The abundance of transcriptomic data and the development of causal inference methods have paved the way for gene network analyses in grapevine. Vitis OneGenE is a transcriptomic data mining tool that finds direct correlations between genes, thus producing association networks. As a proof of concept, the stilbene synthase gene regulatory network obtained with OneGenE has been compared with published co-expression analysis and experimental data, including cistrome data for MYB stilbenoid regulators. As a case study, the two secondary metabolism pathways of stilbenoids and lignin synthesis were explored. Several isoforms of laccase, peroxidase, and dirigent protein genes, putatively involved in the final oxidative oligomerization steps, were identified as specifically belonging to either one of these pathways. Manual curation of the predicted sequences exploiting the last available genome assembly, and the integration of phylogenetic and OneGenE analyses, identified a group of laccases exclusively present in grapevine and related to stilbenoids. Here we show how network analysis by OneGenE can accelerate knowledge discovery by suggesting new candidates for functional characterization and application in breeding programs.
Our group has recently developed gene@home, a BOINC project that permits to search for candidate genes for the expansion of a gene regulatory network using gene expression data. The gene@home project adopts intensive variablesubsetting strategies enabled by the computational power provided by the volunteers who have joined the project by means of the BOINC client, and exploits the PC algorithm for discovering putative causal relationships within each subset of variables. This paper presents our TN-Grid infrastructure that is hosting the gene@home project. Gene@home implements a novel method for Network Expansion by Subsetting and Ranking Aggregation (NESRA), producing a list of genes that are candidates for the gene network expansion task. NESRA is an algorithm that has: 1) a ranking procedure that systematically subsets the variables; the subsetting is iterated several times and a ranked list of candidates is produced by counting the number of times a relationship is found; 2) several ranking steps are executed with different values of the dimension of the subsets and with different number of iterations producing several ranked lists; 3) the ranked lists are aggregated by using a state-of-the-art ranking aggregator. In our experimental results, we show that a single ranking step is enough to outperform both PC and PC*. Evaluations and experiments are done by means of the gene@home project on a real gene regulatory network of the model plant Arabidopsis thaliana.
In recent years the scientific community has been heavily engaged in studying the grapevine response to climate change. Final goal is the identification of key genetic traits to be used in grapevine breeding and the setting of agronomic practices to improve climatic resilience. The increasing availability of transcriptomic studies, describing gene expression in many tissues and developmental, or treatment conditions, have allowed the implementation of gene expression compendia, which enclose a huge amount of information. The mining of transcriptomic data represents an effective approach to expand a known local gene network (LGN) by finding new related genes. We recently published a pipeline based on the iterative application of the PC-algorithm, named NES2RA, to expand gene networks in Escherichia coli and Arabidopsis thaliana. Here, we propose the application of this method to the grapevine transcriptomic compendium Vespucci, in order to expand four LGNs related to the grapevine response to climate change. Two networks are related to the secondary metabolic pathways for anthocyanin and stilbenoid synthesis, involved in the response to solar radiation, whereas the other two are signaling networks, related to the hormones abscisic acid and ethylene, possibly involved in the regulation of cell water balance and cuticle transpiration. The expansion networks produced by NES2RA algorithm have been evaluated by comparison with experimental data and biological knowledge on the identified genes showing fairly good consistency of the results. In addition, the algorithm was effective in retaining only the most significant interactions among the genes providing a useful framework for experimental validation. The application of the NES2RA to Vitis vinifera expression data by means of the BOINC-based implementation is available upon request (valter.cavecchia@cnr.it).
The automatic discovery of causal relationships among human genes can shed light on gene regulatory processes and guide drug repositioning. To this end, a computationally-heavy method for causal discovery is distributed on a volunteer computing grid and, taking advantage of variable subsetting and stratification, proves to be useful for expanding local gene regulatory networks. The input data are purely observational measures of transcripts expression in human tissues and cell lines collected within the FANTOM project. The system relies on the BOINC platform and on optimized client code. The functional relevance of results, measured by analyzing the annotations of the identified interactions, increases significantly over the simple Pearson correlation between the transcripts. Additionally, in 82% of cases networks significantly overlap with known protein-protein interactions annotated in biological databases. In the two case studies presented, this approach has been used to expand the networks of genes associated with two severe human pathologies: prostate cancer and coronary artery disease. The method identified respectively 22 and 36 genes to be evaluated as novel targets for already approved drugs, demonstrating the effective applicability of the approach in pipelines aimed to drug repositioning.
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