Cargando…
huva: A human variation analysis framework to predict gene perturbation from population-scale multi-omics data
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...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10050770/ https://www.ncbi.nlm.nih.gov/pubmed/36964906 http://dx.doi.org/10.1016/j.xpro.2023.102193 |
_version_ | 1785014707284869120 |
---|---|
author | Aschenbrenner, Anna C. Bonaguro, Lorenzo |
author_facet | Aschenbrenner, Anna C. Bonaguro, Lorenzo |
author_sort | Aschenbrenner, Anna C. |
collection | PubMed |
description | 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) |
format | Online Article Text |
id | pubmed-10050770 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-100507702023-03-30 huva: A human variation analysis framework to predict gene perturbation from population-scale multi-omics data Aschenbrenner, Anna C. Bonaguro, Lorenzo STAR Protoc Protocol 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) Elsevier 2023-03-24 /pmc/articles/PMC10050770/ /pubmed/36964906 http://dx.doi.org/10.1016/j.xpro.2023.102193 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Protocol Aschenbrenner, Anna C. Bonaguro, Lorenzo huva: A human variation analysis framework to predict gene perturbation from population-scale multi-omics data |
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 | Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10050770/ https://www.ncbi.nlm.nih.gov/pubmed/36964906 http://dx.doi.org/10.1016/j.xpro.2023.102193 |
work_keys_str_mv | AT aschenbrennerannac huvaahumanvariationanalysisframeworktopredictgeneperturbationfrompopulationscalemultiomicsdata AT bonagurolorenzo huvaahumanvariationanalysisframeworktopredictgeneperturbationfrompopulationscalemultiomicsdata |