Journal Article

DZNE-2023-00409

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png huva: A human variation analysis framework to predict gene perturbation from population-scale multi-omics data.

Aschenbrenner, A. C. (First author)DZNE* ; Bonaguro, L. (Last author)DZNE*

2023
Elsevier Amsterdam

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Please use a persistent id in citations: doi:10.1016/j.xpro.2023.102193

Abstract: Variance of gene expression is intrinsic to any given natural population. Here, we present a protocol to analyze this variance using a conditional quasi loss- and gain-of-function approach. The huva (human variation) package takes advantage of population-scale multi-omics data to infer gene function and the relationship between phenotype and gene expression. We describe the steps for setting up the huva workspace, formatting datasets, performing huva experiments, and exporting data. For complete details on the use and execution of this protocol, please refer to Bonaguro et al. (2022).1.

Keyword(s): Bioinformatics ; Gene Expression ; Immunology ; RNAseq ; Systems Biology

Note: CC BY-NC-ND

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Contributing Institute(s):

1. Clinical Single Cell Omics (CSCO) / Systems Medicine (AG Schultze)
2. Aging and Immunity (AG Aschenbrenner)

Research Program(s):

1. 352 - Disease Mechanisms (POF4-352) (POF4-352)
2. 354 - Disease Prevention and Healthy Aging (POF4-354) (POF4-354)

Appears in the scientific report 2023

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Database coverage:
; ; ; ; Article Processing Charges ; DOAJ Seal ; Fees ; PubMed Central ; SCOPUS

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Linked articles:

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Journal Article Bonaguro, L. (First author)DZNE* ; Schulte-Schrepping, J.DZNE* ; Carraro, C.DZNE* ; Sun, L. L. ; Reiz, B. ; Gemünd, I.DZNE* ; Saglam, A.DZNE* ; Rahmouni, S. ; Georges, M. ; Arts, P. ; Hoischen, A. ; Joosten, L. A. B. ; van de Veerdonk, F. L. ; Netea, M. G. ; Händler, K.DZNE* ; Mukherjee, S.DZNE* ; Ulas, T.DZNE* ; Schultze, J. L.DZNE* ; Aschenbrenner, A. C. (Last author)DZNE*
Human variation in population-wide gene expression data predicts gene perturbation phenotype.
iScience 25(11), 105328 (2022) [10.1016/j.isci.2022.105328]2022   Files  Fulltext by Pubmed Central BibTeX | EndNote: XML, Text | RIS

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