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000257340 037__ $$aDZNE-2023-00409
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000257340 1001_ $$0P:(DE-2719)2812913$$aAschenbrenner, Anna C$$b0$$eFirst author$$udzne
000257340 245__ $$ahuva: A human variation analysis framework to predict gene perturbation from population-scale multi-omics data.
000257340 260__ $$aAmsterdam$$bElsevier$$c2023
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000257340 520__ $$aVariance of gene expression is intrinsic to any given natural population. Here, we present a protocol to analyze this variance using a conditional quasi loss- and gain-of-function approach. The huva (human variation) package takes advantage of population-scale multi-omics data to infer gene function and the relationship between phenotype and gene expression. We describe the steps for setting up the huva workspace, formatting datasets, performing huva experiments, and exporting data. For complete details on the use and execution of this protocol, please refer to Bonaguro et al. (2022).1.
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000257340 650_7 $$2Other$$aBioinformatics
000257340 650_7 $$2Other$$aGene Expression
000257340 650_7 $$2Other$$aImmunology
000257340 650_7 $$2Other$$aRNAseq
000257340 650_7 $$2Other$$aSystems Biology
000257340 7001_ $$0P:(DE-2719)9001512$$aBonaguro, Lorenzo$$b1$$eLast author$$udzne
000257340 773__ $$0PERI:(DE-600)3053335-1$$a10.1016/j.xpro.2023.102193$$gVol. 4, no. 2, p. 102193 -$$n2$$p102193$$tSTAR Protocols$$v4$$x2666-1667$$y2023
000257340 7870_ $$0DZNE-2022-01665$$aBonaguro, Lorenzo et.al.$$dSt. Louis : Elsevier, 2022$$iRelatedTo$$r$$tHuman variation in population-wide gene expression data predicts gene perturbation phenotype.
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