Evaluation of significant features discovered from different data acquisition modes in mass spectrometry-based untargeted metabolomics

Guo, Jian; Huan, Tao

doi:10.1016/j.aca.2020.08.065

Cited by 23 publications

(27 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The performance of EVA was first demonstrated using metabolomics data generated at different MS spectra acquisition rates. In principle, a higher MS spectra acquisition rate leads to fewer metabolic features in LC-MS-based metabolomics . This is because increased MS spectra acquisition rate leads to reduced spectral summation and in turn reduces MS signal intensity and sensitivity.…”

Section: Resultsmentioning

confidence: 99%

“…In principle, a higher MS spectra acquisition rate leads to fewer metabolic features in LC-MS-based metabolomics. 21 This is because increased MS spectra acquisition rate leads to reduced spectral summation and in turn reduces MS signal intensity and sensitivity. On the other side, higher MS spectra acquisition rate also causes more jagged EIC peak shape (Figure S-1), thus, more low-quality features may be extracted.…”

Section: ■ Introductionmentioning

confidence: 99%

See 1 more Smart Citation

EVA: Evaluation of Metabolic Feature Fidelity Using a Deep Learning Model Trained With Over 25000 Extracted Ion Chromatograms

et al. 2021

Self Cite

View full text Add to dashboard Cite

Extracting metabolic features from liquid chromatography−mass spectrometry (LC-MS) data relies on the recognition of extracted ion chromatogram (EIC) peak shapes using peak picking algorithms. Unfortunately, all peak picking algorithms present a significant drawback of generating a problematic number of false positives. In this work, we take advantage of deep learning technology to develop a convolutional neural network (CNN)-based program that can automatically recognize metabolic features with poor EIC shapes, which are of low feature fidelity and more likely to be false. Our CNN model was trained using 25095 EIC plots collected from 22 LC-MS-based metabolomics projects of various sample types, LC and MS conditions. Notably, we manually inspected all the EIC plots to assign good or poor EIC quality for accurate model training. The trained CNN model is embedded into a C#-based program, named EVA (short for evaluation). The EVA Windows Application is a versatile platform that can process metabolic features generated by LC-MS systems of various vendors and processed using various data processing software. Our comprehensive evaluation of EVA indicates that it achieves over 90% classification accuracy. EVA can be readily used in LC-MS-based metabolomics projects and is freely available on the Microsoft Store by searching "EVA Metabolomics".

show abstract

Section: Resultsmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

EVA: Evaluation of Metabolic Feature Fidelity Using a Deep Learning Model Trained With Over 25000 Extracted Ion Chromatograms

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Another study that compared the full-scan, data-dependent acquisition (DDA), and data-independent acquisition (DIA) methods in HR LC–MS/MS based metabolomics to reveal that spectra quality is better in DDA with average dot product score 83.1% higher than DIA and the number of MS 2 spectra (spectra quantity) is larger in DIA (Guo & Huan, 2020a ). Furthermore, it was shown that DDA mode consistently generated fewer uniquely found significant features than full-scan and DIA modes (Guo & Huan, 2020b ).…”

Section: Introductionmentioning

confidence: 99%

New software tools, databases, and resources in metabolomics: updates from 2020

Misra

2021

Metabolomics

152

View full text Add to dashboard Cite

Background Precision medicine, space exploration, drug discovery to characterization of dark chemical space of habitats and organisms, metabolomics takes a centre stage in providing answers to diverse biological, biomedical, and environmental questions. With technological advances in mass-spectrometry and spectroscopy platforms that aid in generation of information rich datasets that are complex big-data, data analytics tend to co-evolve to match the pace of analytical instrumentation. Software tools, resources, databases, and solutions help in harnessing the concealed information in the generated data for eventual translational success. Aim of the review In this review, ~ 85 metabolomics software resources, packages, tools, databases, and other utilities that appeared in 2020 are introduced to the research community. Key scientific concepts of review In Table 1 the computational dependencies and downloadable links of the tools are provided, and the resources are categorized based on their utility. The review aims to keep the community of metabolomics researchers updated with all the resources developed in 2020 at a collated avenue, in line with efforts form 2015 onwards to help them find these at one place for further referencing and use.

show abstract

“…LC-MS-based metabolomics has also become a critical analysis strategy for studying the exposome, defined as the totality of environmental exposures that drive human health and disease [ 4 , 5 , 6 ]. In particular, MS operated in data-dependent acquisition (DDA) mode offers autonomous collection of both MS 1 and MS 2 spectra, allowing for simultaneous quantitative comparison and metabolite annotation [ 7 , 8 , 9 ]. State-of-the-art LC-MS systems are very sensitive and can generate a large amount of metabolic information, including thousands of MS 1 scans that contain tens of thousands of unique m/z values.…”

Section: Introductionmentioning

confidence: 99%

JPA: Joint Metabolic Feature Extraction Increases the Depth of Chemical Coverage for LC-MS-Based Metabolomics and Exposomics

et al. 2022

Self Cite

View full text Add to dashboard Cite

Extracting metabolic features from liquid chromatography-mass spectrometry (LC-MS) data has been a long-standing bioinformatic challenge in untargeted metabolomics. Conventional feature extraction algorithms fail to recognize features with low signal intensities, poor chromatographic peak shapes, or those that do not fit the parameter settings. This problem also poses a challenge for MS-based exposome studies, as low-abundant metabolic or exposomic features cannot be automatically recognized from raw data. To address this data processing challenge, we developed an R package, JPA (short for Joint Metabolomic Data Processing and Annotation), to comprehensively extract metabolic features from raw LC-MS data. JPA performs feature extraction by combining a conventional peak picking algorithm and strategies for (1) recognizing features with bad peak shapes but that have tandem mass spectra (MS2) and (2) picking up features from a user-defined targeted list. The performance of JPA in global metabolomics was demonstrated using serial diluted urine samples, in which JPA was able to rescue an average of 25% of metabolic features that were missed by the conventional peak picking algorithm due to dilution. More importantly, the chromatographic peak shapes, analytical accuracy, and precision of the rescued metabolic features were all evaluated. Furthermore, owing to its sensitive feature extraction, JPA was able to achieve a limit of detection (LOD) that was up to thousands of folds lower when automatically processing metabolomics data of a serial diluted metabolite standard mixture analyzed in HILIC(−) and RP(+) modes. Finally, the performance of JPA in exposome research was validated using a mixture of 250 drugs and 255 pesticides at environmentally relevant levels. JPA detected an average of 2.3-fold more exposure compounds than conventional peak picking only.

show abstract

Evaluation of significant features discovered from different data acquisition modes in mass spectrometry-based untargeted metabolomics

Cited by 23 publications

References 23 publications

EVA: Evaluation of Metabolic Feature Fidelity Using a Deep Learning Model Trained With Over 25000 Extracted Ion Chromatograms

EVA: Evaluation of Metabolic Feature Fidelity Using a Deep Learning Model Trained With Over 25000 Extracted Ion Chromatograms

New software tools, databases, and resources in metabolomics: updates from 2020

JPA: Joint Metabolic Feature Extraction Increases the Depth of Chemical Coverage for LC-MS-Based Metabolomics and Exposomics

Contact Info

Product

Resources

About