Format

Send to

Choose Destination
BMC Bioinformatics. 2018 Apr 11;19(1):127. doi: 10.1186/s12859-018-2125-2.

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

Author information

1
Department of Computer Science, University of Reading, Reading, UK. t.thorne@reading.ac.uk.

Abstract

BACKGROUND:

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.

RESULTS:

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 find improved performance in learning directed networks. We demonstrate an application of our method to a publicly available human neuronal stem 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 statistical distribution of the data when learning networks from RNA-Seq time series. Applying approximate inference techniques we can learn network structures quickly with only moderate computing resources.

PMID:
29642837
PMCID:
PMC5896118
DOI:
10.1186/s12859-018-2125-2
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for BioMed Central Icon for PubMed Central
Loading ...
Support Center