Warpgroup: increased precision of metabolomic data processing by consensus integration bound analysis

Mahieu, Nathaniel G.; Spalding, Jonathan L.; Patti, Gary J.

doi:10.1093/bioinformatics/btv564

Cited by 26 publications

(31 citation statements)

References 28 publications

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“…Warpgroup is an XCMS compatible package that addresses these limitations with consensus integration bound analysis. [30] Warpgroup applies dynamic time warping and graph analysis to improve the precision of metabolomic data processing. Warpgroup improvements include: correspondence determination that leverages the local extracted ion chromatogram topography; detection and grouping of peak subregions; selection of similar integration bounds for each group; intelligent missing value filling; and reporting of several parameters which allow the filtering of bioinformatic noise.…”

Section: Warpgroup: Improving Quantitation With Consensus Integrationmentioning

confidence: 99%

“…For an E. coli dataset, as an example, application of Warpgroup resulted in an increase in the number of unique detected analytes by 26% and halved the mean coefficient of variation of all analytes (compared to the XCMS algorithms alone). [30] Warpgroup is implemented in a general manner and is applicable to all time series data, including metabolomic data from other software packages.…”

Section: Warpgroup: Improving Quantitation With Consensus Integrationmentioning

confidence: 99%

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A roadmap for the XCMS family of software solutions in metabolomics

Mahieu

Genenbacher

Patti

2016

Current Opinion in Chemical Biology

120

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Global profiling of metabolites in biological samples by liquid chromatography/mass spectrometry results in datasets too large to evaluate manually. Fortunately, a variety of software programs are now available to automate the data analysis. Selection of the appropriate processing solution is dependent upon experimental design. Most metabolomic studies a decade ago had a relatively simple experimental design in which the intensities of compounds were compared between only two sample groups. More recently, however, increasingly sophisticated applications have been pursued. Examples include comparing compound intensities between multiple sample groups and unbiasedly tracking the fate of specific isotopic labels. The latter types of applications have necessitated the development of new software programs, which have introduced additional functionalities that facilitate data analysis. The objective of this review is to provide an overview of the freely available bioinformatic solutions that are either based upon or are compatible with the algorithms in XCMS, which we broadly refer to here as the “XCMS family” of software. These include CAMERA, credentialing, Warpgroup, metaXCMS, X13CMS, and XCMS Online. Together, these informatic technologies can accommodate most cutting-edge metabolomic applications and offer some advantages when compared to the original XCMS program.

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Section: Warpgroup: Improving Quantitation With Consensus Integrationmentioning

confidence: 99%

Section: Warpgroup: Improving Quantitation With Consensus Integrationmentioning

confidence: 99%

A roadmap for the XCMS family of software solutions in metabolomics

Mahieu

Genenbacher

Patti

2016

Current Opinion in Chemical Biology

120

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“…As an internal control, various aliquots of the stock solution were mixed with extraction solvents prior to sample introduction. Extracted samples were separated with a Luna aminopropyl column (3 μm, 150 × 1.0 mm I.D., Phenomenex) and analyzed by an Agilent 6540 QTOF as previously described (Mahieu et al 2015). A hydrophilic interaction liquid chromatography separation was used instead of a reversed-phase separation to minimize carry over.…”

Section: Methodsmentioning

confidence: 99%

Inaccurate quantitation of palmitate in metabolomics and isotope tracer studies due to plastics

et al. 2016

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Introduction Palmitate, the typical end product released from fatty acid synthase, is of interest to many researchers performing metabolomics. Although palmitate can be readily detected by using mass spectrometry, many metabolomic platforms involve the use of plastic consumables that introduce a competing background signal of palmitate. Objectives The goal of this study was to quantify palmitate contamination in metabolomics and isotope tracer studies and to examine the reliability of approaches for reducing error. Methods We measured the quantitative error introduced by palmitate contamination from 4 vendors of plastic consumables used in combination with several different extraction solvents. Results The background palmitate signal was as much as sixfold higher than the biological palmitate signal from 4 million 3T3-L1 cells. Importantly, the palmitate contamination signal was highly variable between plastic consumables (even within the same lot) and therefore could not be accurately removed by subtracting the background as measured from a blank. In addition to affecting relative and absolute quantitation, the palmitate background signal from disposable plastics also led to the underestimation of labeled palmitate in isotope tracer experiments. Conclusion When measuring palmitate standard solutions, the best results were obtained when glass vials and glass pipettes were used. However, much of the palmitate background signal could be eliminated by pre-rinsing plastic vials and plastic pipette tips with methanol prior to sample introduction. For isotope tracer studies, error could also be minimized by estimating palmitate enrichment from palmitoylcarnitine, which does not have a competing contamination signal from plastic consumables.

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“…43,47 Pairwise alignment is then completed by reference to the profile with maximum number of detected features, and all other profiles are aligned with respect to the reference in a pairwise fashion using methods such as dynamic time warping, ObiWarp, and kernel smoothing. 42,43,48 …”

Section: Feature Extraction Quality Assessment and Data Correctionmentioning

confidence: 99%

Computational Metabolomics: A Framework for the Million Metabolome

Uppal

Walker

Liu

et al. 2016

Chem. Res. Toxicol.

211

179

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“Sola dosis facit venenum.” These words of Paracelsus, “the dose makes the poison”, can lead to a cavalier attitude concerning potential toxicities of the vast array of low abundance environmental chemicals to which humans are exposed. Exposome research teaches that 80–85% of human disease is linked to environmental exposures. The human exposome is estimated to include >400,000 environmental chemicals, most of which are uncharacterized with regard to human health. In fact, mass spectrometry measures >200,000 m/z features (ions) in microliter volumes derived from human samples; most are unidentified. This crystallizes a grand challenge for chemical research in toxicology: to develop reliable and affordable analytical methods to understand health impacts of the extensive human chemical experience. To this end, there appears to be no choice but to abandon the limitations of measuring one chemical at a time. The present review looks at progress in computational metabolomics to provide probability based annotation linking ions to known chemicals and serve as a foundation for unambiguous designation of unidentified ions for toxicologic study. We review methods to characterize ions in terms of accurate mass m/z, chromatographic retention time, correlation of adduct, isotopic and fragment forms, association with metabolic pathways and measurement of collision-induced dissociation products, collision cross section, and chirality. Such information can support a largely unambiguous system for documenting unidentified ions in environmental surveillance and human biomonitoring. Assembly of this data would provide a resource to characterize and understand health risks of the array of low-abundance chemicals to which humans are exposed.

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Warpgroup: increased precision of metabolomic data processing by consensus integration bound analysis

Cited by 26 publications

References 28 publications

A roadmap for the XCMS family of software solutions in metabolomics

A roadmap for the XCMS family of software solutions in metabolomics

Inaccurate quantitation of palmitate in metabolomics and isotope tracer studies due to plastics

Computational Metabolomics: A Framework for the Million Metabolome

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