From time-series transcriptomics to gene regulatory networks: a review on inference methods

TL;DR

This review summarizes various inference algorithms for constructing gene regulatory networks from time-series transcriptomics data, highlighting their applications and guiding researchers in selecting suitable methods.

Contribution

It provides an updated overview of inference tools based on time-series data, aiding biologists and researchers in method selection.

Findings

Summarizes main types of inference algorithms

Highlights applications of gene regulatory networks

Guides method selection for researchers

Abstract

Inference of gene regulatory networks has been an active area of research for around 20 years, leading to the development of sophisticated inference algorithms based on a variety of assumptions and approaches. With the always increasing demand for more accurate and powerful models, the inference problem remains of broad scientific interest. The abstract representation of biological systems through gene regulatory networks represents a powerful method to study such systems, encoding different amounts and types of information. In this review, we summarize the different types of inference algorithms specifically based on time-series transcriptomics, giving an overview of the main applications of gene regulatory networks in computational biology. This review is intended to give an updated overview of regulatory networks inference tools to biologists and researchers new to the topic and guide them in selecting the appropriate inference method that best fits their questions, aims and experimental data.