ArticleBioinformatics (Oxford, England)2018
R2DGC: threshold-free peak alignment and identification for 2D gas chromatography-mass spectrometry in R.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Evaluation of peak alignment performance of commercial and open-source tools for GC×GC-MS based non-targeted screening.Briefings in bioinformatics · 2026Article
- Postnatal persistence of nonhuman primate sex-dependent renal structural and molecular changes programmed by intrauterine growth restriction.Journal of medical primatology · 2022Article
- Metabolic Alterations and WNT Signaling Impact Immune Response in HGSOC.Clinical cancer research : an official journal of the American Association for Cancer Research · 2022Article
- The metaRbolomics Toolbox in Bioconductor and beyond.Metabolites · 2019Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Summary: Comprehensive 2D gas chromatography-mass spectrometry is a powerful method for analyzing complex mixtures of volatile compounds, but produces a large amount of raw data that requires downstream processing to align signals of interest (peaks) across multiple samples and match peak characteristics to reference standard libraries prior to downstream statistical analysis. Very few existing tools address this aspect of analysis and those that do have shortfalls in usability or performance. We have developed an R package that implements retention time and mass spectra similarity threshold-free alignments, seamlessly integrates retention time standards for universally reproducible alignments, performs common ion filtering and provides compatibility with multiple peak quantification methods. We demonstrate that our package's performance compares favorably to existing tools on a controlled mix of metabolite standards separated under variable chromatography conditions and data generated from cell lines. Availability and implementation: R2DGC can be downloaded at https://github.com/rramaker/R2DGC or installed via the Comprehensive R Archive Network (CRAN). Contact: sjcooper@hudsonalpha.org. Supplementary information: Supplementary data are available at Bioinformatics online.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.