Cargando…

funcExplorer: a tool for fast data-driven functional characterisation of high-throughput expression data

BACKGROUND: A widely applied approach to extract knowledge from high-throughput genomic data is clustering of gene expression profiles followed by functional enrichment analysis. This type of analysis, when done manually, is highly subjective and has limited reproducibility. Moreover, this pipeline...

Descripción completa

Detalles Bibliográficos
Autores principales: Kolberg, Liis, Kuzmin, Ivan, Adler, Priit, Vilo, Jaak, Peterson, Hedi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6236982/
https://www.ncbi.nlm.nih.gov/pubmed/30428831
http://dx.doi.org/10.1186/s12864-018-5176-x
_version_ 1783371125331329024
author Kolberg, Liis
Kuzmin, Ivan
Adler, Priit
Vilo, Jaak
Peterson, Hedi
author_facet Kolberg, Liis
Kuzmin, Ivan
Adler, Priit
Vilo, Jaak
Peterson, Hedi
author_sort Kolberg, Liis
collection PubMed
description BACKGROUND: A widely applied approach to extract knowledge from high-throughput genomic data is clustering of gene expression profiles followed by functional enrichment analysis. This type of analysis, when done manually, is highly subjective and has limited reproducibility. Moreover, this pipeline can be very time-consuming and resource-demanding as enrichment analysis is done for tens to hundreds of clusters at a time. Thus, the task often needs programming skills to form a pipeline of different software tools or R packages to enable an automated approach. Furthermore, visualising the results can be challenging. RESULTS: We developed a web tool, funcExplorer, which automatically combines hierarchical clustering and enrichment analysis to detect functionally related gene clusters. The functional characterisation is achieved using structured knowledge from data sources such as Gene Ontology, KEGG and Reactome pathways, Human Protein Atlas, and Human Phenotype Ontology. funcExplorer includes various measures for finding biologically meaningful clusters, provides a modern graphical user interface, and has wide-ranging data export and sharing options as well as software transparency by open-source code. The results are presented in a visually compact and interactive format, enabling users to explore the biological essence of the data. We compared our results with previously published gene clusters to demonstrate that funcExplorer can perform the data characterisation equally well, but without requiring labour-intensive manual interference. CONCLUSIONS: The open-source web tool funcExplorer enables scientists with high-throughput genomic data to obtain a preliminary interactive overview of the expression patterns, gene names, and shared functionalities in their dataset in a visually pleasing format. funcExplorer is publicly available at https://biit.cs.ut.ee/funcexplorer ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12864-018-5176-x) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-6236982
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-62369822018-11-23 funcExplorer: a tool for fast data-driven functional characterisation of high-throughput expression data Kolberg, Liis Kuzmin, Ivan Adler, Priit Vilo, Jaak Peterson, Hedi BMC Genomics Software BACKGROUND: A widely applied approach to extract knowledge from high-throughput genomic data is clustering of gene expression profiles followed by functional enrichment analysis. This type of analysis, when done manually, is highly subjective and has limited reproducibility. Moreover, this pipeline can be very time-consuming and resource-demanding as enrichment analysis is done for tens to hundreds of clusters at a time. Thus, the task often needs programming skills to form a pipeline of different software tools or R packages to enable an automated approach. Furthermore, visualising the results can be challenging. RESULTS: We developed a web tool, funcExplorer, which automatically combines hierarchical clustering and enrichment analysis to detect functionally related gene clusters. The functional characterisation is achieved using structured knowledge from data sources such as Gene Ontology, KEGG and Reactome pathways, Human Protein Atlas, and Human Phenotype Ontology. funcExplorer includes various measures for finding biologically meaningful clusters, provides a modern graphical user interface, and has wide-ranging data export and sharing options as well as software transparency by open-source code. The results are presented in a visually compact and interactive format, enabling users to explore the biological essence of the data. We compared our results with previously published gene clusters to demonstrate that funcExplorer can perform the data characterisation equally well, but without requiring labour-intensive manual interference. CONCLUSIONS: The open-source web tool funcExplorer enables scientists with high-throughput genomic data to obtain a preliminary interactive overview of the expression patterns, gene names, and shared functionalities in their dataset in a visually pleasing format. funcExplorer is publicly available at https://biit.cs.ut.ee/funcexplorer ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12864-018-5176-x) contains supplementary material, which is available to authorized users. BioMed Central 2018-11-14 /pmc/articles/PMC6236982/ /pubmed/30428831 http://dx.doi.org/10.1186/s12864-018-5176-x Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Software
Kolberg, Liis
Kuzmin, Ivan
Adler, Priit
Vilo, Jaak
Peterson, Hedi
funcExplorer: a tool for fast data-driven functional characterisation of high-throughput expression data
title funcExplorer: a tool for fast data-driven functional characterisation of high-throughput expression data
title_full funcExplorer: a tool for fast data-driven functional characterisation of high-throughput expression data
title_fullStr funcExplorer: a tool for fast data-driven functional characterisation of high-throughput expression data
title_full_unstemmed funcExplorer: a tool for fast data-driven functional characterisation of high-throughput expression data
title_short funcExplorer: a tool for fast data-driven functional characterisation of high-throughput expression data
title_sort funcexplorer: a tool for fast data-driven functional characterisation of high-throughput expression data
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6236982/
https://www.ncbi.nlm.nih.gov/pubmed/30428831
http://dx.doi.org/10.1186/s12864-018-5176-x
work_keys_str_mv AT kolbergliis funcexploreratoolforfastdatadrivenfunctionalcharacterisationofhighthroughputexpressiondata
AT kuzminivan funcexploreratoolforfastdatadrivenfunctionalcharacterisationofhighthroughputexpressiondata
AT adlerpriit funcexploreratoolforfastdatadrivenfunctionalcharacterisationofhighthroughputexpressiondata
AT vilojaak funcexploreratoolforfastdatadrivenfunctionalcharacterisationofhighthroughputexpressiondata
AT petersonhedi funcexploreratoolforfastdatadrivenfunctionalcharacterisationofhighthroughputexpressiondata