Non-targeted UHPLC-MS metabolomic data processing methods: a comparative investigation of normalisation, missing value imputation, transformation and scaling

Guida, Riccardo Di; Engel, Jasper; Allwood, J. William; Weber, Ralf J. M.; Jones, Steven J.M.; Sommer, Ulf; Viant, Mark R.; Dunn, Warwick B.

doi:10.1007/s11306-016-1030-9

Cited by 272 publications

(239 citation statements)

References 49 publications

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“…They used what would now (see e.g. [201][202][203][204][205]) be referred to as 'untargeted metabolomics' to determine the differential uptake of substances from pooled serum when it was incubated with HEK293 cells either lacking measurable amounts, or containing cloned-up levels, of SLC22A4. A specific mass of m/z 144.84 was detected as being particularly taken up in the transporter-containing cells, and this was identified as the dipeptide proline betaine (aka stachydrine), a characteristic constituent of citrus fruits and their juices [206][207][208].…”

Section: Figure 3 An Untargeted Metabolomics Strategy For Determininmentioning

confidence: 99%

Control of metabolite efflux in microbial cell factories: current advances and future prospects

Kell¹

2018

Preprint

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The yield and ease of purification of biotechnological products are typically greatly enhanced if they can be secreted into the external medium. Although not universally appreciated, however, small molecule biotechnological products do not ‘float across’ the phospholipid bilayer portion of biological membranes, and thus they need transporters to assist their passage into the extramembrane and extracellular spaces. Some of these transporters may be reversible, equilibrative transporters that might more normally be used for uptake, while others may have an efflux directionality imposed on them via suitable energy coupling mechanisms. Despite the energetic costs of small molecule synthesis, natural evolution does in fact provide a number of mechanisms by which the secretion of such products can actually enhance fitness. Where available these provide useful starting points, and assaying for such activities is crucial. In particular, in a systems biology approach, we first need to identify such/suitable activities before we can seek to increase them. They may then be improved through promoter engineering, via manipulation of control elements, or by directed evolution of the transporter proteins themselves. Modern methods of synthetic biology provide enormous opportunities for all kinds of efflux transporter engineering; they are just beginning to be realised.

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Section: Figure 3 An Untargeted Metabolomics Strategy For Determininmentioning

confidence: 99%

Control of metabolite efflux in microbial cell factories: current advances and future prospects

Kell¹

2018

Preprint

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“…Different software are available; vendor specific and commercial software, freeware like XCMS (Smith et al 2006) and mzMine (Katajamaa et al 2006) are often used. No single tool is predominant throughout the metabolomics community, and remarkably, data pre-processing methods are not always reported (Di Guida et al 2016).…”

Section: Data Pre-processingmentioning

confidence: 99%

“…Di Guida et al provide a thorough background on each of these steps along with recommendations depending on the aim of further data analysis (Di Guida et al 2016), while Veselkov et al use a more mathematical approach (Veselkov et al 2011). Here, we briefly touch on each of these processes.…”

Section: Data Pre-processingmentioning

confidence: 99%

LC–MS based global metabolite profiling: the necessity of high data quality

et al. 2016

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LC-MS based global metabolite profiling currently lacks detailed guidelines to demonstrate that the obtained data is of high enough analytical quality. Insufficient data quality may result in the failure to generate a hypothesis, or in the worst case, a false or skewed hypothesis. After assessing the literature, it is apparent that an analytically focused summary and critical discussion related to this subject would be beneficial for both beginners and experts engaged in this field. A particular focus will be placed on data quality, which we here define as the degree to which a set of parameters fulfills predetermined criteria, similar to the established guidelines for targeted analysis. However, several of these parameters are difficult to assess since holistic approaches measure thousands of metabolites in parallel and seldom include predefined knowledge of which metabolites will differ between sample groups. In this review, the following parameters will be discussed in detail: reproducibility, selectivity, certainty of metabolite identification and metabolite coverage. The review systematically describes the generic workflow for LC-MS based global metabolite profiling and highlights how each separate part may affect data quality. The last part of the review describes how data quality can be evaluated as well as identifies areas where additional improvement is needed. In this review, we provide our own analytical opinions in regards to evaluation and, to some extent, improvement of data quality.

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“…It has been estimated that approximately 100-1000 metabolites are recorded in a usual non-targeted metabolome data acquisition experiment. 24,25) …”

Section: Targeted Vs Non-targeted Metabolo-micsmentioning

confidence: 99%

“…A sophisticated data processing so ware has been developed based on the smart peak picking, normalization, missing value imputation, transformation, scaling, and multivariate analysis methods (such as Traverse MS by Reifycs, https://www.reifycs.com/). 25,63,64) e visualization environment and interpretation tools such as VANTED (https://immersive-analytics.infotech.monash.edu/vanted/) and metabolite set enrichment analysis via Metaboanalyst Web page (http://www.metaboanalyst.ca/) are also available for metabolome data. [65][66][67] In addition to being an important tool of the biological analysis, metabolomics has the potential of becoming an essential part of the transomics analysis that integrates other omics such as genomics, transcriptomics, and proteomics.…”

Section: Killer Application Of Metabolomics: Biomarker Discovery and mentioning

confidence: 99%

Technical Challenges in Mass Spectrometry-Based Metabolomics

Mizutani

2016

Mass Spectrometry

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Metabolomics is a strategy for analysis, and quanti cation of the complete collection of metabolites present in biological samples. Metabolomics is an emerging area of scienti c research because there are many application areas including clinical, agricultural, and medical researches for the biomarker discovery and the metabolic system analysis by employing widely targeted analysis of a few hundred preselected metabolites from 10-100 biological samples. Further improvement in technologies of mass spectrometry in terms of experimental design for larger scale analysis, computational methods for tandem mass spectrometry-based elucidation of metabolites, and speci c instrumentation for advanced bioanalysis will enable more comprehensive metabolome analysis for exploring the hidden secrets of metabolism.

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Non-targeted UHPLC-MS metabolomic data processing methods: a comparative investigation of normalisation, missing value imputation, transformation and scaling

Cited by 272 publications

References 49 publications

Control of metabolite efflux in microbial cell factories: current advances and future prospects

Control of metabolite efflux in microbial cell factories: current advances and future prospects

LC–MS based global metabolite profiling: the necessity of high data quality

Technical Challenges in Mass Spectrometry-Based Metabolomics

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