Comparison of peak‐picking workflows for untargeted liquid chromatography/high‐resolution mass spectrometry metabolomics data analysis

Rafiei, Atefeh; Sleno, Lekha

doi:10.1002/rcm.7094

Cited by 61 publications

(56 citation statements)

References 17 publications

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“…The purpose of this step is to extract signals in the dataset that arise from real compounds, while attempting to exclude signals resulting from various noise sources. [12] Extracted signals with a unique mass-to-charge ratio and retention time are recorded as features. (Figure 2A)…”

Section: Defining the Needs: A General Bioinformatic Workflowmentioning

confidence: 99%

A roadmap for the XCMS family of software solutions in metabolomics

Mahieu

Genenbacher

Patti

2016

Current Opinion in Chemical Biology

121

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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: Defining the Needs: A General Bioinformatic Workflowmentioning

confidence: 99%

A roadmap for the XCMS family of software solutions in metabolomics

Mahieu

Genenbacher

Patti

2016

Current Opinion in Chemical Biology

121

View full text Add to dashboard Cite

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“…Furthermore, even though the current study used only one method for peak detection (and varying its parameters), it should be noted that there are a wide variety of workflows (vendor-specific, commercial software, and freeware) available for peak picking/detection. Since various algorithms are used by these different peak detection methods, the choice thereof can have a significant influence on the processed end results of a study [14,33,78,79,80]. Hence, careful and thoughtful use of pre-processing and processing tools is mandatory to be able to make a critical judgment on the outcomes following those two essential steps in the metabolomics workflows.…”

Section: Discussionmentioning

confidence: 99%

A Conversation on Data Mining Strategies in LC-MS Untargeted Metabolomics: Pre-Processing and Pre-Treatment Steps

et al. 2016

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Untargeted metabolomic studies generate information-rich, high-dimensional, and complex datasets that remain challenging to handle and fully exploit. Despite the remarkable progress in the development of tools and algorithms, the “exhaustive” extraction of information from these metabolomic datasets is still a non-trivial undertaking. A conversation on data mining strategies for a maximal information extraction from metabolomic data is needed. Using a liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomic dataset, this study explored the influence of collection parameters in the data pre-processing step, scaling and data transformation on the statistical models generated, and feature selection, thereafter. Data obtained in positive mode generated from a LC-MS-based untargeted metabolomic study (sorghum plants responding dynamically to infection by a fungal pathogen) were used. Raw data were pre-processed with MarkerLynxTM software (Waters Corporation, Manchester, UK). Here, two parameters were varied: the intensity threshold (50–100 counts) and the mass tolerance (0.005–0.01 Da). After the pre-processing, the datasets were imported into SIMCA (Umetrics, Umea, Sweden) for more data cleaning and statistical modeling. In addition, different scaling (unit variance, Pareto, etc.) and data transformation (log and power) methods were explored. The results showed that the pre-processing parameters (or algorithms) influence the output dataset with regard to the number of defined features. Furthermore, the study demonstrates that the pre-treatment of data prior to statistical modeling affects the subspace approximation outcome: e.g., the amount of variation in X-data that the model can explain and predict. The pre-processing and pre-treatment steps subsequently influence the number of statistically significant extracted/selected features (variables). Thus, as informed by the results, to maximize the value of untargeted metabolomic data, understanding of the data structures and exploration of different algorithms and methods (at different steps of the data analysis pipeline) might be the best trade-off, currently, and possibly an epistemological imperative.

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“…The softwares use different algorithms to detect peaks in the raw data and therefore often recognize different features (Rafiei and Sleno 2014). Furthermore, the algorithms display different sensitivity to raw data quality (Bauer et al 2011), and the experimental settings in each tool may severely affect the peak-picking (Castillo et al 2011).…”

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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Comparison of peak‐picking workflows for untargeted liquid chromatography/high‐resolution mass spectrometry metabolomics data analysis

Cited by 61 publications

References 17 publications

A roadmap for the XCMS family of software solutions in metabolomics

A roadmap for the XCMS family of software solutions in metabolomics

A Conversation on Data Mining Strategies in LC-MS Untargeted Metabolomics: Pre-Processing and Pre-Treatment Steps

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

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