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peakPantheR, an R package for large-scale targeted extraction and integration of annotated metabolic features in LC–MS profiling datasets
SUMMARY: Untargeted liquid chromatography–mass spectrometry (LC–MS) profiling assays are capable of measuring thousands of chemical compounds in a single sample, but unreliable feature extraction and metabolite identification remain considerable barriers to their interpretation and usefulness. peakP...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8665750/ https://www.ncbi.nlm.nih.gov/pubmed/34125879 http://dx.doi.org/10.1093/bioinformatics/btab433 |
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author | Wolfer, Arnaud M D S Correia, Gonçalo Sands, Caroline J Camuzeaux, Stephane Yuen, Ada H Y Chekmeneva, Elena Takáts, Zoltán Pearce, Jake T M Lewis, Matthew R |
author_facet | Wolfer, Arnaud M D S Correia, Gonçalo Sands, Caroline J Camuzeaux, Stephane Yuen, Ada H Y Chekmeneva, Elena Takáts, Zoltán Pearce, Jake T M Lewis, Matthew R |
author_sort | Wolfer, Arnaud M |
collection | PubMed |
description | SUMMARY: Untargeted liquid chromatography–mass spectrometry (LC–MS) profiling assays are capable of measuring thousands of chemical compounds in a single sample, but unreliable feature extraction and metabolite identification remain considerable barriers to their interpretation and usefulness. peakPantheR (Peak Picking and ANnoTation of High-resolution Experiments in R) is an R package for the targeted extraction and integration of annotated features from LC–MS profiling experiments. It takes advantage of chromatographic and spectral databases and prior information of sample matrix composition to generate annotated and interpretable metabolic phenotypic datasets and power workflows for real-time data quality assessment. AVAILABILITY AND IMPLEMENTATION: peakPantheR is available via Bioconductor (https://bioconductor.org/packages/peakPantheR/). Documentation and worked examples are available at https://phenomecentre.github.io/peakPantheR.github.io/ and https://github.com/phenomecentre/metabotyping-dementia-urine. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-8665750 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-86657502021-12-13 peakPantheR, an R package for large-scale targeted extraction and integration of annotated metabolic features in LC–MS profiling datasets Wolfer, Arnaud M D S Correia, Gonçalo Sands, Caroline J Camuzeaux, Stephane Yuen, Ada H Y Chekmeneva, Elena Takáts, Zoltán Pearce, Jake T M Lewis, Matthew R Bioinformatics Applications Notes SUMMARY: Untargeted liquid chromatography–mass spectrometry (LC–MS) profiling assays are capable of measuring thousands of chemical compounds in a single sample, but unreliable feature extraction and metabolite identification remain considerable barriers to their interpretation and usefulness. peakPantheR (Peak Picking and ANnoTation of High-resolution Experiments in R) is an R package for the targeted extraction and integration of annotated features from LC–MS profiling experiments. It takes advantage of chromatographic and spectral databases and prior information of sample matrix composition to generate annotated and interpretable metabolic phenotypic datasets and power workflows for real-time data quality assessment. AVAILABILITY AND IMPLEMENTATION: peakPantheR is available via Bioconductor (https://bioconductor.org/packages/peakPantheR/). Documentation and worked examples are available at https://phenomecentre.github.io/peakPantheR.github.io/ and https://github.com/phenomecentre/metabotyping-dementia-urine. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2021-06-14 /pmc/articles/PMC8665750/ /pubmed/34125879 http://dx.doi.org/10.1093/bioinformatics/btab433 Text en © The Author(s) 2021. 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 | Applications Notes Wolfer, Arnaud M D S Correia, Gonçalo Sands, Caroline J Camuzeaux, Stephane Yuen, Ada H Y Chekmeneva, Elena Takáts, Zoltán Pearce, Jake T M Lewis, Matthew R peakPantheR, an R package for large-scale targeted extraction and integration of annotated metabolic features in LC–MS profiling datasets |
title | peakPantheR, an R package for large-scale targeted extraction and integration of annotated metabolic features in LC–MS profiling datasets |
title_full | peakPantheR, an R package for large-scale targeted extraction and integration of annotated metabolic features in LC–MS profiling datasets |
title_fullStr | peakPantheR, an R package for large-scale targeted extraction and integration of annotated metabolic features in LC–MS profiling datasets |
title_full_unstemmed | peakPantheR, an R package for large-scale targeted extraction and integration of annotated metabolic features in LC–MS profiling datasets |
title_short | peakPantheR, an R package for large-scale targeted extraction and integration of annotated metabolic features in LC–MS profiling datasets |
title_sort | peakpanther, an r package for large-scale targeted extraction and integration of annotated metabolic features in lc–ms profiling datasets |
topic | Applications Notes |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8665750/ https://www.ncbi.nlm.nih.gov/pubmed/34125879 http://dx.doi.org/10.1093/bioinformatics/btab433 |
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