Gene regulatory network inference is essential to uncover complex relationships among gene pathways and inform downstream experiments, ultimately enabling regulatory network re-engineering. Network inference from transcriptional time-series data requires accurate, interpretable, and efficient determination of causal relationships among thousands of genes. Here, we develop Bootstrap Elastic net regression from Time Series (BETS), a statistical framework based on Granger causality for the recovery of a directed gene network from transcriptional time-series data. BETS uses elastic net regression and stability selection from bootstrapped samples to infer causal relationships among genes. BETS is highly parallelized, enabling efficient analysis of large transcriptional data sets. We show competitive accuracy on a community benchmark, the DREAM4 100-gene network inference challenge, where BETS is one of the fastest among methods of similar performance and additionally infers whether causal effects are activating or inhibitory. We apply BETS to transcriptional time-series data of differentially-expressed genes from A549 cells exposed to glucocorticoids over a period of 12 hours. We identify a network of 2768 genes and 31,945 directed edges (FDR ≤ 0.2). We validate inferred causal network edges using two external data sources: Overexpression experiments on the same glucocorticoid system, and genetic variants associated with inferred edges in primary lung tissue in the Genotype-Tissue Expression (GTEx) v6 project. BETS is available as an open source software package at https://github.com/lujonathanh/BETS.

MOTIVATION: High-throughput reporter assays dramatically improve our ability to assign function to noncoding genetic variants, by measuring allelic effects on gene expression in the controlled setting of a reporter gene. Unlike genetic association tests, such assays are not confounded by linkage disequilibrium when loci are independently assayed. These methods can thus improve the identification of causal disease mutations. While work continues on improving experimental aspects of these assays, less effort has gone into developing methods for assessing the statistical significance of assay results, particularly in the case of rare variants captured from patient DNA. RESULTS: We describe a Bayesian hierarchical model, called Bayesian Inference of Regulatory Differences, which integrates prior information and explicitly accounts for variability between experimental replicates. The model produces substantially more accurate predictions than existing methods when allele frequencies are low, which is of clear advantage in the search for disease-causing variants in DNA captured from patient cohorts. Using the model, we demonstrate a clear tradeoff between variant sequencing coverage and numbers of biological replicates, and we show that the use of additional biological replicates decreases variance in estimates of effect size, due to the properties of the Poisson-binomial distribution. We also provide a power and sample size calculator, which facilitates decision making in experimental design parameters. AVAILABILITY AND IMPLEMENTATION: The software is freely available from www.geneprediction.org/bird. The experimental design web tool can be accessed at http://67.159.92.22:8080. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Over the past several years fungal infections have shown an increasing incidence in the susceptible population, and caused high mortality rates. In parallel, multi-resistant fungi are emerging in human infections. Therefore, the identification of new potential antifungal targets is a priority. The first task of this study was to analyse the protein domain and domain architecture content of the 137 fungal proteomes (corresponding to 111 species) available in UniProtKB (UniProt KnowledgeBase) by January 2013. The resulting list of core and exclusive domain and domain architectures is provided in this paper. It delineates the different levels of fungal taxonomic classification: phylum, subphylum, order, genus and species. The analysis highlighted Aspergillus as the most diverse genus in terms of exclusive domain content. In addition, we also investigated which domains could be considered promiscuous in the different organisms. As an application of this analysis, we explored three different ways to detect potential targets for antifungal drugs. First, we compared the domain and domain architecture content of the human and fungal proteomes, and identified those domains and domain architectures only present in fungi. Secondly, we looked for information regarding fungal pathways in public repositories, where proteins containing promiscuous domains could be involved. Three pathways were identified as a result: lovastatin biosynthesis, xylan degradation and biosynthesis of siroheme. Finally, we classified a subset of the studied fungi in five groups depending on their occurrence in clinical samples. We then looked for exclusive domains in the groups that were more relevant clinically and determined which of them had the potential to bind small molecules. Overall, this study provides a comprehensive analysis of the available fungal proteomes and shows three approaches that can be used as a first step in the detection of new antifungal targets.