Gene Expression Analysis Platform (GEAP) : a highly customizable, fast, versatile and ready-to-use microarray analysis platform

Detalhes bibliográficos
Autor(a) principal: Nunes, Itamar José Guimarães
Data de Publicação: 2022
Outros Autores: Recamonde-Mendoza, Mariana, Feltes, Bruno César
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFRGS
Texto Completo: http://hdl.handle.net/10183/279837
Resumo: There are still numerous challenges to be overcome in microarray data analysis because advanced, state-of-the-art analyses are restricted to programming users. Here we present the Gene Expression Analysis Platform, a versatile, customizable, optimized, and portable software developed for microarray analysis. GEAP was developed in C# for the graphical user interface, data querying, storage, results filtering and dynamic plotting, and R for data processing, quality analysis, and differential expression. Through a new automated system that identifies microarray file formats, retrieves contents, detects file corruption, and solves dependencies, GEAP deals with datasets independently of platform. GEAP covers 32 statistical options, supports quality assessment, differential expression from single and dual-channel experiments, and gene ontology. Users can explore results by different plots and filtering options. Finally, the entire data can be saved and organized through storage features, optimized for memory and data retrieval, with faster performance than R. These features, along with other new options, are not yet present in any microarray analysis software. GEAP accomplishes data analysis in a faster, straightforward, and friendlier way than other similar software, while keeping the flexibility for sophisticated procedures. By developing optimizations, unique customizations and new features, GEAP is destined for both advanced and non-programming users.
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spelling Nunes, Itamar José GuimarãesRecamonde-Mendoza, MarianaFeltes, Bruno César2024-10-10T06:48:51Z20221415-4757http://hdl.handle.net/10183/279837001147579There are still numerous challenges to be overcome in microarray data analysis because advanced, state-of-the-art analyses are restricted to programming users. Here we present the Gene Expression Analysis Platform, a versatile, customizable, optimized, and portable software developed for microarray analysis. GEAP was developed in C# for the graphical user interface, data querying, storage, results filtering and dynamic plotting, and R for data processing, quality analysis, and differential expression. Through a new automated system that identifies microarray file formats, retrieves contents, detects file corruption, and solves dependencies, GEAP deals with datasets independently of platform. GEAP covers 32 statistical options, supports quality assessment, differential expression from single and dual-channel experiments, and gene ontology. Users can explore results by different plots and filtering options. Finally, the entire data can be saved and organized through storage features, optimized for memory and data retrieval, with faster performance than R. These features, along with other new options, are not yet present in any microarray analysis software. GEAP accomplishes data analysis in a faster, straightforward, and friendlier way than other similar software, while keeping the flexibility for sophisticated procedures. By developing optimizations, unique customizations and new features, GEAP is destined for both advanced and non-programming users.application/pdfengGenetics and molecular biology. Vol. 45, no. 1 (2022), 15 p.SoftwareAnálise de dadosExpressão gênicaPesquisa biomédicaMicroarrayGene expressionBiomedical researchGene Expression Analysis Platform (GEAP) : a highly customizable, fast, versatile and ready-to-use microarray analysis platformEstrangeiroinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSTEXT001147579.pdf.txt001147579.pdf.txtExtracted Texttext/plain58074http://www.lume.ufrgs.br/bitstream/10183/279837/2/001147579.pdf.txtec122dac3755435e28f01dbdafb1fc74MD52ORIGINAL001147579.pdfTexto completo (inglês)application/pdf2532256http://www.lume.ufrgs.br/bitstream/10183/279837/1/001147579.pdf7aaa02d734c579d6744a2a89a7fe6b88MD5110183/2798372024-10-11 06:46:59.878735oai:www.lume.ufrgs.br:10183/279837Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2024-10-11T09:46:59Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false
dc.title.pt_BR.fl_str_mv Gene Expression Analysis Platform (GEAP) : a highly customizable, fast, versatile and ready-to-use microarray analysis platform
title Gene Expression Analysis Platform (GEAP) : a highly customizable, fast, versatile and ready-to-use microarray analysis platform
spellingShingle Gene Expression Analysis Platform (GEAP) : a highly customizable, fast, versatile and ready-to-use microarray analysis platform
Nunes, Itamar José Guimarães
Software
Análise de dados
Expressão gênica
Pesquisa biomédica
Microarray
Gene expression
Biomedical research
title_short Gene Expression Analysis Platform (GEAP) : a highly customizable, fast, versatile and ready-to-use microarray analysis platform
title_full Gene Expression Analysis Platform (GEAP) : a highly customizable, fast, versatile and ready-to-use microarray analysis platform
title_fullStr Gene Expression Analysis Platform (GEAP) : a highly customizable, fast, versatile and ready-to-use microarray analysis platform
title_full_unstemmed Gene Expression Analysis Platform (GEAP) : a highly customizable, fast, versatile and ready-to-use microarray analysis platform
title_sort Gene Expression Analysis Platform (GEAP) : a highly customizable, fast, versatile and ready-to-use microarray analysis platform
author Nunes, Itamar José Guimarães
author_facet Nunes, Itamar José Guimarães
Recamonde-Mendoza, Mariana
Feltes, Bruno César
author_role author
author2 Recamonde-Mendoza, Mariana
Feltes, Bruno César
author2_role author
author
dc.contributor.author.fl_str_mv Nunes, Itamar José Guimarães
Recamonde-Mendoza, Mariana
Feltes, Bruno César
dc.subject.por.fl_str_mv Software
Análise de dados
Expressão gênica
Pesquisa biomédica
topic Software
Análise de dados
Expressão gênica
Pesquisa biomédica
Microarray
Gene expression
Biomedical research
dc.subject.eng.fl_str_mv Microarray
Gene expression
Biomedical research
description There are still numerous challenges to be overcome in microarray data analysis because advanced, state-of-the-art analyses are restricted to programming users. Here we present the Gene Expression Analysis Platform, a versatile, customizable, optimized, and portable software developed for microarray analysis. GEAP was developed in C# for the graphical user interface, data querying, storage, results filtering and dynamic plotting, and R for data processing, quality analysis, and differential expression. Through a new automated system that identifies microarray file formats, retrieves contents, detects file corruption, and solves dependencies, GEAP deals with datasets independently of platform. GEAP covers 32 statistical options, supports quality assessment, differential expression from single and dual-channel experiments, and gene ontology. Users can explore results by different plots and filtering options. Finally, the entire data can be saved and organized through storage features, optimized for memory and data retrieval, with faster performance than R. These features, along with other new options, are not yet present in any microarray analysis software. GEAP accomplishes data analysis in a faster, straightforward, and friendlier way than other similar software, while keeping the flexibility for sophisticated procedures. By developing optimizations, unique customizations and new features, GEAP is destined for both advanced and non-programming users.
publishDate 2022
dc.date.issued.fl_str_mv 2022
dc.date.accessioned.fl_str_mv 2024-10-10T06:48:51Z
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dc.relation.ispartof.pt_BR.fl_str_mv Genetics and molecular biology. Vol. 45, no. 1 (2022), 15 p.
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