Home > Publications Database > huva: A human variation analysis framework to predict gene perturbation from population-scale multi-omics data. > print

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024 7 _ |a 10.1016/j.xpro.2023.102193
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100 1 _ |a Aschenbrenner, Anna C
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245 _ _ |a huva: A human variation analysis framework to predict gene perturbation from population-scale multi-omics data.
260 _ _ |a Amsterdam
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520 _ _ |a 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.
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650 _ 7 |a Bioinformatics
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650 _ 7 |a Gene Expression
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650 _ 7 |a Immunology
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650 _ 7 |a RNAseq
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650 _ 7 |a Systems Biology
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700 1 _ |a Bonaguro, Lorenzo
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773 _ _ |a 10.1016/j.xpro.2023.102193
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787 0 _ |a Bonaguro, Lorenzo et.al.
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|t Human variation in population-wide gene expression data predicts gene perturbation phenotype.
856 4 _ |u https://pub.dzne.de/record/257340/files/DZNE-2023-00409.pdf
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