Experimental noise cutoff boosts inferability of transcriptional networks in large-scale gene-deletion studies

Reliable inference of gene interactions from perturbation experiments remains a challenge. Here, the authors quantify the upper limits of transcriptional network inference from knockout screens, identify the key determinants of accuracy, and introduce an unbiased and scalable inference algorithm.

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Detalles Bibliográficos
Autores principales:	C. F. Blum, N. Heramvand, A. S. Khonsari, M. Kollmann
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/48ea6446771949eeae6ac10c57fbbdff
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spelling	oai:doaj.org-article:48ea6446771949eeae6ac10c57fbbdff2021-12-02T16:49:17ZExperimental noise cutoff boosts inferability of transcriptional networks in large-scale gene-deletion studies10.1038/s41467-017-02489-x2041-1723https://doaj.org/article/48ea6446771949eeae6ac10c57fbbdff2018-01-01T00:00:00Zhttps://doi.org/10.1038/s41467-017-02489-xhttps://doaj.org/toc/2041-1723Reliable inference of gene interactions from perturbation experiments remains a challenge. Here, the authors quantify the upper limits of transcriptional network inference from knockout screens, identify the key determinants of accuracy, and introduce an unbiased and scalable inference algorithm.C. F. BlumN. HeramvandA. S. KhonsariM. KollmannNature PortfolioarticleScienceQENNature Communications, Vol 9, Iss 1, Pp 1-10 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q C. F. Blum N. Heramvand A. S. Khonsari M. Kollmann Experimental noise cutoff boosts inferability of transcriptional networks in large-scale gene-deletion studies
description	Reliable inference of gene interactions from perturbation experiments remains a challenge. Here, the authors quantify the upper limits of transcriptional network inference from knockout screens, identify the key determinants of accuracy, and introduce an unbiased and scalable inference algorithm.
format	article
author	C. F. Blum N. Heramvand A. S. Khonsari M. Kollmann
author_facet	C. F. Blum N. Heramvand A. S. Khonsari M. Kollmann
author_sort	C. F. Blum
title	Experimental noise cutoff boosts inferability of transcriptional networks in large-scale gene-deletion studies
title_short	Experimental noise cutoff boosts inferability of transcriptional networks in large-scale gene-deletion studies
title_full	Experimental noise cutoff boosts inferability of transcriptional networks in large-scale gene-deletion studies
title_fullStr	Experimental noise cutoff boosts inferability of transcriptional networks in large-scale gene-deletion studies
title_full_unstemmed	Experimental noise cutoff boosts inferability of transcriptional networks in large-scale gene-deletion studies
title_sort	experimental noise cutoff boosts inferability of transcriptional networks in large-scale gene-deletion studies
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/48ea6446771949eeae6ac10c57fbbdff
work_keys_str_mv	AT cfblum experimentalnoisecutoffboostsinferabilityoftranscriptionalnetworksinlargescalegenedeletionstudies AT nheramvand experimentalnoisecutoffboostsinferabilityoftranscriptionalnetworksinlargescalegenedeletionstudies AT askhonsari experimentalnoisecutoffboostsinferabilityoftranscriptionalnetworksinlargescalegenedeletionstudies AT mkollmann experimentalnoisecutoffboostsinferabilityoftranscriptionalnetworksinlargescalegenedeletionstudies
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Experimental noise cutoff boosts inferability of transcriptional networks in large-scale gene-deletion studies

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