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

Treutler, Hendrik; Neumann, Steffen

doi:10.3390/metabo6040037

Cited by 20 publications

(16 citation statements)

References 41 publications

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“…As was mentioned above, deisotoping is incorporated into feature assembly related to peak picking in FeatureFinderMetabo. An extension of XCMS and CAMERA for an enhanced isotope cluster detection and validation has been recently developed [105] and used in plant stress metabolomics [106,107]. The deisotoping step is crucial in case of sulphur containing metabolites for which detection is based on mass shift between isotopes 32S (the mainly occurring) and 34S (occurring at a low intensity).…”

Section: Bioinformatics and Statistical Analysis In Metabolomicsmentioning

confidence: 99%

Analytical Methods for Detection of Plant Metabolomes Changes in Response to Biotic and Abiotic Stresses

Piasecka

Kachlicki

Stobiecki

2019

IJMS

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Abiotic and biotic stresses are the main reasons of substantial crop yield losses worldwide. Research devoted to reveal mechanisms of plant reactions during their interactions with the environment are conducted on the level of genome, transcriptome, proteome, and metabolome. Data obtained during these studies would permit to define biochemical and physiological mechanisms of plant resistance or susceptibility to affecting factors/stresses. Metabolomics based on mass spectrometric techniques is an important part of research conducted in the direction of breeding new varieties of crop plants tolerant to the affecting stresses and possessing good agronomical features. Studies of this kind are carried out on model, crop and resurrection plants. Metabolites profiling yields large sets of data and due to this fact numerous advanced statistical and bioinformatic methods permitting to obtain qualitative and quantitative evaluation of the results have been developed. Moreover, advanced integration of metabolomics data with these obtained on other omics levels: genome, transcriptome and proteome should be carried out. Such a holistic approach would bring us closer to understanding biochemical and physiological processes of the cell and whole plant interacting with the environment and further apply these observations in successful breeding of stress tolerant or resistant crop plants.

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Section: Bioinformatics and Statistical Analysis In Metabolomicsmentioning

confidence: 99%

Analytical Methods for Detection of Plant Metabolomes Changes in Response to Biotic and Abiotic Stresses

Piasecka

Kachlicki

Stobiecki

2019

IJMS

View full text Add to dashboard Cite

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“…Feature tables do not contain unique signals corresponding to a specific metabolite, but also redundant features (e.g., different isotopes, charges, and adducts in soft ionization techniques such as ESI and MALDI), background signals, etc. [ 109 ]. Computational techniques can address the challenge of isotope and adduct annotation, including the well-known R package CAMERA (based on peak grouping after retention time and peak shape correlation to form groups of ions, followed by annotation of possible isotopes and adducts) and the more recent web application MS-FLO (based on several parameters such as peak height, retention time alignment and mass similarities to detect adducts, isotopes and duplicate features in a preprocessed dataset) [ 109 , 110 ].…”

Section: Experimental Designmentioning

confidence: 99%

“…[ 109 ]. Computational techniques can address the challenge of isotope and adduct annotation, including the well-known R package CAMERA (based on peak grouping after retention time and peak shape correlation to form groups of ions, followed by annotation of possible isotopes and adducts) and the more recent web application MS-FLO (based on several parameters such as peak height, retention time alignment and mass similarities to detect adducts, isotopes and duplicate features in a preprocessed dataset) [ 109 , 110 ]. The number of software packages, databases for metabolite annotation, and processing tools increase every year, with the most recent developments compiled in a review paper by Misra [ 107 ].…”

Section: Experimental Designmentioning

confidence: 99%

Mass Spectrometry-Based Zebrafish Toxicometabolomics: A Review of Analytical and Data Quality Challenges

et al. 2021

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Metabolomics has achieved great progress over the last 20 years, and it is currently considered a mature research field. As a result, the number of applications in toxicology, biomarker, and drug discovery has also increased. Toxicometabolomics has emerged as a powerful strategy to provide complementary information to study molecular-level toxic effects, which can be combined with a wide range of toxicological assessments and models. The zebrafish model has gained importance in recent decades as a bridging tool between in vitro assays and mammalian in vivo studies in the field of toxicology. Furthermore, as this vertebrate model is a low-cost system and features highly conserved metabolic pathways found in humans and mammalian models, it is a promising tool for toxicometabolomics. This short review aims to introduce zebrafish researchers interested in understanding the effects of chemical exposure using metabolomics to the challenges and possibilities of the field, with a special focus on toxicometabolomics-based mass spectrometry. The overall goal is to provide insights into analytical strategies to generate and identify high-quality metabolomic experiments focusing on quality management systems (QMS) and the importance of data reporting and sharing.

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“…XCMS now contains 7 different peak detection algorithms (Smith et al 2006 ; Du et al 2006 ; Treutler and Neumann 2016 ), including Massifquant (Conley et al 2014 ), as well as the established matchFilter (Smith et al 2006 ) and CentWave (Tautenhahn et al 2008 ) methods. The Massifquant (Conley et al 2014 ) algorithm is an open-source implementation of the TracMass (Döös et al 2013 ) algorithm that is designed for isotope trace detection.…”

Section: Preprocessingmentioning

confidence: 99%

Navigating freely-available software tools for metabolomics analysis

et al. 2017

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IntroductionThe field of metabolomics has expanded greatly over the past two decades, both as an experimental science with applications in many areas, as well as in regards to data standards and bioinformatics software tools. The diversity of experimental designs and instrumental technologies used for metabolomics has led to the need for distinct data analysis methods and the development of many software tools.ObjectivesTo compile a comprehensive list of the most widely used freely available software and tools that are used primarily in metabolomics.MethodsThe most widely used tools were selected for inclusion in the review by either ≥ 50 citations on Web of Science (as of 08/09/16) or the use of the tool being reported in the recent Metabolomics Society survey. Tools were then categorised by the type of instrumental data (i.e. LC–MS, GC–MS or NMR) and the functionality (i.e. pre- and post-processing, statistical analysis, workflow and other functions) they are designed for.ResultsA comprehensive list of the most used tools was compiled. Each tool is discussed within the context of its application domain and in relation to comparable tools of the same domain. An extended list including additional tools is available at https://github.com/RASpicer/MetabolomicsTools which is classified and searchable via a simple controlled vocabulary.ConclusionThis review presents the most widely used tools for metabolomics analysis, categorised based on their main functionality. As future work, we suggest a direct comparison of tools’ abilities to perform specific data analysis tasks e.g. peak picking.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-017-1242-7) contains supplementary material, which is available to authorized users.

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

Cited by 20 publications

References 41 publications

Analytical Methods for Detection of Plant Metabolomes Changes in Response to Biotic and Abiotic Stresses

Analytical Methods for Detection of Plant Metabolomes Changes in Response to Biotic and Abiotic Stresses

Mass Spectrometry-Based Zebrafish Toxicometabolomics: A Review of Analytical and Data Quality Challenges

Navigating freely-available software tools for metabolomics analysis

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