Versatile knowledge guided network inference method for prioritizing key regulatory factors in multi-omics data

Abstract Constantly decreasing costs of high-throughput profiling on many molecular levels generate vast amounts of multi-omics data. Studying one biomedical question on two or more omic levels provides deeper insights into underlying molecular processes or disease pathophysiology. For the majority...

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Autores principales:	Christoph Ogris, Yue Hu, Janine Arloth, Nikola S. Müller
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4ae008adfebe443dacd926946dbcc490
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spelling	oai:doaj.org-article:4ae008adfebe443dacd926946dbcc4902021-12-02T13:24:25ZVersatile knowledge guided network inference method for prioritizing key regulatory factors in multi-omics data10.1038/s41598-021-85544-42045-2322https://doaj.org/article/4ae008adfebe443dacd926946dbcc4902021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85544-4https://doaj.org/toc/2045-2322Abstract Constantly decreasing costs of high-throughput profiling on many molecular levels generate vast amounts of multi-omics data. Studying one biomedical question on two or more omic levels provides deeper insights into underlying molecular processes or disease pathophysiology. For the majority of multi-omics data projects, the data analysis is performed level-wise, followed by a combined interpretation of results. Hence the full potential of integrated data analysis is not leveraged yet, presumably due to the complexity of the data and the lacking toolsets. We propose a versatile approach, to perform a multi-level fully integrated analysis: The Knowledge guIded Multi-Omics Network inference approach, KiMONo ( https://github.com/cellmapslab/kimono ). KiMONo performs network inference by using statistical models for combining omics measurements coupled to a powerful knowledge-guided strategy exploiting prior information from existing biological sources. Within the resulting multimodal network, nodes represent features of all input types e.g. variants and genes while edges refer to knowledge-supported and statistically derived associations. In a comprehensive evaluation, we show that our method is robust to noise and exemplify the general applicability to the full spectrum of multi-omics data, demonstrating that KiMONo is a powerful approach towards leveraging the full potential of data sets for detecting biomarker candidates.Christoph OgrisYue HuJanine ArlothNikola S. MüllerNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Christoph Ogris Yue Hu Janine Arloth Nikola S. Müller Versatile knowledge guided network inference method for prioritizing key regulatory factors in multi-omics data
description	Abstract Constantly decreasing costs of high-throughput profiling on many molecular levels generate vast amounts of multi-omics data. Studying one biomedical question on two or more omic levels provides deeper insights into underlying molecular processes or disease pathophysiology. For the majority of multi-omics data projects, the data analysis is performed level-wise, followed by a combined interpretation of results. Hence the full potential of integrated data analysis is not leveraged yet, presumably due to the complexity of the data and the lacking toolsets. We propose a versatile approach, to perform a multi-level fully integrated analysis: The Knowledge guIded Multi-Omics Network inference approach, KiMONo ( https://github.com/cellmapslab/kimono ). KiMONo performs network inference by using statistical models for combining omics measurements coupled to a powerful knowledge-guided strategy exploiting prior information from existing biological sources. Within the resulting multimodal network, nodes represent features of all input types e.g. variants and genes while edges refer to knowledge-supported and statistically derived associations. In a comprehensive evaluation, we show that our method is robust to noise and exemplify the general applicability to the full spectrum of multi-omics data, demonstrating that KiMONo is a powerful approach towards leveraging the full potential of data sets for detecting biomarker candidates.
format	article
author	Christoph Ogris Yue Hu Janine Arloth Nikola S. Müller
author_facet	Christoph Ogris Yue Hu Janine Arloth Nikola S. Müller
author_sort	Christoph Ogris
title	Versatile knowledge guided network inference method for prioritizing key regulatory factors in multi-omics data
title_short	Versatile knowledge guided network inference method for prioritizing key regulatory factors in multi-omics data
title_full	Versatile knowledge guided network inference method for prioritizing key regulatory factors in multi-omics data
title_fullStr	Versatile knowledge guided network inference method for prioritizing key regulatory factors in multi-omics data
title_full_unstemmed	Versatile knowledge guided network inference method for prioritizing key regulatory factors in multi-omics data
title_sort	versatile knowledge guided network inference method for prioritizing key regulatory factors in multi-omics data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4ae008adfebe443dacd926946dbcc490
work_keys_str_mv	AT christophogris versatileknowledgeguidednetworkinferencemethodforprioritizingkeyregulatoryfactorsinmultiomicsdata AT yuehu versatileknowledgeguidednetworkinferencemethodforprioritizingkeyregulatoryfactorsinmultiomicsdata AT janinearloth versatileknowledgeguidednetworkinferencemethodforprioritizingkeyregulatoryfactorsinmultiomicsdata AT nikolasmuller versatileknowledgeguidednetworkinferencemethodforprioritizingkeyregulatoryfactorsinmultiomicsdata
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Versatile knowledge guided network inference method for prioritizing key regulatory factors in multi-omics data

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