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Approximate inference of gene regulatorynetwork models from RNA-Seq time series data

Thorne, Tom (2018) Approximate inference of gene regulatorynetwork models from RNA-Seq time series data BMC Bioinformatics, 19 (127).

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Background: Inference of gene regulatory network structures from RNA-Seq data is challenging due to the natureof the data, as measurements take the form of counts of reads mapped to a given gene. Here we present a model forRNA-Seq time series data that applies a negative binomial distribution for the observations, and uses sparse regressionwith a horseshoe prior to learn a dynamic Bayesian network of interactions between genes. We use a variationalinference scheme to learn approximate posterior distributions for the model parameters. Results: The methodology is benchmarked on synthetic data designed to replicate the distribution of real worldRNA-Seq data. We compare our method to other sparse regression approaches and find improved performance inlearning directed networks. We demonstrate an application of our method to a publicly available human neuronalstem cell differentiation RNA-Seq time series data set to infer the underlying network structure. Conclusions: Our method is able to improve performance on synthetic data by explicitly modelling the statisticaldistribution of the data when learning networks from RNA-Seq time series. Applying approximate inferencetechniques we can learn network structures quickly with only moderate computing resources.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computer Science
Authors :
Date : 11 April 2018
DOI : 10.1186/s12859-018-2125-2
Copyright Disclaimer : © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.
Depositing User : James Marshall
Date Deposited : 17 Jun 2020 09:18
Last Modified : 17 Jun 2020 09:18

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