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

## TL;DR

This paper provides a guide for biologists on using network analysis to understand biological systems from large datasets.

## Contribution

The paper introduces a step-by-step guide and software tools for network analysis in biological research.

## Key findings

- Network analysis can help identify functional pathways in cell differentiation.
- The guide includes practical steps and software for network reconstruction and interrogation.
- The approach aids in determining gene targets for diseases like cancer.

## 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.