De-novo learning of genome-scale regulatory networks in S. cerevisiae.

De-novo reverse-engineering of genome-scale regulatory networks is a fundamental problem of biological and translational research. One of the major obstacles in developing and evaluating approaches for de-novo gene network reconstruction is the absence of high-quality genome-scale gold-standard netw...

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Autores principales:	Sisi Ma, Patrick Kemmeren, David Gresham, Alexander Statnikov
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/238e6d087d424726902f8306681c7224
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spelling	oai:doaj.org-article:238e6d087d424726902f8306681c72242021-11-25T06:00:46ZDe-novo learning of genome-scale regulatory networks in S. cerevisiae.1932-620310.1371/journal.pone.0106479https://doaj.org/article/238e6d087d424726902f8306681c72242014-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0106479https://doaj.org/toc/1932-6203De-novo reverse-engineering of genome-scale regulatory networks is a fundamental problem of biological and translational research. One of the major obstacles in developing and evaluating approaches for de-novo gene network reconstruction is the absence of high-quality genome-scale gold-standard networks of direct regulatory interactions. To establish a foundation for assessing the accuracy of de-novo gene network reverse-engineering, we constructed high-quality genome-scale gold-standard networks of direct regulatory interactions in Saccharomyces cerevisiae that incorporate binding and gene knockout data. Then we used 7 performance metrics to assess accuracy of 18 statistical association-based approaches for de-novo network reverse-engineering in 13 different datasets spanning over 4 data types. We found that most reconstructed networks had statistically significant accuracies. We also determined which statistical approaches and datasets/data types lead to networks with better reconstruction accuracies. While we found that de-novo reverse-engineering of the entire network is a challenging problem, it is possible to reconstruct sub-networks around some transcription factors with good accuracy. The latter transcription factors can be identified by assessing their connectivity in the inferred networks. Overall, this study provides the gene network reverse-engineering community with a rigorous assessment of the accuracy of S. cerevisiae gene network reconstruction and variability in performance of various approaches for learning both the entire network and sub-networks around transcription factors.Sisi MaPatrick KemmerenDavid GreshamAlexander StatnikovPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 9, p e106479 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sisi Ma Patrick Kemmeren David Gresham Alexander Statnikov De-novo learning of genome-scale regulatory networks in S. cerevisiae.
description	De-novo reverse-engineering of genome-scale regulatory networks is a fundamental problem of biological and translational research. One of the major obstacles in developing and evaluating approaches for de-novo gene network reconstruction is the absence of high-quality genome-scale gold-standard networks of direct regulatory interactions. To establish a foundation for assessing the accuracy of de-novo gene network reverse-engineering, we constructed high-quality genome-scale gold-standard networks of direct regulatory interactions in Saccharomyces cerevisiae that incorporate binding and gene knockout data. Then we used 7 performance metrics to assess accuracy of 18 statistical association-based approaches for de-novo network reverse-engineering in 13 different datasets spanning over 4 data types. We found that most reconstructed networks had statistically significant accuracies. We also determined which statistical approaches and datasets/data types lead to networks with better reconstruction accuracies. While we found that de-novo reverse-engineering of the entire network is a challenging problem, it is possible to reconstruct sub-networks around some transcription factors with good accuracy. The latter transcription factors can be identified by assessing their connectivity in the inferred networks. Overall, this study provides the gene network reverse-engineering community with a rigorous assessment of the accuracy of S. cerevisiae gene network reconstruction and variability in performance of various approaches for learning both the entire network and sub-networks around transcription factors.
format	article
author	Sisi Ma Patrick Kemmeren David Gresham Alexander Statnikov
author_facet	Sisi Ma Patrick Kemmeren David Gresham Alexander Statnikov
author_sort	Sisi Ma
title	De-novo learning of genome-scale regulatory networks in S. cerevisiae.
title_short	De-novo learning of genome-scale regulatory networks in S. cerevisiae.
title_full	De-novo learning of genome-scale regulatory networks in S. cerevisiae.
title_fullStr	De-novo learning of genome-scale regulatory networks in S. cerevisiae.
title_full_unstemmed	De-novo learning of genome-scale regulatory networks in S. cerevisiae.
title_sort	de-novo learning of genome-scale regulatory networks in s. cerevisiae.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/238e6d087d424726902f8306681c7224
work_keys_str_mv	AT sisima denovolearningofgenomescaleregulatorynetworksinscerevisiae AT patrickkemmeren denovolearningofgenomescaleregulatorynetworksinscerevisiae AT davidgresham denovolearningofgenomescaleregulatorynetworksinscerevisiae AT alexanderstatnikov denovolearningofgenomescaleregulatorynetworksinscerevisiae
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De-novo learning of genome-scale regulatory networks in S. cerevisiae.

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