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MetaOmGraph: a workbench for interactive exploratory data analysis of large expression datasets
The diverse and growing omics data in public domains provide researchers with tremendous opportunity to extract hidden, yet undiscovered, knowledge. However, the vast majority of archived data remain unused. Here, we present MetaOmGraph (MOG), a free, open-source, standalone software for exploratory...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7039010/ https://www.ncbi.nlm.nih.gov/pubmed/31956905 http://dx.doi.org/10.1093/nar/gkz1209 |
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author | Singh, Urminder Hur, Manhoi Dorman, Karin Wurtele, Eve Syrkin |
author_facet | Singh, Urminder Hur, Manhoi Dorman, Karin Wurtele, Eve Syrkin |
author_sort | Singh, Urminder |
collection | PubMed |
description | The diverse and growing omics data in public domains provide researchers with tremendous opportunity to extract hidden, yet undiscovered, knowledge. However, the vast majority of archived data remain unused. Here, we present MetaOmGraph (MOG), a free, open-source, standalone software for exploratory analysis of massive datasets. Researchers, without coding, can interactively visualize and evaluate data in the context of its metadata, honing-in on groups of samples or genes based on attributes such as expression values, statistical associations, metadata terms and ontology annotations. Interaction with data is easy via interactive visualizations such as line charts, box plots, scatter plots, histograms and volcano plots. Statistical analyses include co-expression analysis, differential expression analysis and differential correlation analysis, with significance tests. Researchers can send data subsets to R for additional analyses. Multithreading and indexing enable efficient big data analysis. A researcher can create new MOG projects from any numerical data; or explore an existing MOG project. MOG projects, with history of explorations, can be saved and shared. We illustrate MOG by case studies of large curated datasets from human cancer RNA-Seq, where we identify novel putative biomarker genes in different tumors, and microarray and metabolomics data from Arabidopsis thaliana. MOG executable and code: http://metnetweb.gdcb.iastate.edu/ and https://github.com/urmi-21/MetaOmGraph/. |
format | Online Article Text |
id | pubmed-7039010 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-70390102020-03-02 MetaOmGraph: a workbench for interactive exploratory data analysis of large expression datasets Singh, Urminder Hur, Manhoi Dorman, Karin Wurtele, Eve Syrkin Nucleic Acids Res Methods Online The diverse and growing omics data in public domains provide researchers with tremendous opportunity to extract hidden, yet undiscovered, knowledge. However, the vast majority of archived data remain unused. Here, we present MetaOmGraph (MOG), a free, open-source, standalone software for exploratory analysis of massive datasets. Researchers, without coding, can interactively visualize and evaluate data in the context of its metadata, honing-in on groups of samples or genes based on attributes such as expression values, statistical associations, metadata terms and ontology annotations. Interaction with data is easy via interactive visualizations such as line charts, box plots, scatter plots, histograms and volcano plots. Statistical analyses include co-expression analysis, differential expression analysis and differential correlation analysis, with significance tests. Researchers can send data subsets to R for additional analyses. Multithreading and indexing enable efficient big data analysis. A researcher can create new MOG projects from any numerical data; or explore an existing MOG project. MOG projects, with history of explorations, can be saved and shared. We illustrate MOG by case studies of large curated datasets from human cancer RNA-Seq, where we identify novel putative biomarker genes in different tumors, and microarray and metabolomics data from Arabidopsis thaliana. MOG executable and code: http://metnetweb.gdcb.iastate.edu/ and https://github.com/urmi-21/MetaOmGraph/. Oxford University Press 2020-02-28 2020-01-20 /pmc/articles/PMC7039010/ /pubmed/31956905 http://dx.doi.org/10.1093/nar/gkz1209 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methods Online Singh, Urminder Hur, Manhoi Dorman, Karin Wurtele, Eve Syrkin MetaOmGraph: a workbench for interactive exploratory data analysis of large expression datasets |
title | MetaOmGraph: a workbench for interactive exploratory data analysis of large expression datasets |
title_full | MetaOmGraph: a workbench for interactive exploratory data analysis of large expression datasets |
title_fullStr | MetaOmGraph: a workbench for interactive exploratory data analysis of large expression datasets |
title_full_unstemmed | MetaOmGraph: a workbench for interactive exploratory data analysis of large expression datasets |
title_short | MetaOmGraph: a workbench for interactive exploratory data analysis of large expression datasets |
title_sort | metaomgraph: a workbench for interactive exploratory data analysis of large expression datasets |
topic | Methods Online |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7039010/ https://www.ncbi.nlm.nih.gov/pubmed/31956905 http://dx.doi.org/10.1093/nar/gkz1209 |
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