Symbolic Aggregate Approximation Improves Gap Filling in High-Resolution Mass Spectrometry Data Processing

Müller, E.; Huber, Carolin; Brack, Werner; Krauß, Martin; Schulze, Tobias

doi:10.1021/acs.analchem.0c00899

Cited by 8 publications

(1 citation statement)

References 35 publications

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“…Performance of numerous peak alignment algorithms has been compared (Koh et al, 2010;Coble and Fraga, 2014;Niu et al, 2014). Third, compounds present at low amounts are often not recognized initially by peak detection algorithms in all samples, so to avoid coding these small amounts as zero, they can be filled in by integrating ions present in other samples at that retention time (Katajamaa and Orešič, 2007;Domingo-Almenara et al, 2016;Müller et al, 2020). Modern implementations of these three algorithms and others for pre-processing of large GC-MS datasets are given in Box 2.…”

Section: Data Pre-processingmentioning

confidence: 99%

An analytical pipeline to support robust research on the ecology, evolution, and function of floral volatiles

Eisen

Powers²,

Raguso³

et al. 2022

Front. Ecol. Evol.

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Research on floral volatiles has grown substantially in the last 20 years, which has generated insights into their diversity and prevalence. These studies have paved the way for new research that explores the evolutionary origins and ecological consequences of different types of variation in floral scent, including community-level, functional, and environmentally induced variation. However, to address these types of questions, novel approaches are needed that can handle large sample sizes, provide quality control measures, and make volatile research more transparent and accessible, particularly for scientists without prior experience in this field. Drawing upon a literature review and our own experiences, we present a set of best practices for next-generation research in floral scent. We outline methods for data collection (experimental designs, methods for conducting field collections, analytical chemistry, compound identification) and data analysis (statistical analysis, database integration) that will facilitate the generation and interpretation of quality data. For the intermediate step of data processing, we created the R package bouquet, which provides a data analysis pipeline. The package contains functions that enable users to convert chromatographic peak integrations to a filtered data table that can be used in subsequent statistical analyses. This package includes default settings for filtering out non-floral compounds, including background contamination, based on our best-practice guidelines, but functions and workflows can be easily customized as necessary. Next-generation research into the ecology and evolution of floral scent has the potential to generate broadly relevant insights into how complex traits evolve, their genomic architecture, and their consequences for ecological interactions. In order to fulfill this potential, the methodology of floral scent studies needs to become more transparent and reproducible. By outlining best practices throughout the lifecycle of a project, from experimental design to statistical analysis, and providing an R package that standardizes the data processing pipeline, we provide a resource for new and seasoned researchers in this field and in adjacent fields, where high-throughput and multi-dimensional datasets are common.

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Section: Data Pre-processingmentioning

confidence: 99%

An analytical pipeline to support robust research on the ecology, evolution, and function of floral volatiles

Eisen

Powers²,

Raguso³

et al. 2022

Front. Ecol. Evol.

View full text Add to dashboard Cite

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Handling of problematic ion chromatograms with the Automated Target Screening (ATS) workflow for unsupervised analysis of high-resolution mass spectrometry data

Braun,

Krauss,

Spahr

et al. 2024

Anal Bioanal Chem

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Liquid chromatography (LC) or gas chromatography (GC) coupled to high-resolution mass spectrometry (HRMS) is a versatile analytical method for the analysis of thousands of chemical pollutants that can be found in environmental and biological samples. While the tools for handling such complex datasets have improved, there are still no fully automated workflows for targeted screening analysis. Here we present an R-based workflow that is able to cope with challenging data like noisy ion chromatograms, retention time shifts, and multiple peak patterns. The workflow can be applied to batches of HRMS data recorded after GC with electron ionization (GC-EI) and LC coupled to electrospray ionization in both negative and positive mode (LC-ESIneg/LC-ESIpos) to perform peak annotation and quantitation fully unsupervised. We used Orbitrap HRMS data of surface water extracts to compare the Automated Target Screening (ATS) workflow with data evaluations performed with the vendor software TraceFinder and the established semi-automated analysis workflow in the MZmine software. The ATS approach increased the overall evaluation performance of the peak annotation compared to the established MZmine module without the need for any post-hoc corrections. The overall accuracy increased from 0.80 to 0.86 (LC-ESIpos), from 0.77 to 0.83 (LC-ESIneg), and from 0.67 to 0.76 (GC-EI). The mean average percentage errors for quantification of ATS were around 30% compared to the manual quantification with TraceFinder. The ATS workflow enables time-efficient analysis of GC- and LC-HRMS data and accelerates and improves the applicability of target screening in studies with a large number of analytes and sample sizes without the need for manual intervention. Graphical Abstract

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Augmentation of field fluorescence measures for improved in situ contaminant detection

Remolina

Peleato²

2022

Environ Monit Assess

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Symbolic Aggregate Approximation Improves Gap Filling in High-Resolution Mass Spectrometry Data Processing

Cited by 8 publications

References 35 publications

An analytical pipeline to support robust research on the ecology, evolution, and function of floral volatiles

An analytical pipeline to support robust research on the ecology, evolution, and function of floral volatiles

Handling of problematic ion chromatograms with the Automated Target Screening (ATS) workflow for unsupervised analysis of high-resolution mass spectrometry data

Augmentation of field fluorescence measures for improved in situ contaminant detection

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