Xconnector: Retrieving and visualizing metabolites and pathways information from various database resources

Anwar, Ali Mostafa; Ahmed, Eman; Soudy, Mohamed; Osama, Aya; Ezzeldin, Shahd; Tanios, Anthony; Mahgoub, Sebaey; Magdeldin, Sameh

doi:10.1016/j.jprot.2021.104302

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…However, some pathways have different significance and enrichment statuses when analyzed against different databases. Several researchers have noticed this feature, which is usually associated with the unique content of pathways [ 35 , 36 , 37 ].…”

Section: Resultsmentioning

confidence: 99%

Exploring Dynamic Metabolome of the HepG2 Cell Line: Rise and Fall

Kiseleva

Kurbatov

Arzumanian

et al. 2022

Cells

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Both biological and technical variations can discredit the reliability of obtained data in omics studies. In this technical note, we investigated the effect of prolonged cultivation of the HepG2 hepatoma cell line on its metabolomic profile. Using the GC × GC-MS approach, we determined the degree of metabolic variability across HepG2 cells cultured in uniform conditions for 0, 5, 10, 15, and 20 days. Post-processing of obtained data revealed substantial changes in relative abundances of 110 metabolites among HepG2 samples under investigation. Our findings have implications for interpreting metabolomic results obtained from immortal cells, especially in longitudinal studies. There are still plenty of unanswered questions regarding metabolomics variability and many potential areas for future targeted and panoramic research. However, we suggest that the metabolome of cell lines is unstable and may undergo significant transformation over time, even if the culture conditions remain the same. Considering metabolomics variability on a relatively long-term basis, careful experimentation with particular attention to control samples is required to ensure reproducibility and relevance of the research results when testing both fundamentally and practically significant hypotheses.

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Section: Resultsmentioning

confidence: 99%

Exploring Dynamic Metabolome of the HepG2 Cell Line: Rise and Fall

Kiseleva

Kurbatov

Arzumanian

et al. 2022

Cells

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“…At the metadata level, common vocabulary and consensual description levels in data collecting steps are required (Alseekh et al, 2021 ). At the computing level, common formats and application programming interfaces (API) are needed to enable data exchange between databases and connect databases to data treatment tools (Anwar et al, 2021 ; Merlet et al, 2016 ). Recent calls in the metabolomics community emphasize the need to develop adapted informatics infrastructures for laboratories based on FAIR principles in order to improve the exchange and interoperability of annotations from one laboratory to another and the sharing (and inclusion) of local spectral libraries with reference databases (Haug et al, 2017 ; Sansone et al, 2012 ).…”

Section: Introductionmentioning

confidence: 99%

PeakForest: a multi-platform digital infrastructure for interoperable metabolite spectral data and metadata management

et al. 2022

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Introduction Accuracy of feature annotation and metabolite identification in biological samples is a key element in metabolomics research. However, the annotation process is often hampered by the lack of spectral reference data in experimental conditions, as well as logistical difficulties in the spectral data management and exchange of annotations between laboratories. Objectives To design an open-source infrastructure allowing hosting both nuclear magnetic resonance (NMR) and mass spectra (MS), with an ergonomic Web interface and Web services to support metabolite annotation and laboratory data management. Methods We developed the PeakForest infrastructure, an open-source Java tool with automatic programming interfaces that can be deployed locally to organize spectral data for metabolome annotation in laboratories. Standardized operating procedures and formats were included to ensure data quality and interoperability, in line with international recommendations and FAIR principles. Results PeakForest is able to capture and store experimental spectral MS and NMR metadata as well as collect and display signal annotations. This modular system provides a structured database with inbuilt tools to curate information, browse and reuse spectral information in data treatment. PeakForest offers data formalization and centralization at the laboratory level, facilitating shared spectral data across laboratories and integration into public databases. Conclusion PeakForest is a comprehensive resource which addresses a technical bottleneck, namely large-scale spectral data annotation and metabolite identification for metabolomics laboratories with multiple instruments. PeakForest databases can be used in conjunction with bespoke data analysis pipelines in the Galaxy environment, offering the opportunity to meet the evolving needs of metabolomics research. Developed and tested by the French metabolomics community, PeakForest is freely-available at https://github.com/peakforest.

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