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.
@article{arxiv.1411.0595,
title = {Reverse enGENEering of regulatory networks from Big Data: a guide for a biologist},
author = {Xiaoxi Dong and Anatoly Yambartsev and Stephen Ramsey and Lina Thomas and Natalia Shulzhenko and Andrey Morgun},
journal= {arXiv preprint arXiv:1411.0595},
year = {2014}
}