Metabolomics
Untargeted LC-MS or GC-MS peaks become annotated features and limma contrasts with pinned preprocessing, figures, R scripts, and a Methods draft.
| Category | Details |
|---|---|
| Supported platforms / instruments | Thermo Orbitrap (Q Exactive, Exploris, Fusion Lumos); SCIEX TripleTOF and ZenoTOF; Agilent Q-TOF (`.d`); Bruker timsTOF and Waters SYNAPT/Xevo when scoped at kickoff; mzML/mzXML; GC-MS and vendor peak tables on request |
| Input requirements | Thermo `.raw`, SCIEX `.wiff`, Agilent `.d`, Bruker `.d`, Waters `.raw`, or mzML/mzXML; multiple biological replicates per condition (often ≥3) for variance estimation [5]; pooled QC injections recommended; polarity mode and chromatography method documented at kickoff; typical standard cohort 10-40 samples |
| Reference databases / standards | HMDB 5.0; METLIN; KEGG; LipidMaps; GNPS spectral libraries; organism-specific in-house libraries on request |
| Primary tools (with versions) | xcms 4.x+; MS-DIAL 5.x+; MZmine 3.x+; MetaboAnalystR 4.x+; limma 3.60+; ropls 1.32+; msconvert (ProteoWizard) 3.x+. Pinned per project |
| Typical turnaround time | 2-4 weeks (standard untargeted LC-MS cohort, one contrast); 4-8 weeks (GC-MS extensions, multi-batch correction, or multi-contrast designs). Confirmed at kickoff |
| Deliverable formats | Feature/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 |
| Key cited best-practice reference | Alseekh et al. [2], *Nature Methods* (MS metabolomics reporting and annotation tiers) |
| Custom / bespoke analysis | Non-standard inputs, contrasts, cross-cohort harmonization, lipid class-specific workflows, GNPS networking, or client-specified ML models scoped at kickoff |
Untargeted metabolomics detects and quantifies m/z-retention time (RT) features from LC-MS or GC-MS without a predefined analyte list, then aligns peaks, normalizes intensities, annotates features, and tests differential abundance [2, 3]. Unlike targeted LC-MS/MS with calibrated standards, untargeted profiling is semi-quantitative and discovery-oriented [2]. Lloyd-Price et al. [4] generated 546 fecal metabolite profiles from 132 IBD subjects alongside metagenomics and host transcriptomics. Pepkio processes raw vendor files or documented peak tables with locked preprocessing parameters. Custom entry points are agreed at kickoff. See the untargeted metabolomics glossary.
Untargeted metabolomics fits when you need broad biochemical discovery, such as which pathways or metabolite classes shift under disease, treatment, or exposure, before committing to a targeted panel [2, 5].
| Approach | Best for | Limitations | Approximate cost range |
|---|---|---|---|
| Untargeted LC-MS / GC-MS | Hypothesis generation, pathway profiling, biomarker discovery across unknown metabolites | Incomplete annotation; semi-quantitative; pipeline choice shifts feature sets [6] | Moderate per-sample MS cost; moderate bioinformatics cost |
| Targeted LC-MS/MS | Known metabolite panels, absolute quantification with standards | Limited to predefined compounds; requires panel design upfront | Lower per-compound MS cost; panel design and standard procurement cost |
| NMR metabolomics (on request) | Structure elucidation, highly reproducible quantification of abundant metabolites | Lower sensitivity and metabolome coverage than LC-MS for low-abundance features | Instrument-dependent; moderate per-sample cost |
Pepkio confirms the biological question, sample matrix, platform, polarity mode, contrast definitions, and pooled-QC design. Confounded batch is flagged before processing [2, 5]. Preprocessing tool (xcms, MS-DIAL, or MZmine) is locked at kickoff [6].
Tools used: Project scoping checklist; MetaboAnalystR design module when applicable
Output: Signed scope document with pipeline choice and `analysis_parameters.yaml` draft
Raw file integrity, polarity, injection order, and sample IDs are verified against the design. Vendor export parameters are recorded for pre-processed peak tables. Pooled QC and blank injections are confirmed [2, 9].
Tools used: Custom validation scripts; `sample_manifest.csv` schema check
Output: `sample_manifest.csv` with file paths, condition labels, batch, injection order, and replicate IDs
Vendor raw files convert to mzML when cross-platform ingestion is required; SCIEX `.wiff` loads in MS-DIAL when scoped. TIC/BPC overlays, mass accuracy, and blank-feature flags are inspected before peak picking [5, 10, 11].
Tools used: msconvert (ProteoWizard); MetaboAnalystR QC module
Output: `raw_qc_report.html`; per-run TIC/BPC plots (PDF/SVG); mzML files when applicable
Features are extracted with consistent m/z and RT tolerances; missing peaks are gap-filled. Peak-picking parameters are documented because they are instrument- and matrix-dependent [12, 13].
Tools used: xcms; MS-DIAL; or MZmine (locked at kickoff)
Output: `peak_matrix.csv` (`feature_id`, `mz`, `rt`, sample intensity columns)
Pooled-QC samples enable QC-RLSC or probabilistic quotient normalization (PQN) when available; total-sum scaling is documented when QC samples are absent [5]. PCA and correlation heatmaps separate condition from batch and injection order before differential testing.
Tools used: MetaboAnalystR; limma `removeBatchEffect` when batch is not confounded with condition [14]
Output: `normalized_matrix.csv`; PCA scores plot; pooled-QC drift plot (PDF/SVG)
Low-abundance and high-missingness features are filtered using kickoff-agreed thresholds. Missingness is profiled by batch for BEAMs before imputation [5, 15]; strategy is logged in `analysis_parameters.yaml`.
Tools used: MetaboAnalystR; custom R filtering scripts
Output: `filtered_matrix.csv`; `missing_value_profile.csv`; missingness heatmap (PDF/SVG)
limma linear models fit the agreed design matrix with Benjamini-Hochberg FDR control [14]. PLS-DA or OPLS-DA with cross-validated metrics is run when scoped for classification-oriented questions [16, 17].
Tools used: limma; ropls
Output: `diff_results_<contrast>.csv` (`feature_id`, `log2FC`, `AveExpr`, `t`, `P.Value`, `adj.P.Val`); PLS-DA/OPLS-DA scores and VIP tables when scoped
Spectra are matched against HMDB 5.0, METLIN, KEGG, LipidMaps, and GNPS with Alseekh-aligned confidence tiers [2]. Pathway over-representation or MSEA is run on annotated features [10].
Tools used: MS-DIAL; MetaboAnalystR; SIRIUS/CSI:FingerID or GNPS molecular networking on request
Output: `annotation_table.csv` (`feature_id`, `mz`, `rt`, `compound_name`, `HMDB_ID`, `KEGG_ID`, `annotation_level`, `MS2_match_score`); `pathway_enrichment.csv`
Volcano plots, top-feature heatmaps, and pathway enrichment dot plots are exported at publication resolution. Annotation tier counts and QC summary statistics are compiled for the Methods draft [2].
Tools used: MetaboAnalystR; custom R visualization scripts
Output: PDF/SVG figure bundle; `figure_manifest.csv`
Commented R scripts, environment lock files, README, and Methods draft are bundled with all processed tables and figures. Post-delivery reviewer support scope is documented [2, 9].
Tools used: `renv` or conda environment export; custom packaging scripts
Output: Final deliverable bundle; README; Methods draft; HTML QC report
Multiple biological replicates per condition, often three or more, are recommended to estimate feature-level variance for limma testing [5]. Pepkio can analyze smaller designs but documents reduced power in the QC report. Pooled QC injections should bracket sample runs when feasible. Sample-size targets are confirmed at kickoff based on contrast design and expected effect sizes.
Yes, with caveats documented in the QC report. Runs with unstable TIC, poor mass accuracy, or high blank contamination are flagged before differential testing. Samples with extreme missingness or outlier positions in PCA are discussed with you; re-measurement is recommended when QC threatens the study question. Imputation strategy is adjusted based on missingness patterns rather than applied blindly [5, 15].
Yes, for formats we can load after kickoff review. Thermo `.raw`, SCIEX `.wiff`, Agilent `.d`, Bruker `.d`, and Waters `.raw` are processed in xcms or MS-DIAL when scoped; cross-platform ingestion uses msconvert [11]. Feasibility depends on file format, acquisition mode, and metadata completeness. The Methods draft records preprocessing tool, m/z tolerance, and RT alignment parameters.
A standard project (roughly 10-40 samples, one primary contrast, LC-MS peak picking, normalization, annotation, and pathway enrichment) typically completes in 2-4 weeks from data receipt. GC-MS extensions, multi-contrast designs, large batch-correction studies, or cohorts exceeding 60 samples may take 4-8 weeks. Milestone check-ins occur during the project. Exact timelines are confirmed at kickoff.
When batch is known and not fully confounded with condition, Pepkio includes batch in the limma design formula or applies `removeBatchEffect` after normalization diagnostics [14]. QC-RLSC or PQN corrects run-order drift using pooled QC samples when available [5]. PCA and correlation heatmaps are reviewed before modeling. BEAMs are profiled before imputation to avoid mixing batch correction with missing-value handling [15]. Fully confounded batch-by-condition designs cannot be corrected statistically and are flagged at kickoff.
Yes. You retain full ownership of all code, scripts, and results delivered under the project agreement. Pepkio provides commented R scripts with `renv.lock` or conda export files so you can rerun analyses when the execution environment matches the pinned setup. Matrices use standard `.csv` and `.tsv` formats readable in R, Python, or Excel. R Markdown delivery is available on request.
Yes. Checkpoint reviews occur after raw spectral QC, after normalization and batch diagnostics, and before final delivery. You can review contrast definitions, normalization method, imputation thresholds, and annotation cutoffs within the agreed project scope. A dedicated scientific contact leads the project and incorporates your matrix-specific knowledge into annotation and interpretation decisions.
Clarification of preprocessing methods, QC thresholds, normalization choices, and minor figure or table revisions within agreed scope (typically ≤20% of project scope). Pepkio drafts Methods and Supplementary text for analyses we performed. Substantial new analyses requested by reviewers (additional contrasts, alternate preprocessing, or cross-cohort extensions) are scoped as separate milestones with updated deliverables and timeline.
No. Pepkio operates as a fee-for-service provider and does not require co-authorship unless that is discussed in advance. Standard practice is acknowledgment of bioinformatics support in the Acknowledgments section. Co-authorship is considered only when Pepkio scientists make substantial intellectual contributions beyond routine analysis agreed in the project scope.
MS-DIAL suits MS/MS-rich untargeted data with built-in deconvolution and annotation [12]. xcms suits R-native, fully scriptable pipelines with Bioconductor integration [3, 10]. MZmine suits workflows that need GNPS molecular networking or modular lipidomics steps [13]. Because only ~8% of features overlapped across four workflows on a benchmark dataset, Pepkio locks the tool at kickoff [6].
MS1-only features are matched by accurate mass and RT against HMDB 5.0, METLIN, and KEGG, assigned putative annotation tiers per Alseekh et al. [2], and reported separately from MS/MS-confirmed identifications. Putative matches carry higher false-discovery risk; the QC report lists confirmed versus putative counts. SIRIUS/CSI:FingerID or GNPS networking can be scoped when MS/MS data or public spectral matches are available [2, 10].
Yes, when export parameters are documented. Peak tables from MS-DIAL, MZmine, Compound Discoverer, or Progenesis QI are accepted with m/z, RT, and intensity columns plus export settings. Pepkio validates feature counts, missingness, and sample ID alignment before modeling. Re-processing from raw files is recommended when export parameters are unknown or inconsistent across batches.
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