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Philos Trans R Soc Lond B Biol Sci. 2018 Jul 5;373(1750). pii: 20170222. doi: 10.1098/rstb.2017.0222.

The gene regulatory network of mESC differentiation: a benchmark for reverse engineering methods.

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Institute of Pathology, Charité Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany.
IRI Life Sciences and Institute for Theoretical Biology, Humboldt University Berlin, Philippstr. 13/Haus 18, 10115 Berlin, Germany.
Institute of Pathology, Charité Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany


A large body of data have accumulated that characterize the gene regulatory network of stem cells. Yet, a comprehensive and integrative understanding of this complex network is lacking. Network reverse engineering methods that use transcriptome data to derive these networks may help to uncover the topology in an unbiased way. Many methods exist that use co-expression to reconstruct networks. However, it remains unclear how these methods perform in the context of stem cell differentiation, as most systematic assessments have been made for regulatory networks of unicellular organisms. Here, we report a systematic benchmark of different reverse engineering methods against functional data. We show that network pruning is critical for reconstruction performance. We also find that performance is similar for algorithms that use different co-expression measures, i.e. mutual information or correlation. In addition, different methods yield very different network topologies, highlighting the challenge of interpreting these resulting networks as a whole.This article is part of the theme issue 'Designer human tissue: coming to a lab near you'.


network inference; network reconstruction; transcriptional network

[Available on 2019-07-05]

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