<i>proFIA</i>: a data preprocessing workflow for flow injection analysis coupled to high-resolution mass spectrometry

Delabrière, Alexis; Hohenester, Ulli Martin; Colsch, Benoît; Junot, Christophe; Fenaille, François; Thévenot, Etienne

doi:10.1093/bioinformatics/btx458

Cited by 15 publications

(10 citation statements)

References 42 publications

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“…Once the peak lists have been extracted from each file, alignment of the features between the samples is performed by using a kernel Gaussian density ( Delabrière et al , 2017 ; Smith et al , 2006 ). Two quality control steps may then be applied to select features (i) with a high reproducibility between samples (alternatively between classes of samples), and/or (ii) labeled as ‘exhaled breath’ in the majority of samples (by thresholding the P -value of the statistical tests described above).…”

Section: Methodsmentioning

confidence: 99%

ptairMS: real-time processing and analysis of PTR-TOF-MS data for biomarker discovery in exhaled breath

2022

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Motivation Analysis of Volatile Organic Compounds (VOCs) in exhaled breath by Proton Transfer Reaction Time-of-Flight Mass Spectrometry (PTR-TOF-MS) is of increasing interest for real-time, non-invasive diagnosis, phenotyping and therapeutic drug monitoring in the clinics. However, there is currently a lack of methods and software tools for the processing of PTR-TOF-MS data from cohorts and suited for biomarker discovery studies. Results We developed a comprehensive suite of algorithms that process raw data from patient acquisitions and generate the table of feature intensities. Notably, we included an innovative 2D peak deconvolution model based on penalized splines signal regression for accurate estimation of the temporal profile and feature quantification, as well as a method to specifically select the VOCs from exhaled breath. The workflow was implemented as the ptairMS software, which contains a graphical interface to facilitate cohort management and data analysis. The approach was validated on both simulated and experimental data sets, and we showed that the sensitivity and specificity of the VOC detection reached 99% and 98.4%, respectively, and that the error of quantification was below 8.1% for concentrations down to 19 ppb. Availability The ptairMS software is publicly available as an R package on Bioconductor (doi:10.18129/B9.bioc.ptairMS), as well as its companion experiment package ptairData (doi:10.18129/B9.bioc.ptairData). Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Methodsmentioning

confidence: 99%

ptairMS: real-time processing and analysis of PTR-TOF-MS data for biomarker discovery in exhaled breath

2022

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“…Single acquisition Orbitrap mass spectra (no averaging) were acquired at a resolution of 500 000 (FWHM, m / z 200) with an automatic gain control (AGC) of 2 × 10 5 and a maximum injection time of 100 ms (typically ion injection times were about 2 ms, and they rarely surpassed 10 ms because the AGC target had already been reached). Data evaluation was performed with the proFIA tool described by Thevenot and coworkers 42 …”

Section: Methodsmentioning

confidence: 99%

Investigation of space charge effects and ion trapping capacity on direct introduction ultra‐high‐resolution mass spectrometry workflows for metabolomics

et al. 2020

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Ultra-high-resolution mass spectrometry, in the absence of chromatography, is finding its place for direct analyses of highly complex mixtures, such as those encountered during untargeted metabolomics screening. Advances, however, have been tempered by difficulties such as uneven signal suppression experienced during electrospray ionization. Moreover, ultra-high-resolution mass spectrometers that use Orbitrap and ICR analyzers both suffer from limited ion trapping capacities, owing principally to space-charge effects. This study has evaluated and contrasted the above two types of Fourier transform mass spectrometers for their abilities to detect and identify by accurate mass measurement, small molecule metabolites present in complex mixtures. For these direct introduction studies, the Orbitrap Fusion showed a major advantage in terms of speed of analysis, enabling detection of 218 of 440 molecules (<2 ppm error, 500 000 resolution at m/z 200) present in a complex mixture in 5 min. This approach is the most viable for high-throughput workflows, such as those used in investigations involving very large cohorts of metabolomics samples. From the same mixture, 183 unique molecules were observed by FT-ICR in the broadband mode, but this number was raised to 235 when "selected ion monitoring-stitching" (SIM-stitching) was employed (<0.1 ppm error, 7 T magnet with dynamic harmonization cell, 1.8 million resolution at m/z 200, both cases). SIMstitching FT-ICR thus offered the most complete detection, which may be of paramount importance in situations where it is essential to obtain the most complete metabolic profile possible. This added completeness, however, came at the cost of a more lengthy analysis time (120 min including manual treatment). Compared to the data presented here, future automation of processing, plus the use of absorption mode detection, segmented ion detection (stepwise detection of smaller width m/z sections), and higher magnetic field strengths, can substantially reduce FT-ICR acquisition times.

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“…proFIA is an R package that implements several novel algorithms to preprocess Flow Injection Analysis coupled to High‐Resolution Mass Spectrometry (FIA‐HRMS) workflows for high throughput metabolomics studies . The tool performs noise estimation, peak detection and quantification, peak grouping across samples, and missing value imputation among other functionalities.…”

Section: Tools For Data Analysismentioning

confidence: 99%

New tools and resources in metabolomics: 2016–2017

Misra¹

2018

Electrophoresis

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Rapid advances in mass spectrometry (MS) and nuclear magnetic resonance (NMR)-based platforms for metabolomics have led to an upsurge of data every single year. Newer high-throughput platforms, hyphenated technologies, miniaturization, and tool kits in data acquisition efforts in metabolomics have led to additional challenges in metabolomics data pre-processing, analysis, interpretation, and integration. Thanks to the informatics, statistics, and computational community, new resources continue to develop for metabolomics researchers. The purpose of this review is to provide a summary of the metabolomics tools, software, and databases that were developed or improved during 2016-2017, thus, enabling readers, developers, and researchers access to a succinct but thorough list of resources for further improvisation, implementation, and application in due course of time.

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proFIA: a data preprocessing workflow for flow injection analysis coupled to high-resolution mass spectrometry

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 15 publications

References 42 publications

ptairMS: real-time processing and analysis of PTR-TOF-MS data for biomarker discovery in exhaled breath

ptairMS: real-time processing and analysis of PTR-TOF-MS data for biomarker discovery in exhaled breath

Investigation of space charge effects and ion trapping capacity on direct introduction ultra‐high‐resolution mass spectrometry workflows for metabolomics

New tools and resources in metabolomics: 2016–2017

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