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

Thorne, T. (2018) Approximate inference of gene regulatory network models from RNA-Seq time series data. BMC Bioinformatics, 19. 127. ISSN 1471-2105

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To link to this item DOI: 10.1186/s12859-018-2125-2

Abstract/Summary

Inference of gene regulatory network structures from RNA-Seq data is challenging due to the nature of the data, as measurements take the form of counts of reads mapped to a given gene. Here we present a model for RNA-Seq time series data that applies a negative binomial distribution for the observations, and uses sparse regression with a horseshoe prior to learn a dynamic Bayesian network of interactions between genes. We use a variational inference scheme to learn approximate posterior distributions for the model parameters. The methodology is benchmarked on synthetic data designed to replicate the distribution of real world RNA-Seq data. We compare our method to other sparse regression approaches and information theoretic methods. We demonstrate an application of our method to a publicly available human neuronal stem cell differentiation RNA-Seq time series.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:76194
Publisher:BioMed Central

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