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Flame (v2.0): advanced integration and interpretation of functional enrichment results from multiple sources

 : Summary: Functional enrichment is the process of identifying implicated functional terms from a given input list of genes or proteins. In this article, we present Flame (v2.0), a web tool which offers a combinatorial approach through merging and visualizing results from widely used functional enr...

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Autores principales: Karatzas, Evangelos, Baltoumas, Fotis A, Aplakidou, Eleni, Kontou, Panagiota I, Stathopoulos, Panos, Stefanis, Leonidas, Bagos, Pantelis G, Pavlopoulos, Georgios A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10423032/
https://www.ncbi.nlm.nih.gov/pubmed/37540207
http://dx.doi.org/10.1093/bioinformatics/btad490
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author Karatzas, Evangelos
Baltoumas, Fotis A
Aplakidou, Eleni
Kontou, Panagiota I
Stathopoulos, Panos
Stefanis, Leonidas
Bagos, Pantelis G
Pavlopoulos, Georgios A
author_facet Karatzas, Evangelos
Baltoumas, Fotis A
Aplakidou, Eleni
Kontou, Panagiota I
Stathopoulos, Panos
Stefanis, Leonidas
Bagos, Pantelis G
Pavlopoulos, Georgios A
author_sort Karatzas, Evangelos
collection PubMed
description  : Summary: Functional enrichment is the process of identifying implicated functional terms from a given input list of genes or proteins. In this article, we present Flame (v2.0), a web tool which offers a combinatorial approach through merging and visualizing results from widely used functional enrichment applications while also allowing various flexible input options. In this version, Flame utilizes the aGOtool, g: Profiler, WebGestalt, and Enrichr pipelines and presents their outputs separately or in combination following a visual analytics approach. For intuitive representations and easier interpretation, it uses interactive plots such as parameterizable networks, heatmaps, barcharts, and scatter plots. Users can also: (i) handle multiple protein/gene lists and analyse union and intersection sets simultaneously through interactive UpSet plots, (ii) automatically extract genes and proteins from free text through text-mining and Named Entity Recognition (NER) techniques, (iii) upload single nucleotide polymorphisms (SNPs) and extract their relative genes, or (iv) analyse multiple lists of differentially expressed proteins/genes after selecting them interactively from a parameterizable volcano plot. Compared to the previous version of 197 supported organisms, Flame (v2.0) currently allows enrichment for 14 436 organisms. AVAILABILITY AND IMPLEMENTATION: Web Application: http://flame.pavlopouloslab.info. Code: https://github.com/PavlopoulosLab/Flame. Docker: https://hub.docker.com/r/pavlopouloslab/flame.
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spelling pubmed-104230322023-08-13 Flame (v2.0): advanced integration and interpretation of functional enrichment results from multiple sources Karatzas, Evangelos Baltoumas, Fotis A Aplakidou, Eleni Kontou, Panagiota I Stathopoulos, Panos Stefanis, Leonidas Bagos, Pantelis G Pavlopoulos, Georgios A Bioinformatics Original Paper  : Summary: Functional enrichment is the process of identifying implicated functional terms from a given input list of genes or proteins. In this article, we present Flame (v2.0), a web tool which offers a combinatorial approach through merging and visualizing results from widely used functional enrichment applications while also allowing various flexible input options. In this version, Flame utilizes the aGOtool, g: Profiler, WebGestalt, and Enrichr pipelines and presents their outputs separately or in combination following a visual analytics approach. For intuitive representations and easier interpretation, it uses interactive plots such as parameterizable networks, heatmaps, barcharts, and scatter plots. Users can also: (i) handle multiple protein/gene lists and analyse union and intersection sets simultaneously through interactive UpSet plots, (ii) automatically extract genes and proteins from free text through text-mining and Named Entity Recognition (NER) techniques, (iii) upload single nucleotide polymorphisms (SNPs) and extract their relative genes, or (iv) analyse multiple lists of differentially expressed proteins/genes after selecting them interactively from a parameterizable volcano plot. Compared to the previous version of 197 supported organisms, Flame (v2.0) currently allows enrichment for 14 436 organisms. AVAILABILITY AND IMPLEMENTATION: Web Application: http://flame.pavlopouloslab.info. Code: https://github.com/PavlopoulosLab/Flame. Docker: https://hub.docker.com/r/pavlopouloslab/flame. Oxford University Press 2023-08-04 /pmc/articles/PMC10423032/ /pubmed/37540207 http://dx.doi.org/10.1093/bioinformatics/btad490 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Paper
Karatzas, Evangelos
Baltoumas, Fotis A
Aplakidou, Eleni
Kontou, Panagiota I
Stathopoulos, Panos
Stefanis, Leonidas
Bagos, Pantelis G
Pavlopoulos, Georgios A
Flame (v2.0): advanced integration and interpretation of functional enrichment results from multiple sources
title Flame (v2.0): advanced integration and interpretation of functional enrichment results from multiple sources
title_full Flame (v2.0): advanced integration and interpretation of functional enrichment results from multiple sources
title_fullStr Flame (v2.0): advanced integration and interpretation of functional enrichment results from multiple sources
title_full_unstemmed Flame (v2.0): advanced integration and interpretation of functional enrichment results from multiple sources
title_short Flame (v2.0): advanced integration and interpretation of functional enrichment results from multiple sources
title_sort flame (v2.0): advanced integration and interpretation of functional enrichment results from multiple sources
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10423032/
https://www.ncbi.nlm.nih.gov/pubmed/37540207
http://dx.doi.org/10.1093/bioinformatics/btad490
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