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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...

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Detalles Bibliográficos
Autores principales: Dong, Xiaoxi, Yambartsev, Anatoly, Ramsey, Stephen A, Thomas, Lina D, Shulzhenko, Natalia, Morgun, Andrey
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Libertas Academica 2015
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.
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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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