tuxnet: a simple interface to process RNA sequencing data and infer gene regulatory networks
| Autor: | Natalie M. Clark, Ryan J. Spurney, Maria A. de Luis Balaguer, Lisa Van den Broeck, Adam P Fisher, Rosangela Sozzani |
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| Rok vydání: | 2019 |
| Předmět: |
0106 biological sciences
0301 basic medicine Gene regulatory network Arabidopsis Inference Plant Science Computational biology Biology Network topology 01 natural sciences 03 medical and health sciences Gene Expression Regulation Plant Genetics Gene Regulatory Networks Spatial analysis Dynamic Bayesian network Sequence Analysis RNA Computational Biology Bayes Theorem Cell Biology Pipeline (software) Temporal database 030104 developmental biology Transcriptome Algorithms Genome Plant 010606 plant biology & botany Reference genome |
| Zdroj: | The Plant journal : for cell and molecular biologyReferences. 101(3) |
| ISSN: | 1365-313X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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