huva: A human variation analysis framework to predict gene perturbation from population-scale multi-omics data
Summary: 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...
| Main Authors: | , |
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| Formato: | Artigo |
| Idioma: | English |
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Elsevier
2023-06-01
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| Series: | STAR Protocols |
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| Acceso en liña: | http://www.sciencedirect.com/science/article/pii/S266616672300151X |
| _version_ | 1827979753043263488 |
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| author | Anna C. Aschenbrenner Lorenzo Bonaguro |
| author_facet | Anna C. Aschenbrenner Lorenzo Bonaguro |
| author_sort | Anna C. Aschenbrenner |
| collection | DOAJ |
| description | Summary: 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 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. |
| first_indexed | 2024-04-09T21:41:43Z |
| format | Article |
| id | doaj.art-ddcfa63d85b44de1ad00b8e62e6f1390 |
| institution | Directory Open Access Journal |
| issn | 2666-1667 |
| language | English |
| last_indexed | 2024-04-09T21:41:43Z |
| publishDate | 2023-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | STAR Protocols |
| spelling | doaj.art-ddcfa63d85b44de1ad00b8e62e6f13902023-03-26T05:19:11ZengElsevierSTAR Protocols2666-16672023-06-0142102193huva: A human variation analysis framework to predict gene perturbation from population-scale multi-omics dataAnna C. Aschenbrenner0Lorenzo Bonaguro1Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, GermanySystems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany; Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany; Corresponding authorSummary: 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 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.http://www.sciencedirect.com/science/article/pii/S266616672300151XBioinformaticsRNAseqImmunologyGene ExpressionSystems Biology |
| spellingShingle | Anna C. Aschenbrenner Lorenzo Bonaguro huva: A human variation analysis framework to predict gene perturbation from population-scale multi-omics data STAR Protocols Bioinformatics RNAseq Immunology Gene Expression Systems Biology |
| title | huva: A human variation analysis framework to predict gene perturbation from population-scale multi-omics data |
| title_full | huva: A human variation analysis framework to predict gene perturbation from population-scale multi-omics data |
| title_fullStr | huva: A human variation analysis framework to predict gene perturbation from population-scale multi-omics data |
| title_full_unstemmed | huva: A human variation analysis framework to predict gene perturbation from population-scale multi-omics data |
| title_short | huva: A human variation analysis framework to predict gene perturbation from population-scale multi-omics data |
| title_sort | huva a human variation analysis framework to predict gene perturbation from population scale multi omics data |
| topic | Bioinformatics RNAseq Immunology Gene Expression Systems Biology |
| url | http://www.sciencedirect.com/science/article/pii/S266616672300151X |
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