Systematic identification of mammalian regulatory motifs' target genes and functions

We developed an algorithm, Lever, that systematically maps metazoan DNA regulatory motifs or motif combinations to sets of genes. Lever assesses whether the motifs are enriched in cis-regulatory modules (CRMs), predicted by our PhylCRM algorithm, in the noncoding sequences surrounding the genes. Lever analysis allows unbiased inference of functional annotations to regulatory motifs and candidate CRMs. We used human myogenic differentiation as a model system to statistically assess greater than 25,000 pairings of gene sets and motifs or motif combinations. We assigned functional annotations to candidate regulatory motifs predicted previously and identified gene sets that are likely to be co-regulated via shared regulatory motifs. Lever allows moving beyond the identification of putative regulatory motifs in mammalian genomes, toward understanding their biological roles. This approach is general and can be applied readily to any cell type, gene expression pattern or organism of interest.     

Integration of Steady-State and Temporal Gene Expression Data for the Inference of Gene Regulatory Networks

We develop a new regression algorithm, cMIKANA, for inference of gene regulatory networks from combinations of steady-state and time-series gene expression data. Using simulated gene expression datasets to assess the accuracy of reconstructing gene regulatory networks, we show that steady-state and time-series data sets can successfully be combined to identify gene regulatory interactions using the new algorithm. Inferring gene networks from combined data sets was found to be advantageous when using noisy measurements collected with either lower sampling rates or a limited number of experimental replicates. We illustrate our method by applying it to a microarray gene expression dataset from human umbilical vein endothelial cells (HUVECs) which combines time series data from treatment with growth factor TNF and steady state data from siRNA knockdown treatments. Our results suggest that the combination of steady-state and time-series datasets may provide better prediction of RNA-to-RNA interactions, and may also reveal biological features that cannot be identified from dynamic or steady state information alone. Finally, we consider the experimental design of genomics experiments for gene regulatory network inference and show that network inference can be improved by incorporating steady-state measurements with time-series data.