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Reverse enGENEering of Regulatory Networks from Big Data: A Roadmap for Biologists
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 knowl...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Libertas Academica
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4415676/ https://www.ncbi.nlm.nih.gov/pubmed/25983554 http://dx.doi.org/10.4137/BBI.S12467 |
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author | Dong, Xiaoxi Yambartsev, Anatoly Ramsey, Stephen A Thomas, Lina D Shulzhenko, Natalia Morgun, Andrey |
author_facet | Dong, Xiaoxi Yambartsev, Anatoly Ramsey, Stephen A Thomas, Lina D Shulzhenko, Natalia Morgun, Andrey |
author_sort | Dong, Xiaoxi |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4415676 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Libertas Academica |
record_format | MEDLINE/PubMed |
spelling | pubmed-44156762015-05-15 Reverse enGENEering of Regulatory Networks from Big Data: A Roadmap for Biologists Dong, Xiaoxi Yambartsev, Anatoly Ramsey, Stephen A Thomas, Lina D Shulzhenko, Natalia Morgun, Andrey Bioinform Biol Insights Review 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. Libertas Academica 2015-04-29 /pmc/articles/PMC4415676/ /pubmed/25983554 http://dx.doi.org/10.4137/BBI.S12467 Text en © 2015 the author(s), publisher and licensee Libertas Academica Ltd. This is an open-access article distributed under the terms of the Creative Commons CC-BY-NC 3.0 License. |
spellingShingle | Review Dong, Xiaoxi Yambartsev, Anatoly Ramsey, Stephen A Thomas, Lina D Shulzhenko, Natalia Morgun, Andrey Reverse enGENEering of Regulatory Networks from Big Data: A Roadmap for Biologists |
title | Reverse enGENEering of Regulatory Networks from Big Data: A Roadmap for Biologists |
title_full | Reverse enGENEering of Regulatory Networks from Big Data: A Roadmap for Biologists |
title_fullStr | Reverse enGENEering of Regulatory Networks from Big Data: A Roadmap for Biologists |
title_full_unstemmed | Reverse enGENEering of Regulatory Networks from Big Data: A Roadmap for Biologists |
title_short | Reverse enGENEering of Regulatory Networks from Big Data: A Roadmap for Biologists |
title_sort | reverse engeneering of regulatory networks from big data: a roadmap for biologists |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4415676/ https://www.ncbi.nlm.nih.gov/pubmed/25983554 http://dx.doi.org/10.4137/BBI.S12467 |
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