Data- and knowledge-based modeling of gene regulatory networks: an update
Gene regulatory network inference is a systems biology approach which predicts interactions between genes with the help of high-throughput data. In this review, we present current and updated network inference methods focusing on novel techniques for data acquisition, network inference assessment, n...
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Published in | EXCLI journal Vol. 14; pp. 346 - 378 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Germany
Leibniz Research Centre for Working Environment and Human Factors
01.01.2015
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Abstract | Gene regulatory network inference is a systems biology approach which predicts interactions between genes with the help of high-throughput data. In this review, we present current and updated network inference methods focusing on novel techniques for data acquisition, network inference assessment, network inference for interacting species and the integration of prior knowledge. After the advance of Next-Generation-Sequencing of cDNAs derived from RNA samples (RNA-Seq) we discuss in detail its application to network inference. Furthermore, we present progress for large-scale or even full-genomic network inference as well as for small-scale condensed network inference and review advances in the evaluation of network inference methods by crowdsourcing. Finally, we reflect the current availability of data and prior knowledge sources and give an outlook for the inference of gene regulatory networks that reflect interacting species, in particular pathogen-host interactions. |
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AbstractList | Gene regulatory network inference is a systems biology approach which predicts interactions between genes with the help of high-throughput data. In this review, we present current and updated network inference methods focusing on novel techniques for data acquisition, network inference assessment, network inference for interacting species and the integration of prior knowledge. After the advance of Next-Generation-Sequencing of cDNAs derived from RNA samples (RNA-Seq) we discuss in detail its application to network inference. Furthermore, we present progress for large-scale or even full-genomic network inference as well as for small-scale condensed network inference and review advances in the evaluation of network inference methods by crowdsourcing. Finally, we reflect the current availability of data and prior knowledge sources and give an outlook for the inference of gene regulatory networks that reflect interacting species, in particular pathogen-host interactions. |
Author | Schulze, Sylvie Linde, Jörg Henkel, Sebastian G Guthke, Reinhard |
AuthorAffiliation | 1 Research Group Systems Biology / Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute, Beutenbergstr. 11a, 07745 Jena, Germany 2 BioControl Jena GmbH, Wildenbruchstr. 15, 07745 Jena, Germany |
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Author_xml | – sequence: 1 givenname: Jörg surname: Linde fullname: Linde, Jörg organization: Research Group Systems Biology / Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute, Beutenbergstr. 11a, 07745 Jena, Germany – sequence: 2 givenname: Sylvie surname: Schulze fullname: Schulze, Sylvie organization: Research Group Systems Biology / Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute, Beutenbergstr. 11a, 07745 Jena, Germany – sequence: 3 givenname: Sebastian G surname: Henkel fullname: Henkel, Sebastian G organization: BioControl Jena GmbH, Wildenbruchstr. 15, 07745 Jena, Germany – sequence: 4 givenname: Reinhard surname: Guthke fullname: Guthke, Reinhard organization: Research Group Systems Biology / Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute, Beutenbergstr. 11a, 07745 Jena, Germany |
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Copyright | Copyright © 2015 Linde et al. 2015 |
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Keywords | reverse engineering network inference gene regulatory networks modeling RNA-Seq prior knowledge |
Language | English |
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