Tuxnet: a simple interface to process RNA sequencing data and infer gene regulatory networks. (27th November 2019)

• Record Type: Journal Article
• Authors: Spurney, Ryan J.; Van den Broeck, Lisa; Clark, Natalie M.; Fisher, Adam P.; de Luis Balaguer, Maria A.; Sozzani, Rosangela
• Is Part Of: Plant journal. Volume 101:Number 3(2020)
• Page Start: 716
• Page End: 730
• Publication Date: 2019-11-27
• Subjects: Arabidopsis thaliana -- gene regulatory network inference -- graphical user interface -- RNA sequencing processing -- stem cell maintenance -- technical advance; Plant molecular biology -- Periodicals; Plant cells and tissues -- Periodicals; Botany -- Periodicals; 580
• DOI: 10.1111/tpj.14558
• Languages: English
• ISSNs: 0960-7412

Abstract

Summary: Predicting 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. Significance Statement: tuxnet offers a simple integrated interface for both computational and non‐computational biologists to perform RNA‐seq data analysis and infer GRNs from RNA‐seq data (https://rspurney.github.io/TuxNet/ ). By implementing network inference techniques, tuxnet allows for the prediction of causal regulations with high confidence and thus is a practical tool to evaluate and handle transcriptome data.