tuxnet: a simple interface to process RNA sequencing data and infer gene regulatory networks

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.

Analysis of Spatio-Temporal Transcriptome Profiles of Soybean (Glycine max) Tissues during Early Seed Development

“…GRNs are a useful tool to better understand the relationships between regulators and their targets. In this study, we inferred the GRN for each seed tissue separately (eight networks for each tissue) using genist algorithm from TUXNET [ 61 ] and then combined them to generate the final network. To discover the tissue-specific regulatory relationship among the key regulators for each network, genes from two sets were used for network inference.…”

TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data

“…To address intervention cause, we need to perform a sequence of established steps to pre-process the RNA-seq data, by performing quality control, alignment, read count calculation, filtering, normalization and correction. Several pipeline methods codify these steps to enable users to subsequently perform DE analysis and summarize uncovered regulatory mechanisms (Afgan et al, 2018;Torre et al, 2018;Ge et al, 2018;Cornwell et al, 2018;de Jong et al, 2015;Jensen et al, 2017;Kartashov and Barski, 2015;Spurney et al, 2020). However, there are many possible methods to choose from at each step in this process (STAR Methods), not all experimental designs are the same, and downstream results heavily depend on how the RNA-seq data are processed.…”

“…Also, the majority of platforms do not use multiple data-types (iDEP (Ge et al, 2018); T-REx (de Jong et al, 2015)), even though we know that RNA-seq does not provide direct evidence of gene interaction. Other pipeline methods used to process such RNA-seq data have to be pieced together such as Galaxy (Afgan et al, 2018) and do not use a time series DE model for time series data such as TuxNet (Spurney et al, 2020) thus disregarding time dependencies. Figure S1 provides an overview of the available methods.…”

“…Importantly, time series DE methods algorithms are still in their infancy and are underutilized (Spies et al, 2019). The few DE methods that do consider temporal dynamics are not yet part of pipeline methods to infer GRNs and are therefore incomplete (Fischer et al, 2018;Jensen et al, 2017;Michna et al, 2016;Nueda et al, 2014;Spurney et al, 2020;Wani and Raza, 2019). For time series DE methods that do exist, some can only analyze specific experimental designs or require normalized and corrected data on input (Sahraeian et al, 2017;Spies and Ciaudo, 2015;Spies et al, 2019).…”

“…We compared these methods in terms of 14 features ( Figure S1). Some of these methods analyze RNA-seq data from raw .fastq files and perform gene ontology, claim to be able to analyze time series RNA-seq data, and/or report gene regulatory networks (Afgan et al, 2018;Torre et al, 2018;Ge et al, 2018;Cornwell et al, 2018;de Jong et al, 2015;Jensen et al, 2017;Kartashov and Barski, 2015;Spurney et al, 2020). TIMEOR fills a gap by providing at least twice as many features to uncover gene regulatory mechanisms from time series data compared to existing methods.…”