One Step Forward for Reducing False Positive and False Negative Compound Identifications from Mass Spectrometry Metabolomics Data: New Algorithms for Constructing Extracted Ion Chromatograms and Detecting Chromatographic Peaks

Myers, Owen D.; Sumner, Susan; Li, Shuzhao; Barnes, Stephen; Du, Xiuxia

doi:10.1021/acs.analchem.7b00947

Cited by 315 publications

(292 citation statements)

References 33 publications

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“…Centroided mass detection was done keeping the noise level at 1 and 0.01 for MS1 and MS2, respectively. Chromatograms were built using the ADAP algorithm (Myers et al 2017) by inputting the following parameters: intensity threshold of 5.0, minimum highest intensity of 5.0 and m/z tolerance of 10 ppm. Chromatogram deconvolution was done using ADAP algorithm, and chromatograms were deisotoped.…”

Section: Lc-hr-ms/ms Data Processing and Analysismentioning

confidence: 99%

Metabolic and metagenomic profiling of hydrocarbon-degrading microorganisms obtained from the deep biosphere of the Gulf of México

Moreno‐Ulloa

Vs²,

et al. 2019

Preprint

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Marine microbes are capable of degrading hydrocarbons; however, those inhabiting the deep biosphere (>1000 m) remain largely unexplored. Microbial metabolism could lead to the generation of diverse chemistries (some with therapeutic activities), but the impact of using hydrocarbons as the sole source of microbial energy in the synthesis of metabolites, remains obscure. Here, we investigated the metagenomic and metabolomic profile of two deep-marine sediments (>1 200 m deep, designated as A7 and B18) collected from the Gulf of México (GM) when grown for 28 days with a simple mixture of 4 hydrocarbons and complex hydrocarbon mixture (petroleum API 40) as their sole source of energy. State of the art techniques and analysis (e.g., Global Natural Products Social Molecular Networking, network annotation propagation [NAP], and MS2LDA) were used to describe the chemistries associated to the microbial utilization of hydrocarbons. The metagenomic sequencing analysis suggests a predominant abundance of Proteobacteria in environmental and API 40-enriched samples, while the abundance of Pseudomonas increased after microbial growth with API 40. The metabolomic analysis suggests the presence of diverse chemistries predominantly associated with lipid and lipid-like and phenyl propanoids and polyketides superclass (Classyfire annotation). Hydrocarbon derivatives were detected as carboxylic acids (e.g., azelaic and sebacic acid) or alcohols, while non-hydrocarbon related chemistries were also detected including tetracycline-related metabolites and sphinganines. Our study provides valuable chemical and microbiological information of microbes inhabiting one of the most understudied ecosystems in the earth, the deep marine biosphere.

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Section: Lc-hr-ms/ms Data Processing and Analysismentioning

confidence: 99%

Metabolic and metagenomic profiling of hydrocarbon-degrading microorganisms obtained from the deep biosphere of the Gulf of México

Moreno‐Ulloa

Vs²,

et al. 2019

Preprint

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“…We started the workflow development by defining the problems of peak detection. Myers et al have shown, that, when applied to the same dataset, different peak picking algorithms return two different yet overlapping peak sets [9,13]. To assess the performance of a peak picking algorithm, we must also consider the true peak set, which is unknown.…”

Section: Methodsmentioning

confidence: 99%

“…Instead, this is commonly achieved by using one of a number of software options on the market e.g. XCMS [5], metaMS [6], MetAlign [7], mzMine [8], ADAP-GC [9,10], PyMS [11] and eRah [12].…”

Section: Introductionmentioning

confidence: 99%

“…It is commonly understood that there are still certain conditions in which these automated methods are sub-optimal and the user-defined settings and some of the hard coding in the software will have a large impact on the results [13]. However, each tends to have its strengths and weaknesses and will result in a slightly different result [9,13]. In this study, we sought to optimize the strengths of each while minimizing the weaknesses.…”

Section: Introductionmentioning

confidence: 99%

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WiPP: Workflow for improved Peak Picking for Gas Chromatography-Mass Spectrometry (GC-MS) data

Borgsmüller

Gloaguen

Opialla

et al. 2019

Preprint

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High false positive rates in GC-MS metabolomics peak detection is a common issue that impedes automated analysis of large-scale datasets. There is a growing need for improving the reliability and scalability of data analysis workflows. Many algorithms are available for peak detection [1], a crucial step for the data analysis, but performance and outcome can differ widely depending on both algorithmic approach and data acquisition method. This makes it difficult to compare and contrast between algorithms without extensive manual intervention.We present a workflow for improved peak picking (WiPP), a parameter optimizing, multi-algorithm peak detection workflow for GC-MS metabolomics, which automatically evaluates the quality of detected peaks using machine learning-based classification. First, the classifier is trained to distinguish between real compound related peaks and false positive peaks. Then the algorithm parameters are scored based on the quality of detected peaks and optimized accordingly. This procedure is repeated for two peak detection algorithms and subsequently both algorithms are run in parallel on the entire data set with the optimized parameters. The qualitative information returned by the classifier for every peak is then used to merge individual algorithm results into one final high confidence peak set.Using this approach, we show that automated detection and evaluation of peak quality is improved. The additional quantitative and qualitative information generated by the classifier allows: 1. a novel way to classify peaks based on seven classes and thus objectively to assess their quality 2. impartial performance comparison of different peak picking algorithms 3. automated parameter optimization for each individual peak picking algorithm 4. a final, improved high quality peak list to be generated for statistical or further analyses.It achieves this while minimising the operator-time required by packaging this within a fully automated workflow. The modular design allows extension, adjustment and improvement of the workflow using different or additional peak detection algorithms and classifiers. Importantly, due to the fully automated implementation, the workflow is suitable for large-scale studies.The pipeline supports mzML, mzData and NetCDF formats and is implemented in python using snakemake, a reproducible and scalable workflow management system, it is available on GitHub (https://github.com/bihealth/WiPP).

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“…The converted dataset can be processed with various peak picking software tools, such as MZmine 19,21 , XCMS 20 , or OpenMS 25 . Due to its open-source modular framework, MZmine 2 has seen multiple extensions implemented by various different laboratories in the past years, which include feature detection algorithms [26][27][28] , molecular networking 29,30 , visualization techniques 31,32 , as well as compound identification algorithms 33,34 , making the overall toolbox almost ready for GC-TOF MS data mining of complex archaeological sample sets, as recently shown by Decq et al 35 . Since electron ionisation (EI) results in numerous fragments, which provide information about the molecular structure, spectra matching was the choice for compound identification.…”

Section: Optimization Of a Lc-ms Metabolomics Data Mining Workflow Fomentioning

confidence: 99%

Digging deeper - A new data mining workflow for improved processing and interpretation of high resolution GC-Q-TOF MS data in archaeological research

Korf

Hammann

Schmid

et al. 2020

Sci Rep

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Gas chromatography-mass spectrometry profiling is the most established method for the analysis of organic residues, particularly lipids, from archaeological contexts. This technique allows the decryption of hidden chemical information associated with archaeological artefacts, such as ceramic pottery fragments. The molecular and isotopic compositions of such residues can be used to reconstruct past resource use, and hence address major questions relating to patterns of subsistence, diet and ritual practices in the past. A targeted data analysis approach, based on previous findings reported in the literature is common but greatly depends on the investigator's prior knowledge of specific compound classes and their mass spectrometric behaviour, and poses the risk of missing unknown, potentially diagnostic compounds. Organic residues from post-prehistoric archaeological samples often lead to highly complex chromatograms, which makes manual chromatogram inspection very tedious and time consuming, especially for large datasets. This poses a significant limitation regarding the scale and interpretative scopes of such projects. Therefore, we have developed a non-targeted data mining workflow to extract a higher number of known and unknown compounds from the raw data to reduce investigator's bias and to vastly accelerate overall analysis time. The workflow covers all steps from raw data handling, feature selection, and compound identification up to statistical interpretation.

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One Step Forward for Reducing False Positive and False Negative Compound Identifications from Mass Spectrometry Metabolomics Data: New Algorithms for Constructing Extracted Ion Chromatograms and Detecting Chromatographic Peaks

Cited by 315 publications

References 33 publications

Metabolic and metagenomic profiling of hydrocarbon-degrading microorganisms obtained from the deep biosphere of the Gulf of México

Metabolic and metagenomic profiling of hydrocarbon-degrading microorganisms obtained from the deep biosphere of the Gulf of México

WiPP: Workflow for improved Peak Picking for Gas Chromatography-Mass Spectrometry (GC-MS) data

Digging deeper - A new data mining workflow for improved processing and interpretation of high resolution GC-Q-TOF MS data in archaeological research

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