Probabilistic Model for Untargeted Peak Detection in LC–MS Using Bayesian Statistics

Woldegebriel, Michael; Vivó-Truyols, Gabriel

doi:10.1021/acs.analchem.5b01521

Cited by 29 publications

(38 citation statements)

References 29 publications

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“…We use this relaxed signal-to-noise threshold by default in the freely available implementation of this algorithms in the R package xcms. The targeted peak picking with predicted isotope ROIs can easily be adapted in other tools such as MZmine2 [38], apLCMS [39], and related approaches [40]. The validation of putative isotope clusters in combination with predicted isotope ROIs results in the highest number of correctly predicted molecular formulas and also the highest number of correct molecular formulas among the first three ranks.…”

Section: Discussionmentioning

confidence: 99%

Prediction, Detection, and Validation of Isotope Clusters in Mass Spectrometry Data

Treutler

Neumann

2016

Metabolites

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Mass spectrometry is a key analytical platform for metabolomics. The precise quantification and identification of small molecules is a prerequisite for elucidating the metabolism and the detection, validation, and evaluation of isotope clusters in LC-MS data is important for this task. Here, we present an approach for the improved detection of isotope clusters using chemical prior knowledge and the validation of detected isotope clusters depending on the substance mass using database statistics. We find remarkable improvements regarding the number of detected isotope clusters and are able to predict the correct molecular formula in the top three ranks in 92% of the cases. We make our methodology freely available as part of the Bioconductor packages xcms version 1.50.0 and CAMERA version 1.30.0.

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Section: Discussionmentioning

confidence: 99%

Prediction, Detection, and Validation of Isotope Clusters in Mass Spectrometry Data

Treutler

Neumann

2016

Metabolites

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“…A Bayesian method has been successfully used by Adutwum et al for determining the regions of interest [118]. A Bayesian probabilistic model for untargeted peak detection was developed for LC-MS by Woldegebriel et al [119]. The advantage of the latter approach was that true peaks could be distinguished from chemical noise without any pre-processing.…”

Section: Recent Developments In Peak Detectionmentioning

confidence: 99%

Recent applications of chemometrics in one‐ and two‐dimensional chromatography

Bos

Knol

Molenaar

et al. 2020

J of Separation Science

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The proliferation of increasingly more sophisticated analytical separation systems, often incorporating increasingly more powerful detection techniques, such as highresolution mass spectrometry, causes an urgent need for highly efficient dataanalysis and optimization strategies. This is especially true for comprehensive twodimensional chromatography applied to the separation of very complex samples. In this contribution, the requirement for chemometric tools is explained and the latest developments in approaches for (pre-)processing and analyzing data arising from oneand two-dimensional chromatography systems are reviewed. The final part of this review focuses on the application of chemometrics for method development and optimization.

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“…In this work, we introduce a novel probabilistic approach for peak detection in CE‐LIF epg's from STR DNA profiling data. Our Bayesian framework has been originally developed and introduced for chromatographic‐mass spectrometric data in analytical chemistry and proved to be ideally suited for this purpose . We theorize that the statistical evaluation of low‐level (complex) DNA profiles will be enhanced by including probabilistic peak detection data which are generated directly from CE data.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic peak detection in CE‐LIF for STR DNA typing

et al. 2017

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In this work, we present a novel probabilistic peak detection algorithm based on a Bayesian framework for forensic DNA analysis. The proposed method aims at an exhaustive use of raw electropherogram data from a laser-induced fluorescence multi-CE system. As the raw data are informative up to a single data point, the conventional threshold-based approaches discard relevant forensic information early in the data analysis pipeline. Our proposed method assigns a posterior probability reflecting the data point's relevance with respect to peak detection criteria. Peaks of low intensity generated from a truly existing allele can thus constitute evidential value instead of fully discarding them and contemplating a potential allele drop-out. This way of working utilizes the information available within each individual data point and thus avoids making early (binary) decisions on the data analysis that can lead to error propagation. The proposed method was tested and compared to the application of a set threshold as is current practice in forensic STR DNA profiling. The new method was found to yield a significant improvement in the number of alleles identified, regardless of the peak heights and deviation from Gaussian shape.

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Probabilistic Model for Untargeted Peak Detection in LC–MS Using Bayesian Statistics

Cited by 29 publications

References 29 publications

Prediction, Detection, and Validation of Isotope Clusters in Mass Spectrometry Data

Prediction, Detection, and Validation of Isotope Clusters in Mass Spectrometry Data

Recent applications of chemometrics in one‐ and two‐dimensional chromatography

Probabilistic peak detection in CE‐LIF for STR DNA typing

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