Reverse enGENEering of regulatory networks from Big Data: a guide for a biologist

TL;DR

This paper provides a comprehensive guide for biologists on how to reconstruct and analyze biological networks from Big Data using omics technologies, including practical steps and software tools.

Contribution

It offers a step-by-step methodology and software recommendations for network analysis tailored for biological questions from omics Big Data.

Findings

Network analysis helps identify functional pathways involved in cell differentiation.

Guidelines improve the reproducibility of biological network reconstruction.

Software tools facilitate the application of network analysis in biological research.

Abstract

Omics technologies enable unbiased investigation of biological systems through massively parallel sequence acquisition or molecular measurements, bringing the life sciences into the era of Big Data. A central challenge posed by such omics datasets is how to transform this data into biological knowledge. For example, how to use this data to answer questions such as: which functional pathways are involved in cell differentiation? Which genes should we target to stop cancer? Network analysis is a powerful and general approach to solve this problem consisting of two fundamental stages, network reconstruction and network interrogation. Herein, we provide an overview of network analysis including a step by step guide on how to perform and use this approach to investigate a biological question. In this guide, we also include the software packages that we and others employ for each of the steps of a network analysis workflow.