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A multi-omics dataset for the analysis of frontotemporal dementia genetic subtypes

by Heutink, Peter , Menden, Kevin , Fischer, Andre , Neumann, Manuela , Blauwendraat, Cornelis , Craig, David Wesley , Rodriguez-Nieto, Salvador , Bonn, Stefan , Simon-Sanchez, Javier , Kaurani, Lalit , Francescatto, Margherita , Kronenberg-Versteeg, Deborah , Atasu, Burcu , Nyima, Tenzin , Borroni, Barbara , Castillo-Lizardo, Melissa , Rizzu, Patrizia , Dhingra, Ashutosh , Fernandes, Noémia , Sadikoglou, Eldem

in 631/114/1314 / 631/114/2401 / 631/208/191 / 631/378/1689/364 / Biological analysis / Data Descriptor / Datasets / Dementia / Dementia disorders / Frontal Lobe / Frontotemporal dementia / Frontotemporal Dementia - genetics / Humanities and Social Sciences / Humans / Methylation / Molecular modelling / multidisciplinary / Multiomics / Mutation / Science / Science (multidisciplinary) / Transcriptomes

2023

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2023

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2023

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Journal Article

A multi-omics dataset for the analysis of frontotemporal dementia genetic subtypes

Heutink, Peter,

Menden, Kevin,

Fischer, Andre,

Neumann, Manuela,

Blauwendraat, Cornelis,

Craig, David Wesley,

Rodriguez-Nieto, Salvador,

Bonn, Stefan,

Simon-Sanchez, Javier,

Kaurani, Lalit,

Francescatto, Margherita,

Kronenberg-Versteeg, Deborah,

Atasu, Burcu,

Nyima, Tenzin,

Borroni, Barbara,

Castillo-Lizardo, Melissa,

Rizzu, Patrizia,

Dhingra, Ashutosh,

Fernandes, Noémia,

Sadikoglou, Eldem

2023

Overview

Understanding the molecular mechanisms underlying frontotemporal dementia (FTD) is essential for the development of successful therapies. Systematic studies on human post-mortem brain tissue of patients with genetic subtypes of FTD are currently lacking. The Risk and Modyfing Factors of Frontotemporal Dementia (RiMod-FTD) consortium therefore has generated a multi-omics dataset for genetic subtypes of FTD to identify common and distinct molecular mechanisms disturbed in disease. Here, we present multi-omics datasets generated from the frontal lobe of post-mortem human brain tissue from patients with mutations in MAPT, GRN and C9orf72 and healthy controls. This data resource consists of four datasets generated with different technologies to capture the transcriptome by RNA-seq, small RNA-seq, CAGE-seq, and methylation profiling. We show concrete examples on how to use the resulting data and confirm current knowledge about FTD and identify new processes for further investigation. This extensive multi-omics dataset holds great value to reveal new research avenues for this devastating disease.

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Publisher

Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio

Subject

/ Biological analysis

/ Data Descriptor

/ Datasets