The Plant Promoter Analysis Navigator (PlantPAN; http://PlantPAN.itps.ncku.edu.tw/) is an effective resource for predicting regulatory elements and reconstructing transcriptional regulatory networks for plant genes. In this release (PlantPAN 3.0), 17 230 TFs were collected from 78 plant species. To explore regulatory landscapes, genomic locations of TFBSs have been captured from 662 public ChIP-seq samples using standard data processing. A total of 1 233 999 regulatory linkages were identified from 99 regulatory factors (TFs, histones and other DNA-binding proteins) and their target genes across seven species. Additionally, this new version added 2449 matrices extracted from ChIP-seq peaks for cis-regulatory element prediction. In addition to integrated ChIP-seq data, four major improvements were provided for more comprehensive information of TF binding events, including (i) 1107 experimentally verified TF matrices from the literature, (ii) gene regulation network comparison between two species, (iii) 3D structures of TFs and TF-DNA complexes and (iv) condition-specific co-expression networks of TFs and their target genes extended to four species. The PlantPAN 3.0 can not only be efficiently used to investigate critical cis- and trans-regulatory elements in plant promoters, but also to reconstruct high-confidence relationships among TF–targets under specific conditions.

Nk-2 proteins are essential developmental regulators from flies to humans. In Drosophila, the family member tinman is the major regulator of cell fate within the dorsal mesoderm, including heart, visceral, and dorsal somatic muscle. To decipher Tinman's direct regulatory role, we performed a time course of ChIP-on-chip experiments, revealing a more prominent role in somatic muscle specification than previously anticipated. Through the combination of transgenic enhancer-reporter assays, colocalization studies, and phenotypic analyses, we uncovered two additional factors within this myogenic network: by activating eyes absent, Tinman's regulatory network extends beyond developmental stages and tissues where it is expressed; by regulating stat92E expression, Tinman modulates the transcriptional readout of JAK/STAT signaling. We show that this pathway is essential for somatic muscle development in Drosophila and for myotome morphogenesis in zebrafish. Taken together, these data uncover a conserved requirement for JAK/STAT signaling and an important component of the transcriptional network driving myogenesis.

We present a computational method for identifying potential targets of a transcription factor (TF) using wild-type gene expression time series data. For each putative target gene we fit a simple differential equation model of transcriptional regulation, and the model likelihood serves as a score to rank targets. The expression profile of the TF is modeled as a sample from a Gaussian process prior distribution that is integrated out using a nonparametric Bayesian procedure. This results in a parsimonious model with relatively few parameters that can be applied to short time series datasets without noticeable overfitting. We assess our method using genome-wide chromatin immunoprecipitation (ChIP-chip) and lossof-function mutant expression data for two TFs, Twist, and Mef2, controlling mesoderm development in Drosophila. Lists of topranked genes identified by our method are significantly enriched for genes close to bound regions identified in the ChIP-chip data and for genes that are differentially expressed in loss-of-function mutants. Targets of Twist display diverse expression profiles, and in this case a model-based approach performs significantly better than scoring based on correlation with TF expression. Our approach is found to be comparable or superior to ranking based on mutant differential expression scores. Also, we show how integrating complementary wild-type spatial expression data can further improve target ranking performance.Bayesian inference | Gaussian process inference | gene regulation | gene regulatory network | systems biology T ranscription factors are key nodes in the gene regulatory networks that determine the function and fate of cells. An important first step in uncovering a gene regulatory network is the identification of target genes regulated by a specific transcription factor (TF). A common approach to this problem is to experimentally locate physical binding of TF proteins to the DNA sequence in vivo using a genome-wide chromatin immunoprecipitation (ChIP) experiment (1, 2). However, recent studies suggest that many observed binding events are neutral and do not regulate transcription, while regulatory binding events often occur at enhancers that are not proximal to the target gene that they control (3, 4). Therefore, the task of identifying transcriptional targets requires the integration of ChIP binding predictions with evidence from expression data to help associate binding events with target gene regulation. If there is access to expression data from a mutant in which the TF has been knocked out or overexpressed, then differential expression of genes between wild type and mutant is indicative of a potential regulatory interaction (5, 6). Available spatial expression data for the TF and the putative target can also provide support for a hypothesized regulatory link.A problem with the above approach is that the creation of mutant strains is challenging or impossible for many TFs of interest. Even when available, mutants may provide very limited information because of redundancy or...

Precise patterns of spatial and temporal gene expression are central to metazoan complexity and act as a driving force for embryonic development. While there has been substantial progress in dissecting and predicting cis-regulatory activity, our understanding of how information from multiple enhancer elements converge to regulate a gene's expression remains elusive. This is in large part due to the number of different biological processes involved in mediating regulation as well as limited availability of experimental measurements for many of them. Here, we used a Bayesian approach to model diverse experimental regulatory data, leading to accurate predictions of both spatial and temporal aspects of gene expression. We integrated whole-embryo information on transcription factor recruitment to multiple cis-regulatory modules, insulator binding and histone modification status in the vicinity of individual gene loci, at a genome-wide scale during Drosophila development. The model uses Bayesian networks to represent the relation between transcription factor occupancy and enhancer activity in specific tissues and stages. All parameters are optimized in an Expectation Maximization procedure providing a model capable of predicting tissue- and stage-specific activity of new, previously unassayed genes. Performing the optimization with subsets of input data demonstrated that neither enhancer occupancy nor chromatin state alone can explain all gene expression patterns, but taken together allow for accurate predictions of spatio-temporal activity. Model predictions were validated using the expression patterns of more than 600 genes recently made available by the BDGP consortium, demonstrating an average 15-fold enrichment of genes expressed in the predicted tissue over a naïve model. We further validated the model by experimentally testing the expression of 20 predicted target genes of unknown expression, resulting in an accuracy of 95% for temporal predictions and 50% for spatial. While this is, to our knowledge, the first genome-wide approach to predict tissue-specific gene expression in metazoan development, our results suggest that integrative models of this type will become more prevalent in the future.

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