huva: A human variation analysis framework to predict gene perturbation from population-scale multi-omics data

Anna C Aschenbrenner; Lorenzo Bonaguro

doi:10.1016/j.xpro.2023.102193

huva: A human variation analysis framework to predict gene perturbation from population-scale multi-omics data

STAR Protoc. 2023 Mar 24;4(2):102193. doi: 10.1016/j.xpro.2023.102193. Online ahead of print.

Authors

Anna C Aschenbrenner¹, Lorenzo Bonaguro²

Affiliations

¹ Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany.
² Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany; Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany. Electronic address: lorenzobonaguro@uni-bonn.de.

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.

Keywords: Bioinformatics; Gene Expression; Immunology; RNAseq; Systems Biology.