
Recent development of high-throughput technologies such as DNA microarray and RNA-Seq has made it possible for biologists to simultaneously measure gene expression at a genome scale
Inferred networks as compared with the true networks In this work, our approach was applied to inferring GRNs in three different ways: For in silico networks with 10 genes, the gene regulatory networks were inferred only by the relative change ratio (RCR) method from steady state data, in which we used mainly the gene knockout dataset; for networks with 50 genes, the networks inferred using RCR and time-delayed dynamic Bayesian network (TDBN) separately were combined into the final networks; for networks with 100 genes, we used only TDBN to reconstruct gene networks from time series trajectory gene expression dataset
One of the inferred E. coli 10-node GRN is shown in Figure 3, where seven matching edges are correctly identified by our model, in comparison to the corresponding true network
Summary
Recent development of high-throughput technologies such as DNA microarray and RNA-Seq (i.e., next-generation sequencing of RNA transcripts) has made it possible for biologists to simultaneously measure gene expression at a genome scale. Various mathematical methods and computational approaches have been proposed to infer gene regulatory networks (GRN) from DNA microarray data, including. The relative performances among these algorithms are not well studied because computational biologists must repeatedly test them on large-scale and high-quality datasets obtained from different experimental conditions and derived from different networks. Experimental datasets of customized size and design are usually unavailable and most biological networks are unknown or incomplete. Since each of these methods uses different datasets and comparison strategies, it is difficult to systematically validate the interactions predicted by different computational approaches
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