Title | Reverse enGENEering of Regulatory Networks from Big Data: A Roadmap for Biologists. |
Publication Type | Journal Article |
Year of Publication | 2015 |
Authors | Dong, X, Yambartsev, A, Ramsey, SA, Thomas, LD, Shulzhenko, N, Morgun, A |
Journal | Bioinform Biol Insights |
Volume | 9 |
Pagination | 61-74 |
Date Published | 2015 |
ISSN | 1177-9322 |
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 these data into biological knowledge, for example, how to use these 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. Here 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. |
DOI | 10.4137/BBI.S12467 |
Alternate Journal | Bioinform Biol Insights |
PubMed ID | 25983554 |
PubMed Central ID | PMC4415676 |
Grant List | K25 HL098807 / HL / NHLBI NIH HHS / United States R21 AI107485 / AI / NIAID NIH HHS / United States U01 AI109695 / AI / NIAID NIH HHS / United States |