Evaluating LC-HRMS metabolomics data processing software using FAIR principles for research software

Du, Xinsong; Dastmalchi, Farhad; Hao, Yue; Garrett, Timothy J.; Diller, Matthew; Liu, Mei; Hogan, William R.; Brochhausen, Mathias; Lemas, Dominick J.

doi:10.1007/s11306-023-01974-3

Cited by 10 publications

(12 citation statements)

References 125 publications

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“…A recent review regarding the FAIRness (Findability, Accessibility, Interoperability, Reusability) of LC−HRMS metabolomics software indicates that no software for LC−MS metabolomics data processing includes semantic annotation of input, output, or operations in their documentation, which considerably diminishes FAIRness. 6 Using our proposed strategy for workflow composition will encourage research software developers to annotate their software semantically so that this would be discovered by the AWC system and used in research workflows. Additionally, workflow reproducibility and reliability can be mitigated by improving workflow completeness and stability.…”

Section: Discussionmentioning

confidence: 99%

“…LC–MS metabolomics data processing consists of signal processing techniques such as noise filtering and peak deconvolution . To support these analyses, freely available software has been developed, including XCMS, MZmine, and MS-DIAL . A primary challenge to reproducibility of LC–MS metabolomics data processing workflows is workflow decay, , which is defined as the computational failure or reduced ability to execute or repeat a computational procedure over time .…”

Section: Introductionmentioning

confidence: 99%

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An Automated Workflow Composition System for Liquid Chromatography–Mass Spectrometry Metabolomics Data Processing

Du,

Dastmalchi,

Diller

et al. 2023

J. Am. Soc. Mass Spectrom.

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Liquid chromatography−mass spectrometry (LC− MS) metabolomics studies produce high-dimensional data that must be processed by a complex network of informatics tools to generate analysis-ready data sets. As the first computational step in metabolomics, data processing is increasingly becoming a challenge for researchers to develop customized computational workflows that are applicable for LC−MS metabolomics analysis. Ontology-based automated workflow composition (AWC) systems provide a feasible approach for developing computational workflows that consume high-dimensional molecular data. We used the Automated Pipeline Explorer (APE) to create an AWC for LC− MS metabolomics data processing across three use cases. Our results show that APE predicted 145 data processing workflows across all the three use cases. We identified six traditional workflows and six novel workflows. Through manual review, we found that onethird of novel workflows were executable whereby the data processing function could be completed without obtaining an error. When selecting the top six workflows from each use case, the computational viable rate of our predicted workflows reached 45%. Collectively, our study demonstrates the feasibility of developing an AWC system for LC−MS metabolomics data processing.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Automated Workflow Composition System for Liquid Chromatography–Mass Spectrometry Metabolomics Data Processing

Du,

Dastmalchi,

Diller

et al. 2023

J. Am. Soc. Mass Spectrom.

Self Cite

View full text Add to dashboard Cite

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“…Many funding bodies require a DMP, and in such cases a draft DMP should be defined in the proposal, then formalized during the project (before data/metadata collection begins), thus encompassing the entire RDM strategy of the project (Figure 1a). (a) DMPs document RDM practices for data/metadata; (b) Although DMPs encompass reusable standardized RDM practices [1], the standardization of the DMP content is not as evident as the standardization of RDM practices and data/metadata between projects [5,6].…”

Section: Introductionmentioning

confidence: 99%

DataPLAN: a web-based data management plan generator for the plant sciences

Zhou,

Beier,

Brilhaus

et al. 2023

Preprint

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Research Data Management (RDM) is a system for the effective handling of research data that enables scientists to structure their research questions and ensure best practices throughout the data lifecycle, from acquisition, computation and annotation to data publication and re-use. Data management plans (DMPs) are documents that formally set out the RDM of a project and are required by many funding bodies. DMPs help to organize and structure RDM strategies, thus promoting data findability, accessibility, interoperability and reusability (FAIR). Although DMPs incorporate methods and standards that can be reused by different research projects, the standardization of DMP content is not as evident as the standardization of RDM practices and data/metadata. To address this issue in the plant sciences, we developed DataPLAN — a tool that combines a questionnaire with pre-written standardized responses. We wrapped the questionnaire in a serverless single-page web application that can then generate standardized responses from DMP templates. The current templates cater to plant research grant proposals for Horizon 2020, Horizon Europe and the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG). In the future the range of templates will be extended to accommodate other funding schemes, thereby enabling more users to generate their own templates. The DataPLAN web application is open-source and does not require an internet connection. By utilizing DataPLAN, the workload associated with creating, updating, and adhering to DMPs is significantly reduced.

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“…Given the lack of need for proprietary software to export pixel-by-pixel data, an individual feature list provides MALDI-MS instrument/application flexibility. Additionally, considering each pixel data as a replicate from a defined ROI, exported pixel data can also be used for multivariate spatial metabolomics data analysis using either vendor-specific or non-proprietary metabolomics data software platforms [27][28][29][30][31][32][33][34][35][36][37][38][39] . Figure S1 shows an exemplary conversion of ROI pixel data to formats compatible with import into MetaboAnlayst 27 and Agilent Mass Hunter Professional, allowing for multivariate statistical analysis and biomarker discovery.…”

mentioning

confidence: 99%

“…Additionally, considering each pixel as a replicate from a defined ROI, exported pixel data can be readily used with either vendor-specific or non-proprietary metabolomics data software platforms for biomarker discovery and multivariate spatial metabolomics analysis such as principal component analysis (PCA), partial least-squares discriminant analysis (PLS-DA), hierarchical clustering (HCA), ROC curve assessment, VIP scoring, and pathway enrichment. [27][28][29][30][31][32][33][34][35][36][37][38][39] . Figure S1 shows an exemplary conversion of ROI pixel data to formats compatible with import into MetaboAnlayst 27 and Agilent Mass Hunter Professional.…”

mentioning

confidence: 99%

Untargeted Pixel-by-Pixel Imaging of Metabolite Ratio Pairs as a Novel Tool for Biomedical Discovery in Mass Spectrometry Imaging

Cheng,

Miller,

Southwell

et al. 2024

Preprint

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Mass spectrometry imaging (MSI) is a powerful technology that can be employed to define the spatial distribution and relative abundance of structurally identified and yet-undefined metabolites across a tissue cryosection. While numerous software packages enable pixel-by-pixel imaging of individual metabolite distributions, the research community lacks a discovery tool that provides spatial imaging of all metabolite abundance ratio pairs. Importantly, the recognition of correlating metabolite pairs offers a strategy to discover unanticipated molecules that contribute to or regulate a shared metabolic pathway, uncover hidden metabolic heterogeneity across cells and tissue subregions, and offers a single timepoint indicator of flux through a particular metabolic pathway of interest. Here, we describe the development and implementation of an untargeted R package workflow for pixel-by-pixel imaging of ratios for all metabolites detected in an MSI experiment. Considering untargeted MSI studies of murine brain and embryogenesis, we demonstrate that ratio imaging offers the opportunity to minimize systematic data variation introduced during sample handling or due to instrument drift, markedly enhances spatial image resolution, and can serve to reveal previously unrecognized tissue regions that are metabotype-distinct. Furthermore, ratio imaging facilitates the discovery of novel regional biomarkers, and can provide anatomical information regarding the spatial distribution of metabolite-linked biochemical pathways. The algorithm described herein is generic and can be applied to any MSI dataset containing spatial information for metabolites, peptides or proteins. Importantly, this software package offers a powerful add-on tool that can significantly enhance knowledge obtained from currently employed spatial metabolite profiling technologies.

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Evaluating LC-HRMS metabolomics data processing software using FAIR principles for research software

Cited by 10 publications

References 125 publications

An Automated Workflow Composition System for Liquid Chromatography–Mass Spectrometry Metabolomics Data Processing

An Automated Workflow Composition System for Liquid Chromatography–Mass Spectrometry Metabolomics Data Processing

DataPLAN: a web-based data management plan generator for the plant sciences

Untargeted Pixel-by-Pixel Imaging of Metabolite Ratio Pairs as a Novel Tool for Biomedical Discovery in Mass Spectrometry Imaging

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