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miEAA 2.0: integrating multi-species microRNA enrichment analysis and workflow management systems

Gene set enrichment analysis has become one of the most frequently used applications in molecular biology research. Originally developed for gene sets, the same statistical principles are now available for all omics types. In 2016, we published the miRNA enrichment analysis and annotation tool (miEA...

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Autores principales: Kern, Fabian, Fehlmann, Tobias, Solomon, Jeffrey, Schwed, Louisa, Grammes, Nadja, Backes, Christina, Van Keuren-Jensen, Kendall, Craig, David Wesley, Meese, Eckart, Keller, Andreas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7319446/
https://www.ncbi.nlm.nih.gov/pubmed/32374865
http://dx.doi.org/10.1093/nar/gkaa309
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author Kern, Fabian
Fehlmann, Tobias
Solomon, Jeffrey
Schwed, Louisa
Grammes, Nadja
Backes, Christina
Van Keuren-Jensen, Kendall
Craig, David Wesley
Meese, Eckart
Keller, Andreas
author_facet Kern, Fabian
Fehlmann, Tobias
Solomon, Jeffrey
Schwed, Louisa
Grammes, Nadja
Backes, Christina
Van Keuren-Jensen, Kendall
Craig, David Wesley
Meese, Eckart
Keller, Andreas
author_sort Kern, Fabian
collection PubMed
description Gene set enrichment analysis has become one of the most frequently used applications in molecular biology research. Originally developed for gene sets, the same statistical principles are now available for all omics types. In 2016, we published the miRNA enrichment analysis and annotation tool (miEAA) for human precursor and mature miRNAs. Here, we present miEAA 2.0, supporting miRNA input from ten frequently investigated organisms. To facilitate inclusion of miEAA in workflow systems, we implemented an Application Programming Interface (API). Users can perform miRNA set enrichment analysis using either the web-interface, a dedicated Python package, or custom remote clients. Moreover, the number of category sets was raised by an order of magnitude. We implemented novel categories like annotation confidence level or localisation in biological compartments. In combination with the miRBase miRNA-version and miRNA-to-precursor converters, miEAA supports research settings where older releases of miRBase are in use. The web server also offers novel comprehensive visualizations such as heatmaps and running sum curves with background distributions. We demonstrate the new features with case studies for human kidney cancer, a biomarker study on Parkinson’s disease from the PPMI cohort, and a mouse model for breast cancer. The tool is freely accessible at: https://www.ccb.uni-saarland.de/mieaa2.
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spelling pubmed-73194462020-07-01 miEAA 2.0: integrating multi-species microRNA enrichment analysis and workflow management systems Kern, Fabian Fehlmann, Tobias Solomon, Jeffrey Schwed, Louisa Grammes, Nadja Backes, Christina Van Keuren-Jensen, Kendall Craig, David Wesley Meese, Eckart Keller, Andreas Nucleic Acids Res Web Server Issue Gene set enrichment analysis has become one of the most frequently used applications in molecular biology research. Originally developed for gene sets, the same statistical principles are now available for all omics types. In 2016, we published the miRNA enrichment analysis and annotation tool (miEAA) for human precursor and mature miRNAs. Here, we present miEAA 2.0, supporting miRNA input from ten frequently investigated organisms. To facilitate inclusion of miEAA in workflow systems, we implemented an Application Programming Interface (API). Users can perform miRNA set enrichment analysis using either the web-interface, a dedicated Python package, or custom remote clients. Moreover, the number of category sets was raised by an order of magnitude. We implemented novel categories like annotation confidence level or localisation in biological compartments. In combination with the miRBase miRNA-version and miRNA-to-precursor converters, miEAA supports research settings where older releases of miRBase are in use. The web server also offers novel comprehensive visualizations such as heatmaps and running sum curves with background distributions. We demonstrate the new features with case studies for human kidney cancer, a biomarker study on Parkinson’s disease from the PPMI cohort, and a mouse model for breast cancer. The tool is freely accessible at: https://www.ccb.uni-saarland.de/mieaa2. Oxford University Press 2020-07-02 2020-05-06 /pmc/articles/PMC7319446/ /pubmed/32374865 http://dx.doi.org/10.1093/nar/gkaa309 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Web Server Issue
Kern, Fabian
Fehlmann, Tobias
Solomon, Jeffrey
Schwed, Louisa
Grammes, Nadja
Backes, Christina
Van Keuren-Jensen, Kendall
Craig, David Wesley
Meese, Eckart
Keller, Andreas
miEAA 2.0: integrating multi-species microRNA enrichment analysis and workflow management systems
title miEAA 2.0: integrating multi-species microRNA enrichment analysis and workflow management systems
title_full miEAA 2.0: integrating multi-species microRNA enrichment analysis and workflow management systems
title_fullStr miEAA 2.0: integrating multi-species microRNA enrichment analysis and workflow management systems
title_full_unstemmed miEAA 2.0: integrating multi-species microRNA enrichment analysis and workflow management systems
title_short miEAA 2.0: integrating multi-species microRNA enrichment analysis and workflow management systems
title_sort mieaa 2.0: integrating multi-species microrna enrichment analysis and workflow management systems
topic Web Server Issue
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7319446/
https://www.ncbi.nlm.nih.gov/pubmed/32374865
http://dx.doi.org/10.1093/nar/gkaa309
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