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Gene Regulatory Networks pp 95–109Cite as

Whole-Transcriptome Causal Network Inference with Genomic and Transcriptomic Data

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1883))

Abstract

Reconstruction of causal gene networks can distinguish regulators from targets and reduce false positives by integrating genetic variations. Its recent developments in speed and accuracy have enabled whole-transcriptome causal network inference on a personal computer. Here, we demonstrate this technique with program Findr on 3000 genes from the Geuvadis dataset. Subsequent analysis reveals major hub genes in the reconstructed network.

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Acknowledgements

Development of Findr was supported by grants from the BBSRC [BB/J004235/1, BB/M020053/1].

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Authors and Affiliations

  1. Division of Genetics and Genomics, The Roslin Institute, The University of Edinburgh, Midlothian, Scotland, UK

    Lingfei Wang & Tom Michoel

  2. Current address: Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway

    Tom Michoel

Authors
  1. Lingfei Wang
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  2. Tom Michoel
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Corresponding author

Correspondence to Lingfei Wang .

Editor information

Editors and Affiliations

  1. School of Informatics, University of Edinburgh, Edinburgh, UK

    Guido Sanguinetti

  2. Department of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium

    Vân Anh Huynh-Thu

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Wang, L., Michoel, T. (2019). Whole-Transcriptome Causal Network Inference with Genomic and Transcriptomic Data. In: Sanguinetti, G., Huynh-Thu, V. (eds) Gene Regulatory Networks. Methods in Molecular Biology, vol 1883. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8882-2_4

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  • DOI: https://doi.org/10.1007/978-1-4939-8882-2_4

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-8881-5

  • Online ISBN: 978-1-4939-8882-2

  • eBook Packages: Springer Protocols

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