Comprehensive evaluation of untargeted metabolomics data processing software in feature detection, quantification and discriminating marker selection

Li, Zhucui; Lu, Yan; Guo, Yiting; Cao, Haijie; Wang, Qinhong; Wang, Shui

doi:10.1016/j.aca.2018.05.001

Cited by 125 publications

(116 citation statements)

References 33 publications

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“…As anticipated, we observed significant feature inflation in this mixture of 51 NP standards: 869 signals from PI and NI acquisition modes were detected (Figure 2). This approximately 95% feature inflation is consistent with a previous report of 10 000-30 000 features detected after injection of 900 unique metabolites 33 and with a study that used isotope labeling as a feature filtering approach 11 . Blank ratio filtering deleted 50% of the features and the other generic filters described above removed 15% of the remaining ones.…”

Section: Resultssupporting

confidence: 91%

“…We demonstrate here that feature degeneracy - the ocean of data - has a great impact on the final annotated peak list information, thus impacting the biological knowledge mined from untargeted metabolomic studies. We estimate, based on analysis of standard mixtures, that feature inflation is close to 95%, in agreement with other studies 33,11 . Our package MS-CleanR, with its a point-and-click software on a Shiny interface, is a new component in the suite of tools comprising the GUI software MS-DIAL and the annotation capabilities of MS-FINDER which altogether provide a comprehensive workflow, from raw data to final annotated peaklist.…”

Section: Discussionsupporting

confidence: 92%

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MS-CleanR: A feature-filtering approach to improve annotation rate in untargeted LC-MS based metabolomics

Fraisier-Vannier

Chervin

Cabanac

et al. 2020

Preprint

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5Untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS) is currently the euteiches; a group of glycosylated triterpenoids overrepresented in resistant lines were identified as 2 9candidate compounds conferring pathogen resistance. MS-CleanR is implemented through a Shiny 3 0 interface for intuitive use by end-users (available at: https://github.com/eMetaboHUB/MS-CleanR). 3 1

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Section: Resultssupporting

confidence: 91%

Section: Discussionsupporting

confidence: 92%

MS-CleanR: A feature-filtering approach to improve annotation rate in untargeted LC-MS based metabolomics

Fraisier-Vannier

Chervin

Cabanac

et al. 2020

Preprint

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“…Metabolomic analyses can be separated into targeted or untargeted approaches. A targeted metabolomic analysis investigates quantitative changes of predetermined metabolites using analytical standards, while an untargeted approach measures any metabolite above the detection limit within the metabolomic profile . Untargeted metabolomics has been used extensively for biomarker discovery and identification of disease‐related metabolic pathways.…”

Section: Investigating the Gut Microbiome With Metabolomicsmentioning

confidence: 99%

Metaproteomic and Metabolomic Approaches for Characterizing the Gut Microbiome

et al. 2019

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The gut microbiome has been shown to play a significant role in human healthy and diseased states. The dynamic signaling that occurs between the host and microbiome is critical for the maintenance of host homeostasis. Analyzing the human microbiome with metaproteomics, metabolomics, and integrative multi-omics analyses can provide significant information on markers for healthy and diseased states, allowing for the eventual creation of microbiome-targeted treatments for diseases associated with dysbiosis. Metaproteomics enables functional activity information to be gained from the microbiome samples, while metabolomics provides insight into the overall metabolic states affecting/representing the host-microbiome interactions. Combining these functional -omic platforms together with microbiome composition profiling allows for a holistic overview on the functional and metabolic state of the microbiome and its influence on human health. Here the benefits of metaproteomics, metabolomics, and the integrative multi-omic approaches to investigating the gut microbiome in the context of human health and diseases are reviewed.Samples collected from the human gut contains microbiome and host proteins. These microbiome/host proteins directly represent the functional activity of the intestinal ecosystem and can be simultaneously measured by metaproteomics. Metaproteomics can also provide valuable information on the pathways and taxaspecific functions, which provides a more in-depth perspective for studying detailed mechanisms underlying host-microbiome

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“…Although there exists a wide array of tools, they all produce very different results. For instance, in a recent study, where the authors evaluated five software on a benchmarked standard and common dataset (MS‐Dial, MZmine 2, XCMS, MarkerView, and Compound Discoverer), they reported various advantages and disadvantages of each of these tools in the detection of true features derived from compounds in the mixture . Various obstacles and corrective measures available in four major aspects associated with an untargeted MS‐based metabolomics experiment—(1) experimental design, (2) preanalytical (sample collection and preparation), (3) analytical (chromatography and detection), and (4) postanalytical (data processing)—have been discussed elsewhere .…”

Section: Tools For Analytical Platformsmentioning

confidence: 99%

Tools and resources for metabolomics research community: A 2017–2018 update

Misra¹,

Mohapatra

2018

Electrophoresis

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The scale at which MS‐ and NMR‐based platforms generate metabolomics datasets for both research, core, and clinical facilities to address challenges in the various sciences—ranging from biomedical to agricultural—is underappreciated. Thus, metabolomics efforts spanning microbe, environment, plant, animal, and human systems have led to continual and concomitant growth of in silico resources for analysis and interpretation of these datasets. These software tools, resources, and databases drive the field forward to help keep pace with the amount of data being generated and the sophisticated and diverse analytical platforms that are being used to generate these metabolomics datasets. To address challenges in data preprocessing, metabolite annotation, statistical interrogation, visualization, interpretation, and integration, the metabolomics and informatics research community comes up with hundreds of tools every year. The purpose of the present review is to provide a brief and useful summary of more than 95 metabolomics tools, software, and databases that were either developed or significantly improved during 2017–2018. We hope to see this review help readers, developers, and researchers to obtain informed access to these thorough lists of resources for further improvisation, implementation, and application in due course of time.

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Comprehensive evaluation of untargeted metabolomics data processing software in feature detection, quantification and discriminating marker selection

Cited by 125 publications

References 33 publications

MS-CleanR: A feature-filtering approach to improve annotation rate in untargeted LC-MS based metabolomics

MS-CleanR: A feature-filtering approach to improve annotation rate in untargeted LC-MS based metabolomics

Metaproteomic and Metabolomic Approaches for Characterizing the Gut Microbiome

Tools and resources for metabolomics research community: A 2017–2018 update

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