Xenometabolomics in Ecotoxicology: Concepts and Applications DOI
Phillip Ankley, Hannah Mahoney, Markus Brinkmann

et al.

Environmental Science & Technology, Journal Year: 2025, Volume and Issue: unknown

Published: April 22, 2025

Nontargeted high-resolution mass spectrometry (HRMS) allows for the characterization of a large fraction exposome, i.e., entirety chemicals an organism is exposed to, and helps detect important exogenous chemical compounds that could be key drivers toxicological impact. Along with these occur endogenous metabolites are essential health host organism. Chemical derived from biotransformation xenobiotics present in exposome referred to as xenometabolome, while endometabolome. Recent advancements HRMS technology allow detection features biological ecological importance context safety assessments unprecedented sensitivity resolution. In this perspective, we highlight application HRMS-based metabolomics organisms ecotoxicology, complexity comprehensively characterizing endometabolome, distinguishing xenometabolome.

Language: Английский

Wekemo Bioincloud: A user‐friendly platform for meta‐omics data analyses DOI Creative Commons
Yunyun Gao, Guo‐Xing Zhang,

Shunyao Jiang

et al.

iMeta, Journal Year: 2024, Volume and Issue: 3(1)

Published: Feb. 1, 2024

The increasing application of meta-omics approaches to investigate the structure, function, and intercellular interactions microbial communities has led a surge in available data. However, this abundance human environmental microbiome data exposed new scalability challenges for existing bioinformatics tools. In response, we introduce Wekemo Bioincloud-a specialized platform -omics studies. This offers comprehensive analysis solution, specifically designed alleviate tool selection users face expanding sets. As now, Bioincloud been regularly equipped with 22 workflows 65 visualization tools, establishing itself as user-friendly widely embraced studying diverse Additionally, enables online modification vector outputs, registration-independent personalized dashboard system ensures privacy traceability. is freely at https://www.bioincloud.tech/.

Language: Английский

Citations

92
Comprehensive investigation of pathway enrichment methods for functional interpretation of LC–MS global metabolomics data DOI
Yao Lü, Zhiqiang Pang, Jianguo Xia

et al.

Briefings in Bioinformatics, Journal Year: 2022, Volume and Issue: 24(1)

Published: Nov. 15, 2022

Global or untargeted metabolomics is widely used to comprehensively investigate metabolic profiles under various pathophysiological conditions such as inflammations, infections, responses exposures interactions with microbial communities. However, biological interpretation of global data remains a daunting task. Recent years have seen growing applications pathway enrichment analysis based on putative annotations liquid chromatography coupled mass spectrometry (LC-MS) peaks for functional LC-MS-based data. due intricate peak-metabolite and metabolite-pathway relationships, considerable variations are observed among results obtained using different approaches. There an urgent need benchmark these approaches inform the best practices.We conducted study common peak annotation methods in current studies. Representative approaches, including three four methods, were selected benchmarked scenarios. Based results, we provided set recommendations regarding annotation, ranking metrics feature selection. The overall better performance was mummichog approach. We that ~30% rate sufficient achieve high recall (~90% mummichog), semi-annotated improves interpretation. platforms further propose identifiability index indicate possibility being reliably identified. Finally, evaluated all 11 COVID-19 8 inflammatory bowel diseases (IBD) datasets.

Language: Английский

Citations

91
WebGestalt 2024: faster gene set analysis and new support for metabolomics and multi-omics DOI Creative Commons
John M Elizarraras, Yuxing Liao, Zhiao Shi

et al.

Nucleic Acids Research, Journal Year: 2024, Volume and Issue: 52(W1), P. W415 - W421

Published: May 29, 2024

Enrichment analysis, crucial for interpreting genomic, transcriptomic, and proteomic data, is expanding into metabolomics. Furthermore, there a rising demand integrated enrichment analysis that combines data from different studies omics platforms, as seen in meta-analysis multi-omics research. To address these growing needs, we have updated WebGestalt to include capabilities both metabolites multiple input lists of analytes. We also significantly increased speed, revamped the user interface, introduced new pathway visualizations accommodate updates. Notably, adoption Rust backend reduced gene set time by 95% 270.64 12.41 s network topology-based 89% 159.59 17.31 our evaluation. This performance improvement accessible R package newly Python package. Additionally, database reflect current status each source expanded collection pathways, networks, signatures. The 2024 update represents significant leap forward, offering support metabolomics, streamlined capabilities, remarkable enhancements. Discover updates more at https://www.webgestalt.org.

Language: Английский

Citations

75
Studying Metabolism by NMR-Based Metabolomics DOI Creative Commons
Sofia Moco

Frontiers in Molecular Biosciences, Journal Year: 2022, Volume and Issue: 9

Published: April 27, 2022

During the past few decades, direct analysis of metabolic intermediates in biological samples has greatly improved understanding processes. The most used technologies for these advances have been mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. NMR is traditionally to elucidate molecular structures now extended complex mixtures, as samples: NMR-based metabolomics. There are however other areas small molecule biochemistry which equally powerful. These include quantification metabolites (qNMR); use stable isotope tracers determine fate drugs or nutrients, unravelling new pathways, flux through pathways; metabolite-protein interactions regulation pharmacological effects. Computational tools resources automating spectra extracting meaningful biochemical information developed tandem contributes a more detailed systems biochemistry. In this review, we highlight contribution biochemistry, specifically studies by reviewing state-of-the-art methodologies spectroscopy future directions.

Language: Английский

Citations

71
Integrative multi-omics analysis of autism spectrum disorder reveals unique microbial macromolecules interactions DOI Creative Commons
Aya Osama, Ali Mostafa Anwar, Shahd Ezzeldin

et al.

Journal of Advanced Research, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

Citations

2
Nine quick tips for pathway enrichment analysis DOI Creative Commons
Davide Chicco, Giuseppe Agapito

PLoS Computational Biology, Journal Year: 2022, Volume and Issue: 18(8), P. e1010348 - e1010348

Published: Aug. 11, 2022

Pathway enrichment analysis (PEA) is a computational biology method that identifies biological functions are overrepresented in group of genes more than would be expected by chance and ranks these relevance. The relative abundance pertinent to specific pathways measured through statistical methods, associated functional retrieved from online bioinformatics databases. In the last decade, along with spread internet, higher availability resources made PEA software tools easy access use for practitioners worldwide. Although it became easier tools, also make mistakes could generate inflated or misleading results, especially beginners inexperienced biologists. With this article, we propose nine quick tips avoid common out complete, sound, thorough PEA, which can produce relevant robust results. We describe our guidelines simple way, so they understood used anyone, including students beginners. Some explain what do before starting others suggestions how correctly meaningful some final indicate useful steps properly interpret Our help users perform better pathway analyses eventually contribute understanding current biology.

Language: Английский

Citations

44
Interpreting omics data with pathway enrichment analysis DOI Creative Commons
Kangmei Zhao, Seung Y. Rhee

Trends in Genetics, Journal Year: 2023, Volume and Issue: 39(4), P. 308 - 319

Published: Feb. 6, 2023

Language: Английский

Citations

39
Statistical analysis of high-dimensional biomedical data: a gentle introduction to analytical goals, common approaches and challenges DOI Creative Commons
Jörg Rahnenführer, Riccardo De Bin, Axel Benner

et al.

BMC Medicine, Journal Year: 2023, Volume and Issue: 21(1)

Published: May 15, 2023

Abstract Background In high-dimensional data (HDD) settings, the number of variables associated with each observation is very large. Prominent examples HDD in biomedical research include omics a large such as many measurements across genome, proteome, or metabolome, well electronic health records that have numbers recorded for patient. The statistical analysis requires knowledge and experience, sometimes complex methods adapted to respective questions. Methods Advances methodology machine learning offer new opportunities innovative analyses HDD, but at same time require deeper understanding some fundamental concepts. Topic group TG9 “High-dimensional data” STRATOS (STRengthening Analytical Thinking Observational Studies) initiative provides guidance observational studies, addressing particular challenges studies involving HDD. this overview, we discuss key aspects provide gentle introduction non-statisticians classically trained statisticians little experience specific Results paper organized respect subtopics are most relevant initial analysis, exploratory multiple testing, prediction. For subtopic, main analytical goals settings outlined. these goals, basic explanations commonly used provided. Situations identified where traditional cannot, should not, be setting, adequate analytic tools still lacking. Many references Conclusions This review aims solid foundation researchers, including non-statisticians, who simply want better evaluate understand results analyses.

Language: Английский

Citations

36
Recent advances in mass spectrometry-based computational metabolomics DOI Creative Commons
Timothy M. D. Ebbels, Justin J. J. van der Hooft, Haley Chatelaine

et al.

Current Opinion in Chemical Biology, Journal Year: 2023, Volume and Issue: 74, P. 102288 - 102288

Published: March 24, 2023

The computational metabolomics field brings together computer scientists, bioinformaticians, chemists, clinicians, and biologists to maximize the impact of across a wide array scientific medical disciplines. continues expand as modern instrumentation produces datasets with increasing complexity, resolution, sensitivity. These must be processed, annotated, modeled, interpreted enable biological insight. Techniques for visualization, integration (within or between omics), interpretation data have evolved along innovation in databases knowledge resources required aid understanding. In this review, we highlight recent advances reflect on opportunities innovations response most pressing challenges. This review was compiled from discussions 2022 Dagstuhl seminar entitled "Computational Metabolomics: From Spectra Knowledge".

Language: Английский

Citations

34
Metabolomics on depression: A comparison of clinical and animal research DOI
Yibo Wang,

Xinyi Cai,

Yuchen Ma

et al.

Journal of Affective Disorders, Journal Year: 2024, Volume and Issue: 349, P. 559 - 568

Published: Jan. 9, 2024

Language: Английский

Citations

12