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PhenoExam: gene set analyses through integration of different phenotype databases

BACKGROUND: Gene set enrichment analysis (detecting phenotypic terms that emerge as significant in a set of genes) plays an important role in bioinformatics focused on diseases of genetic basis. To facilitate phenotype-oriented gene set analysis, we developed PhenoExam, a freely available R package...

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Autores principales: Cisterna, Alejandro, González-Vidal, Aurora, Ruiz, Daniel, Ortiz, Jordi, Gómez-Pascual, Alicia, Chen, Zhongbo, Nalls, Mike, Faghri, Faraz, Hardy, John, Díez, Irene, Maietta, Paolo, Álvarez, Sara, Ryten, Mina, Botía, Juan A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9805686/
https://www.ncbi.nlm.nih.gov/pubmed/36587217
http://dx.doi.org/10.1186/s12859-022-05122-x
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author Cisterna, Alejandro
González-Vidal, Aurora
Ruiz, Daniel
Ortiz, Jordi
Gómez-Pascual, Alicia
Chen, Zhongbo
Nalls, Mike
Faghri, Faraz
Hardy, John
Díez, Irene
Maietta, Paolo
Álvarez, Sara
Ryten, Mina
Botía, Juan A.
author_facet Cisterna, Alejandro
González-Vidal, Aurora
Ruiz, Daniel
Ortiz, Jordi
Gómez-Pascual, Alicia
Chen, Zhongbo
Nalls, Mike
Faghri, Faraz
Hardy, John
Díez, Irene
Maietta, Paolo
Álvarez, Sara
Ryten, Mina
Botía, Juan A.
author_sort Cisterna, Alejandro
collection PubMed
description BACKGROUND: Gene set enrichment analysis (detecting phenotypic terms that emerge as significant in a set of genes) plays an important role in bioinformatics focused on diseases of genetic basis. To facilitate phenotype-oriented gene set analysis, we developed PhenoExam, a freely available R package for tool developers and a web interface for users, which performs: (1) phenotype and disease enrichment analysis on a gene set; (2) measures statistically significant phenotype similarities between gene sets and (3) detects significant differential phenotypes or disease terms across different databases. RESULTS: PhenoExam generates sensitive and accurate phenotype enrichment analyses. It is also effective in segregating gene sets or Mendelian diseases with very similar phenotypes. We tested the tool with two similar diseases (Parkinson and dystonia), to show phenotype-level similarities but also potentially interesting differences. Moreover, we used PhenoExam to validate computationally predicted new genes potentially associated with epilepsy. CONCLUSIONS: We developed PhenoExam, a freely available R package and Web application, which performs phenotype enrichment and disease enrichment analysis on gene set G, measures statistically significant phenotype similarities between pairs of gene sets G and G′ and detects statistically significant exclusive phenotypes or disease terms, across different databases. We proved with simulations and real cases that it is useful to distinguish between gene sets or diseases with very similar phenotypes. Github R package URL is https://github.com/alexcis95/PhenoExam. Shiny App URL is https://alejandrocisterna.shinyapps.io/phenoexamweb/. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-05122-x.
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spelling pubmed-98056862023-01-02 PhenoExam: gene set analyses through integration of different phenotype databases Cisterna, Alejandro González-Vidal, Aurora Ruiz, Daniel Ortiz, Jordi Gómez-Pascual, Alicia Chen, Zhongbo Nalls, Mike Faghri, Faraz Hardy, John Díez, Irene Maietta, Paolo Álvarez, Sara Ryten, Mina Botía, Juan A. BMC Bioinformatics Software BACKGROUND: Gene set enrichment analysis (detecting phenotypic terms that emerge as significant in a set of genes) plays an important role in bioinformatics focused on diseases of genetic basis. To facilitate phenotype-oriented gene set analysis, we developed PhenoExam, a freely available R package for tool developers and a web interface for users, which performs: (1) phenotype and disease enrichment analysis on a gene set; (2) measures statistically significant phenotype similarities between gene sets and (3) detects significant differential phenotypes or disease terms across different databases. RESULTS: PhenoExam generates sensitive and accurate phenotype enrichment analyses. It is also effective in segregating gene sets or Mendelian diseases with very similar phenotypes. We tested the tool with two similar diseases (Parkinson and dystonia), to show phenotype-level similarities but also potentially interesting differences. Moreover, we used PhenoExam to validate computationally predicted new genes potentially associated with epilepsy. CONCLUSIONS: We developed PhenoExam, a freely available R package and Web application, which performs phenotype enrichment and disease enrichment analysis on gene set G, measures statistically significant phenotype similarities between pairs of gene sets G and G′ and detects statistically significant exclusive phenotypes or disease terms, across different databases. We proved with simulations and real cases that it is useful to distinguish between gene sets or diseases with very similar phenotypes. Github R package URL is https://github.com/alexcis95/PhenoExam. Shiny App URL is https://alejandrocisterna.shinyapps.io/phenoexamweb/. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-05122-x. BioMed Central 2022-12-31 /pmc/articles/PMC9805686/ /pubmed/36587217 http://dx.doi.org/10.1186/s12859-022-05122-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Software
Cisterna, Alejandro
González-Vidal, Aurora
Ruiz, Daniel
Ortiz, Jordi
Gómez-Pascual, Alicia
Chen, Zhongbo
Nalls, Mike
Faghri, Faraz
Hardy, John
Díez, Irene
Maietta, Paolo
Álvarez, Sara
Ryten, Mina
Botía, Juan A.
PhenoExam: gene set analyses through integration of different phenotype databases
title PhenoExam: gene set analyses through integration of different phenotype databases
title_full PhenoExam: gene set analyses through integration of different phenotype databases
title_fullStr PhenoExam: gene set analyses through integration of different phenotype databases
title_full_unstemmed PhenoExam: gene set analyses through integration of different phenotype databases
title_short PhenoExam: gene set analyses through integration of different phenotype databases
title_sort phenoexam: gene set analyses through integration of different phenotype databases
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9805686/
https://www.ncbi.nlm.nih.gov/pubmed/36587217
http://dx.doi.org/10.1186/s12859-022-05122-x
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