Cargando…

The BinDiscover database: a biology-focused meta-analysis tool for 156,000 GC–TOF MS metabolome samples

Metabolomics by gas chromatography/mass spectrometry (GC/MS) provides a standardized and reliable platform for understanding small molecule biology. Since 2005, the West Coast Metabolomics Center at the University of California at Davis has collated GC/MS metabolomics data from over 156,000 samples...

Descripción completa

Detalles Bibliográficos
Autores principales: Bremer, Parker Ladd, Wohlgemuth, Gert, Fiehn, Oliver
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10359220/
https://www.ncbi.nlm.nih.gov/pubmed/37475020
http://dx.doi.org/10.1186/s13321-023-00734-8
_version_ 1785075831564926976
author Bremer, Parker Ladd
Wohlgemuth, Gert
Fiehn, Oliver
author_facet Bremer, Parker Ladd
Wohlgemuth, Gert
Fiehn, Oliver
author_sort Bremer, Parker Ladd
collection PubMed
description Metabolomics by gas chromatography/mass spectrometry (GC/MS) provides a standardized and reliable platform for understanding small molecule biology. Since 2005, the West Coast Metabolomics Center at the University of California at Davis has collated GC/MS metabolomics data from over 156,000 samples and 2000 studies into the standardized BinBase database. We believe that the observations from these samples will provide meaningful insight to biologists and that our data treatment and webtool will provide insight to others who seek to standardize disparate metabolomics studies. We here developed an easy-to-use query interface, BinDiscover, to enable intuitive, rapid hypothesis generation for biologists based on these metabolomic samples. BinDiscover creates observation summaries and graphics across a broad range of species, organs, diseases, and compounds. Throughout the components of BinDiscover, we emphasize the use of ontologies to aggregate large groups of samples based on the proximity of their metadata within these ontologies. This adjacency allows for the simultaneous exploration of entire categories such as “rodents”, “digestive tract”, or “amino acids”. The ontologies are particularly relevant for BinDiscover’s ontologically grouped differential analysis, which, like other components of BinDiscover, creates clear graphs and summary statistics across compounds and biological metadata. We exemplify BinDiscover’s extensive applicability in three showcases across biological domains. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13321-023-00734-8.
format Online
Article
Text
id pubmed-10359220
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Springer International Publishing
record_format MEDLINE/PubMed
spelling pubmed-103592202023-07-22 The BinDiscover database: a biology-focused meta-analysis tool for 156,000 GC–TOF MS metabolome samples Bremer, Parker Ladd Wohlgemuth, Gert Fiehn, Oliver J Cheminform Research Metabolomics by gas chromatography/mass spectrometry (GC/MS) provides a standardized and reliable platform for understanding small molecule biology. Since 2005, the West Coast Metabolomics Center at the University of California at Davis has collated GC/MS metabolomics data from over 156,000 samples and 2000 studies into the standardized BinBase database. We believe that the observations from these samples will provide meaningful insight to biologists and that our data treatment and webtool will provide insight to others who seek to standardize disparate metabolomics studies. We here developed an easy-to-use query interface, BinDiscover, to enable intuitive, rapid hypothesis generation for biologists based on these metabolomic samples. BinDiscover creates observation summaries and graphics across a broad range of species, organs, diseases, and compounds. Throughout the components of BinDiscover, we emphasize the use of ontologies to aggregate large groups of samples based on the proximity of their metadata within these ontologies. This adjacency allows for the simultaneous exploration of entire categories such as “rodents”, “digestive tract”, or “amino acids”. The ontologies are particularly relevant for BinDiscover’s ontologically grouped differential analysis, which, like other components of BinDiscover, creates clear graphs and summary statistics across compounds and biological metadata. We exemplify BinDiscover’s extensive applicability in three showcases across biological domains. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13321-023-00734-8. Springer International Publishing 2023-07-20 /pmc/articles/PMC10359220/ /pubmed/37475020 http://dx.doi.org/10.1186/s13321-023-00734-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Bremer, Parker Ladd
Wohlgemuth, Gert
Fiehn, Oliver
The BinDiscover database: a biology-focused meta-analysis tool for 156,000 GC–TOF MS metabolome samples
title The BinDiscover database: a biology-focused meta-analysis tool for 156,000 GC–TOF MS metabolome samples
title_full The BinDiscover database: a biology-focused meta-analysis tool for 156,000 GC–TOF MS metabolome samples
title_fullStr The BinDiscover database: a biology-focused meta-analysis tool for 156,000 GC–TOF MS metabolome samples
title_full_unstemmed The BinDiscover database: a biology-focused meta-analysis tool for 156,000 GC–TOF MS metabolome samples
title_short The BinDiscover database: a biology-focused meta-analysis tool for 156,000 GC–TOF MS metabolome samples
title_sort bindiscover database: a biology-focused meta-analysis tool for 156,000 gc–tof ms metabolome samples
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10359220/
https://www.ncbi.nlm.nih.gov/pubmed/37475020
http://dx.doi.org/10.1186/s13321-023-00734-8
work_keys_str_mv AT bremerparkerladd thebindiscoverdatabaseabiologyfocusedmetaanalysistoolfor156000gctofmsmetabolomesamples
AT wohlgemuthgert thebindiscoverdatabaseabiologyfocusedmetaanalysistoolfor156000gctofmsmetabolomesamples
AT fiehnoliver thebindiscoverdatabaseabiologyfocusedmetaanalysistoolfor156000gctofmsmetabolomesamples
AT bremerparkerladd bindiscoverdatabaseabiologyfocusedmetaanalysistoolfor156000gctofmsmetabolomesamples
AT wohlgemuthgert bindiscoverdatabaseabiologyfocusedmetaanalysistoolfor156000gctofmsmetabolomesamples
AT fiehnoliver bindiscoverdatabaseabiologyfocusedmetaanalysistoolfor156000gctofmsmetabolomesamples