@article{thomas_broeck_spurney_sozzani_frank_2022, title={Gene regulatory networks for compatible versus incompatible grafts identify a role for SlWOX4 during junction formation}, volume={34}, ISSN={["1532-298X"]}, url={https://doi.org/10.1093/plcell/koab246}, DOI={10.1093/plcell/koab246}, abstractNote={Abstract
               Grafting has been adopted for a wide range of crops to enhance productivity and resilience; for example, grafting of Solanaceous crops couples disease-resistant rootstocks with scions that produce high-quality fruit. However, incompatibility severely limits the application of grafting and graft incompatibility remains poorly understood. In grafts, immediate incompatibility results in rapid death, but delayed incompatibility can take months or even years to manifest, creating a significant economic burden for perennial crop production. To gain insight into the genetic mechanisms underlying this phenomenon, we developed a model system using heterografting of tomato (Solanum lycopersicum) and pepper (Capsicum annuum). These grafted plants express signs of anatomical junction failure within the first week of grafting. By generating a detailed timeline for junction formation, we were able to pinpoint the cellular basis for this delayed incompatibility. Furthermore, we inferred gene regulatory networks for compatible self-grafts and incompatible heterografts based on these key anatomical events, which predict core regulators for grafting. Finally, we examined the role of vascular development in graft formation and uncovered SlWOX4 as a potential regulator of graft compatibility. Following this predicted regulator up with functional analysis, we show that Slwox4 homografts fail to form xylem bridges across the junction, demonstrating that indeed, SlWOX4 is essential for vascular reconnection during grafting, and may function as an early indicator of graft failure.}, number={1}, journal={PLANT CELL}, publisher={Oxford University Press (OUP)}, author={Thomas, Hannah and Broeck, Lisa and Spurney, Ryan and Sozzani, Rosangela and Frank, Margaret}, year={2022}, month={Jan}, pages={535–556} }
 @article{spurney_schwartz_gobble_sozzani_broeck_2021, title={Spatiotemporal Gene Expression Profiling and Network Inference: A Roadmap for Analysis, Visualization, and Key Gene Identification}, volume={2328}, ISBN={["978-1-0716-1533-1"]}, ISSN={["1940-6029"]}, DOI={10.1007/978-1-0716-1534-8_4}, abstractNote={Gene expression data analysis and the prediction of causal relationships within gene regulatory networks (GRNs) have guided the identification of key regulatory factors and unraveled the dynamic properties of biological systems. However, drawing accurate and unbiased conclusions requires a comprehensive understanding of relevant tools, computational methods, and their workflows. The topics covered in this chapter encompass the entire workflow for GRN inference including: (1) experimental design; (2) RNA sequencing data processing; (3) differentially expressed gene (DEG) selection; (4) clustering prior to inference; (5) network inference techniques; and (6) network visualization and analysis. Moreover, this chapter aims to present a workflow feasible and accessible for plant biologists without a bioinformatics or computer science background. To address this need, TuxNet, a user-friendly graphical user interface that integrates RNA sequencing data analysis with GRN inference, is chosen for the purpose of providing a detailed tutorial.}, journal={MODELING TRANSCRIPTIONAL REGULATION}, author={Spurney, Ryan and Schwartz, Michael and Gobble, Mariah and Sozzani, Rosangela and Broeck, Lisa}, year={2021}, pages={47–65} }
 @article{spurney_broeck_clark_fisher_balaguer_sozzani_2020, title={tuxnet: a simple interface to process RNA sequencing data and infer gene regulatory networks}, volume={101}, ISSN={["1365-313X"]}, url={https://doi.org/10.1111/tpj.14558}, DOI={10.1111/tpj.14558}, abstractNote={SummaryPredicting gene regulatory networks (GRNs) from expression profiles is a common approach for identifying important biological regulators. Despite the increased use of inference methods, existing computational approaches often do not integrate RNA‐sequencing data analysis, are not automated or are restricted to users with bioinformatics backgrounds. To address these limitations, we developed tuxnet, a user‐friendly platform that can process raw RNA‐sequencing data from any organism with an existing reference genome using a modified tuxedo pipeline (hisat 2 + cufflinks package) and infer GRNs from these processed data. tuxnet is implemented as a graphical user interface and can mine gene regulations, either by applying a dynamic Bayesian network (DBN) inference algorithm, genist, or a regression tree‐based pipeline, rtp‐star. We obtained time‐course expression data of a PERIANTHIA (PAN) inducible line and inferred a GRN using genist to illustrate the use of tuxnet while gaining insight into the regulations downstream of the Arabidopsis root stem cell regulator PAN. Using rtp‐star, we inferred the network of ATHB13, a downstream gene of PAN, for which we obtained wild‐type and mutant expression profiles. Additionally, we generated two networks using temporal data from developmental leaf data and spatial data from root cell‐type data to highlight the use of tuxnet to form new testable hypotheses from previously explored data. Our case studies feature the versatility of tuxnet when using different types of gene expression data to infer networks and its accessibility as a pipeline for non‐bioinformaticians to analyze transcriptome data, predict causal regulations, assess network topology and identify key regulators.}, number={3}, journal={PLANT JOURNAL}, author={Spurney, Ryan J. and Broeck, Lisa and Clark, Natalie M. and Fisher, Adam P. and Balaguer, Maria A. de Luis and Sozzani, Rosangela}, year={2020}, month={Feb}, pages={716–730} }