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@ARTICLE{Aschenbrenner:257340,
      author       = {Aschenbrenner, Anna C and Bonaguro, Lorenzo},
      title        = {huva: {A} human variation analysis framework to predict
                      gene perturbation from population-scale multi-omics data.},
      journal      = {STAR Protocols},
      volume       = {4},
      number       = {2},
      issn         = {2666-1667},
      address      = {Amsterdam},
      publisher    = {Elsevier},
      reportid     = {DZNE-2023-00409},
      pages        = {102193},
      year         = {2023},
      note         = {CC BY-NC-ND},
      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 (Other) / Gene Expression (Other) /
                      Immunology (Other) / RNAseq (Other) / Systems Biology
                      (Other)},
      cin          = {AG Schultze / AG Aschenbrenner},
      ddc          = {600},
      cid          = {I:(DE-2719)1013038 / I:(DE-2719)5000082},
      pnm          = {352 - Disease Mechanisms (POF4-352) / 354 - Disease
                      Prevention and Healthy Aging (POF4-354)},
      pid          = {G:(DE-HGF)POF4-352 / G:(DE-HGF)POF4-354},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:36964906},
      pmc          = {pmc:PMC10050770},
      doi          = {10.1016/j.xpro.2023.102193},
      url          = {https://pub.dzne.de/record/257340},
}