Bioinformatics analysis service

Metabolomics analysis services: LC-MS and GC-MS workflows from raw spectra to annotated features

Metabolomics measures small-molecule abundance in biofluids, cells, or tissues to profile biochemical state at the phenotype level. Pepkio uses version-pinned pipelines from raw mzML or vendor exports to annotated feature tables, QC reports, PDF/SVG figures, documented R code, and a Methods draft for academic, biotech, and pharma teams. Custom workflows, including non-standard inputs, outputs, or analyses beyond standard untargeted differential-feature testing, are scoped at kickoff.

Key facts

Key facts about metabolomics analysis
FactValue
Data types supportedLC-MS/MS (Thermo `.raw`, SCIEX `.wiff`, Agilent `.d`, Bruker `.d`, Waters `.raw`, mzML/mzXML); GC-MS and vendor peak tables on request; NMR bucket tables on request
Reference builds or standards usedHMDB 5.0; METLIN; KEGG; LipidMaps; GNPS spectral libraries; organism-specific in-house libraries on request
Primary tools (with versions)xcms 4.2.3; MS-DIAL 5.1; MZmine 3.9; MetaboAnalystR 4.0; limma 3.60; ropls 1.32; MOFA2 1.12; mixOmics 6.28. See Tools table
Typical turnaround range2-4 weeks (standard untargeted LC-MS cohort, one contrast); 4-8 weeks (GC-MS extensions, multi-omics integration, or multi-batch correction). Confirmed at kickoff
Deliverable formatsFeature/intensity matrices (`.csv`, `.tsv`); annotation tables with confidence tiers; PDF/SVG PCA, PLS-DA, and volcano plots; pathway enrichment tables; HTML QC report; commented R scripts; Methods draft
Regulatory/reproducibility standards followedAlseekh et al. MS metabolomics reporting guidelines; version-pinned conda or `renv` environments; sample manifests; optional private Git or Zenodo archival on request
Custom / bespoke analysisNon-standard inputs, contrasts, cross-cohort harmonization, multi-omics extensions, or client-specified ML models scoped at kickoff
Key terms:

A feature is an m/z-retention time (RT) pair before metabolite assignment; MS2 (tandem MS) fragments precursors for structural annotation. Untargeted profiling detects all features; targeted assays quantify predefined compounds. A QC sample (pooled reference injected repeatedly) monitors drift. Annotation level follows tiers from MS/MS match to putative formula [2].

What is metabolomics?

Metabolomics measures small molecules, including amino acids, lipids, organic acids, and other metabolites, in biological samples. Metabolites sit close to phenotype, so metabolomics is used to identify which biochemical pathways change under disease, treatment, or environmental exposure [1]. Untargeted liquid chromatography-mass spectrometry (LC-MS) can detect thousands of features per run, though confident annotation remains a bottleneck [2]. HMDB 5.0 catalogs 217,920 metabolite entries for human studies [3].

What metabolomics analysis can answer

Published examples of questions metabolomics can address:

Which serum metabolites distinguish metastatic colorectal cancer from healthy controls?

Martín-Blázquez et al. (2019): untargeted LC-HRMS on 65 metastatic CRC and 60 control sera identified altered sphingolipids, glycerophospholipids, and an endocannabinoid.

Which urinary metabolites associate with type 2 diabetes?

Salihovic et al. [15]: UPLC-MS in 1,424 Swedish adults linked lower 3-hydroxyundecanoyl-carnitine to prevalent T2D; six metabolites improved incident prediction (C-statistic 0.866 to 0.892).

How do metabolites shift with microbiome and host transcriptome in IBD?

Lloyd-Price et al. [16]: 132 longitudinal IBD subjects with integrated metabolomics, metagenomics, and host transcriptomics linked metabolic shifts to disease activity.

Which pancreatic cancer metabolite biomarkers replicate across studies?

Roth & Powers [17]: 24 PDAC studies; 10 metabolites validated across cohorts, but 87% of 655 proposed biomarkers appeared in only one study.

When should metabolomics complement transcript or protein layers in ccRCC?

Clark et al. [18]: proteogenomic profiling of 103 tumors and 80 normal tissues showed oxidative phosphorylation dysregulation at the protein level not apparent from RNA-seq, which is a reason to add matched metabolomics when sample IDs align.

Services included in this category

This category includes untargeted LC-MS/GC-MS profiling and multi-omics integration. Each spoke page lists inputs, tools, and deliverables.

Metabolomics services offered by Pepkio
ServiceDescriptionPrimary tools
Untargeted metabolomicsPeak detection, alignment, normalization, annotation, and differential feature testing from LC-MS or GC-MS raw data or vendor peak tablesxcms 4.2.3, MS-DIAL 5.1, MZmine 3.9, MetaboAnalystR 4.0, limma 3.60
Multi-omics integrationJoint modeling of metabolomics with transcriptomics, proteomics, or metagenomics on matched samples to identify shared pathways and cross-layer biomarkersMOFA2 1.12, mixOmics DIABLO 6.28, correlation and WGCNA modules on request

What Pepkio delivers

Each project includes processed data tables, QC-audited figures, version-pinned code, and a Methods draft.

Processed data and figures

  • `feature_matrix.csv`, `annotation_table.csv`, `diff_results_<contrast>.csv`; PCA/PLS-DA and volcano plots (PDF/SVG); pathway enrichment tables with Alseekh-aligned annotation tiers

QC, code, and support

  • `sample_manifest.csv`, `qc_summary.html`, `analysis_parameters.yaml`; commented R scripts with conda/`renv` locks, README, Methods draft; milestone check-ins; reviewer clarification within agreed scope (typically ≤20% of deliverables)

Non-standard contrasts or figures are scoped at kickoff.

How the analysis works — step by step

  1. 1. Scope study design and select preprocessing pipeline

    Confirm question, matrix, platform, contrasts, and QC-sample design; flag confounded batch [1, 2].

    Tools and outputs

    Output: signed scope with pipeline choice (xcms, MS-DIAL, or MZmine)

  2. 2. Validate inputs and record metadata

    Verify file integrity, polarity, and sample IDs; convert vendor formats to mzML when needed; confirm pooled QC injections.

    Tools and outputs

    Tools used: msconvert (ProteoWizard 3.0.20360)

    Output: sample_manifest.csv

  3. 3. Run raw spectral QC

    Inspect TIC and base-peak chromatograms, mass accuracy, and blank flags.

    Tools and outputs

    Tools used: MetaboAnalystR 4.0 QC module [4]

    Output: raw_qc_report.html

  4. 4. Detect, align, and integrate peaks

    Extract features with consistent m/z and RT tolerances; gap-fill missing peaks.

    Tools and outputs

    Tools used: xcms 4.2.3 [5]; MS-DIAL 5.1 [6]; or MZmine 3.9 [7]

    Output: peak_matrix.csv

  5. 5. Normalize and diagnose batch and run-order effects

    Apply QC-RLSC or probabilistic quotient normalization; review PCA for condition versus batch [1].

    Tools and outputs

    Tools used: MetaboAnalystR 4.0; limma `removeBatchEffect` when batch is not confounded with condition [8]

    Output: normalized_matrix.csv; PCA plots

  6. 6. Filter features and handle missing values

    Remove low-abundance and blank-contaminated features; profile missingness by batch; document imputation before testing [1, 9].

    Tools and outputs

    Output: filtered_matrix.csv; missingness heatmap

  7. 7. Test differential abundance and multivariate structure

    Model contrasts with limma; run PLS-DA or OPLS-DA when scoped [8, 10, 11].

    Tools and outputs

    Tools used: limma 3.60; ropls 1.32

    Output: diff_results_<contrast>.csv; cross-validated PLS-DA metrics

  8. 8. Annotate features and run pathway enrichment

    Match spectra against HMDB, METLIN, and KEGG; assign annotation tiers; run pathway over-representation or MSEA [2, 4].

    Tools and outputs

    Tools used: MetaboAnalystR 4.0; SIRIUS/CSI:FingerID on request

    Output: annotation_table.csv; pathway_enrichment.csv

  9. 9. Package figures, scripts, and Methods draft

    Assemble deliverables with pinned versions; add MOFA2 or DIABLO when matched omics matrices are in scope [12, 13].

    Tools and outputs

    Output: final deliverable bundle

Tools and standards we use

Core tools for untargeted and multi-omics projects. Versions are pinned at kickoff and cited in the Methods draft.

Metabolomics tools and standards
ToolVersionRolePrimary citation
xcms4.2.3LC-MS/GC-MS peak detection, alignment, and gap filling[5] — https://doi.org/10.1021/ac051437y
MS-DIAL5.1Vendor-agnostic peak picking, MS/MS deconvolution, and annotation[6] — https://doi.org/10.1038/nmeth.3259
MZmine3.9Modular LC-MS/GC-MS processing and lipidomics workflows[7] — https://doi.org/10.1038/s41596-024-00996-y
MetaboAnalystR4.0Unified LC-MS preprocessing, statistics, and pathway analysis[4] — https://doi.org/10.1038/s41467-024-48009-6
limma3.60Linear models for differential feature testing; batch adjustment via `removeBatchEffect`[8] — https://doi.org/10.1093/nar/gkv007
ropls1.32PLS-DA and OPLS-DA multivariate modeling[11] — https://doi.org/10.1021/acs.jproteome.5b00354
MOFA21.12Unsupervised multi-omics factor integration[12] — https://doi.org/10.15252/msb.20178124
mixOmics (DIABLO)6.28Supervised multi-omics integration with outcome labels[13] — https://doi.org/10.1093/bioinformatics/bty1054
msconvert (ProteoWizard)3.0.20360Raw-to-mzML conversion for cross-platform ingestion[14] — https://doi.org/10.1038/nbt.2376
HMDB5.0Human metabolite reference and spectral matching[3] — https://doi.org/10.1093/nar/gkab1062

Common challenges — and how we handle them

Missing values, annotation uncertainty, and pipeline choice need documented decisions before differential testing.

Metabolite annotation confidence varies widely

MS1-only matches carry higher false-discovery risk than MS/MS-confirmed identifications [2]. Pepkio assigns annotation tiers and reports putative versus confirmed counts in the QC summary.

Pipeline choice shifts feature sets materially

Only about 8% of features overlapped across four workflows (XCMS, MS-DIAL, MZmine, Compound Discoverer) on a shared LC-MS dataset [19]. Pepkio locks pipeline and parameters at kickoff.

Missing values confound with batch effects

Batch-associated missingness (BEAMs) can bias imputation and batch correction if handled sequentially [1, 9]. Pepkio profiles missingness by batch and applies batch adjustment only when batch is not confounded with condition.

Run-order drift and ion suppression distort quantification

Signal intensity can shift across injection order and matrix complexity [1, 2]. Pepkio uses pooled QC samples where feasible and applies QC-RLSC or PQN with run-order diagnostics.

Clinical biomarker claims lack cross-study replication

Meta-analysis of 24 PDAC studies found that 87% of proposed biomarkers appeared in single reports only [7]. Pepkio recommends validation cohorts before biomarker claims.

Common questions

What data do I need to provide for a metabolomics analysis project?

Provide raw MS files (`.raw`, `.wiff`, `.d`, or mzML), metadata with condition and batch columns, and platform plus polarity mode. Include pooled QC files when available [1, 2]. Pre-processed peak tables from MS-DIAL or MZmine are accepted when export parameters are documented.

How long does metabolomics analysis take at Pepkio?

Standard untargeted LC-MS projects (roughly 10-40 samples, one contrast) typically complete in 2-4 weeks. GC-MS extensions, multi-omics integration, multi-contrast designs, or large batch-correction studies may take 4-8 weeks. Exact timelines are confirmed at kickoff.

What do the deliverables look like?

You receive feature and annotation tables, differential-abundance results per contrast, PCA/PLS-DA and volcano figures (PDF/SVG), an HTML QC report, commented R scripts with conda or `renv` lock files, a README, and a Methods draft. Optional GitHub or Zenodo archival is available on request.

Can you handle Thermo, SCIEX, Agilent, or Waters LC-MS data?

Thermo `.raw`, Agilent `.d`, Bruker `.d`, and Waters `.raw` are converted to mzML via msconvert or imported in MS-DIAL when scoped. SCIEX `.wiff` can be processed in MS-DIAL 5.1 when that workflow is scoped at kickoff, and mzML exports are also accepted. The Methods draft records preprocessing tool, m/z tolerance, and RT alignment parameters.

What if my data quality is poor or I have many missing features?

Pepkio documents TIC quality, missing-value fractions, and outlier samples in the QC report before differential testing. High missingness triggers a review of imputation strategy, including small-value, k-nearest neighbours, or complete-case analysis [1, 9]. Re-analysis after sample re-measurement is discussed when QC threatens the study question.

Do you provide the code, and can I reproduce the results?

You retain full ownership of all code and results. Pepkio delivers commented R scripts with conda or `renv` lock files listing exact package versions, organized by pipeline stage with a README.

Can I be involved during the analysis?

Checkpoint reviews occur after raw-data QC, after normalization and batch diagnostics, and before final delivery. You can review contrast definitions, normalization, imputation, and annotation thresholds within agreed scope. A dedicated scientific contact leads the project.

What happens if a reviewer requests changes after delivery?

Post-delivery support covers clarification of methods, QC thresholds, and minor figure or table revisions within agreed scope (typically ≤20% of deliverables). Substantial new analyses are scoped as separate milestones, consistent with Pepkio's reviewer-support policy.

Can you integrate metabolomics with my RNA-seq or proteomics data?

Metabolomics can be integrated with RNA-seq or proteomics when sample IDs match across layers. Pepkio scopes multi-omics integration via MOFA2 for unsupervised factor discovery or mixOmics DIABLO when a supervised outcome label exists [12, 13]. Cross-layer correlation and pathway concordance are documented in the Methods draft.

Do you support custom analyses outside standard untargeted workflows?

Bespoke analyses, including custom contrasts, cross-cohort harmonization, lipid class-specific workflows, network propagation, ML classifiers, or non-standard deliverables, are scoped at kickoff after a feasibility review.

Related services

References
  1. Reinhold D, Pielke-Lombardo H, Jacobson S, Ghosh D, Kechris K. Pre-analytic considerations for mass spectrometry based untargeted metabolomics data. Methods in Molecular Biology. 2019;1978:323–340. https://doi.org/10.1007/978-1-4939-9236-2_20 (PMID: 31119672)
  2. Alseekh S, Aharoni A, Brotman Y, et al. Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices. Nature Methods. 2021;18(7):747–756. https://doi.org/10.1038/s41592-021-01197-1 (PMID: 34239102)
  3. Wishart DS, Guo AC, Oler E, et al. HMDB 5.0: the Human Metabolome Database for 2022. Nucleic Acids Research. 2022;50(D1):D622–D631. https://doi.org/10.1093/nar/gkab1062 (PMID: 34986597)
  4. Pang Z, Xu L, Viau C, et al. MetaboAnalystR 4.0: a unified LC-MS workflow for global metabolomics. Nature Communications. 2024;15:3675. https://doi.org/10.1038/s41467-024-48009-6 (PMID: 38693118)
  5. Smith CA, Want EJ, O\'Maille G, Abagyan R, Siuzdak G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Analytical Chemistry. 2006;78(3):779–787. https://doi.org/10.1021/ac051437y (PMID: 16448051)
  6. Tsugawa H, Cajka T, Kind T, et al. MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nature Methods. 2015;12(6):523–526. https://doi.org/10.1038/nmeth.3259 (PMID: 25938372)
  7. Heuckeroth S, Damiani T, Smirnov A, et al. Reproducible mass spectrometry data processing and compound annotation in MZmine 3. Nature Protocols. 2024;19(9):2597–2641. https://doi.org/10.1038/s41596-024-00996-y (PMID: 38769143)
  8. Ritchie ME, Phipson B, Wu D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research. 2015;43(7):e47. https://doi.org/10.1093/nar/gkv007 (PMID: 25605792)
  9. Goh WWB, Hui HWH, Wong L. How missing value imputation is confounded with batch effects and what you can do about it. Drug Discovery Today. 2023;28(9):103661. https://doi.org/10.1016/j.drudis.2023.103661 (PMID: 37301250)
  10. Eriksson L, Trygg J, Wold S. CV-ANOVA for significance testing of PLS and OPLS models. Journal of Chemometrics. 2008;22(11–12):594–600. https://doi.org/10.1002/cem.1187
  11. Thévenot EA, Roux A, Xu Y, et al. Analysis of the human adult urinary metabolome variations with age, body mass index, and gender by implementing a comprehensive workflow for univariate and OPLS statistical analyses. Journal of Proteome Research. 2015;14(8):3322–3335. https://doi.org/10.1021/acs.jproteome.5b00354 (PMID: 26088811)
  12. Argelaguet R, Velten B, Arnol D, et al. Multi-Omics Factor Analysis—a framework for unsupervised integration of multi-omics data sets. Molecular Systems Biology. 2018;14(6):e8124. https://doi.org/10.15252/msb.20178124 (PMID: 29925568)
  13. Singh A, Shannon CP, Gautier B, et al. DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays. Bioinformatics. 2019;35(17):3055–3062. https://doi.org/10.1093/bioinformatics/bty1054 (PMID: 30657866)
  14. Chambers MC, Maclean B, Burke R, et al. A cross-platform toolkit for mass spectrometry and proteomics. Nature Biotechnology. 2012;30(10):918–920. https://doi.org/10.1038/nbt.2376 (PMID: 23051804)
  15. Salihovic S, Broeckling CD, Ganna A, et al. Non-targeted urine metabolomics and associations with prevalent and incident type 2 diabetes. Scientific Reports. 2020;10:16474. https://doi.org/10.1038/s41598-020-72456-y (PMID: 33020500)
  16. Lloyd-Price J, Arze C, Ananthakrishnan AN, et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature. 2019;569(7758):655–662. https://doi.org/10.1038/s41586-019-1237-9 (PMID: 31142855)
  17. Roth HE, Powers R. Meta-analysis reveals both the promises and the challenges of clinical metabolomics. Cancers. 2022;14(16):3992. https://doi.org/10.3390/cancers14163992 (PMID: 36010984)
  18. Clark DJ, Dhanasekaran SM, Petralia F, et al. Integrated proteogenomic characterization of clear cell renal cell carcinoma. Cell. 2019;179(4):964–983.e31. https://doi.org/10.1016/j.cell.2019.10.007 (PMID: 31675502)
  19. Aigensberger M, Bueschl C, Castillo-Lopez E, et al. Modular comparison of untargeted metabolomics processing steps. Analytica Chimica Acta. 2025;1336:343491. https://doi.org/10.1016/j.aca.2024.343491 (PMID: 39788662)

Individual services

Deep-dive pages for specific metabolomics methods and workflows.

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