Proteomics
DDA and DIA raw files or protein matrices become differential-abundance tables with pinned MaxQuant, FragPipe, or DIA-NN workflows, figures, scripts, and a Methods draft.
| Category | Details |
|---|---|
| Supported platforms / instruments | Thermo Orbitrap (Exploris, Q Exactive, Fusion Lumos; Astral nDIA when scoped); Bruker timsTOF (dia-PASEF); SCIEX TripleTOF; ZenoTOF and Waters when scoped at kickoff; protein matrices accepted |
| Input requirements | Thermo `.raw`, Bruker `.d`, SCIEX `.wiff` (converted to mzML when needed), or mzML/mzXML; multiple biological replicates per condition (often ≥3) for variance estimation [5, 6]; TMT/iTRAQ plex design documented at kickoff; phosphoproteomics and fractionated libraries scoped separately |
| Reference builds supported | UniProt Swiss-Prot reviewed proteomes (human UP000005640, mouse UP000000589, yeast UP000002311); CPTAC-style contaminant sequences; organism-specific FASTA on request |
| Primary tools (with versions) | MaxQuant 2.8+; FragPipe 22+ with MSFragger 4.x; Philosopher 5.x; DIA-NN 2.0+; directLFQ; Perseus 2.0; MSstats 2.14+; proDA 1.0; proBatch 1.0; limma 3.60, pinned per project |
| Typical turnaround time | 2-4 weeks (standard LFQ or DIA cohort, 4-24 samples, one contrast); 4-8 weeks (TMT multiplex, phosphoproteomics, multi-contrast designs, or large cohorts). Confirmed at kickoff. |
| Deliverable formats | Protein and peptide matrices (`.tsv`, `.csv`); MSstats/proDA contrast tables; PDF/SVG volcano plots, heatmaps, and PCA; HTML QC report; commented R/Python scripts; Methods draft |
| Key cited best-practice reference | Yu et al. [2], *Molecular & Cellular Proteomics* (DIA survey); Jiang et al. [1], *Nature Communications* (DIA software benchmarking) |
| Custom / bespoke analysis | Non-standard inputs, outputs, contrasts, phosphoproteomics extensions, affinity proteomics, proteogenomic correlation, or client-specified search engines scoped at kickoff |
DDA/DIA proteomics identifies peptides from LC-MS/MS spectra and aggregates precursor or fragment intensities into sample-by-protein abundance matrices. That measures which proteins change in abundance or modification between conditions [2, 3]. DDA selects high-abundance precursors for fragmentation. DIA fragments precursors within defined *m/z* windows, which tends to improve run-to-run quantitative consistency [2]. In Orbitrap and timsTOF benchmark studies, DIA-NN and Spectronaut showed distinct advantages [1]. Narrow-window DIA on Orbitrap Astral quantified about 10,000 human protein groups in a 30-minute gradient [4]. Pepkio processes both modes with documented library, FDR, and normalization choices. Custom workflows are agreed at kickoff. See the DDA/DIA proteomics glossary.
DDA/DIA proteomics fits when you need a direct measure of protein abundance, post-translational modification, or pathway-level protein dysregulation. RNA-seq often cannot answer those questions on its own [7].
| Approach | Best for | Limitations | Approximate cost range |
|---|---|---|---|
| DDA label-free (LFQ) | Flexible discovery, phospho-enriched DDA, small cohorts, MaxQuant/FragPipe compatibility | Stochastic precursor selection; lower run-to-run reproducibility than DIA | Lower per-sample MS time; moderate bioinformatics cost |
| DIA / SWATH | Large cohorts, quantitative consistency, library-free workflows | Spectral library strategy affects depth; platform-specific acquisition tuning | Moderate to higher MS acquisition time; efficient per-sample quantification at scale |
| TMT / iTRAQ multiplex | Controlled multi-condition comparison in one MS run (typical 6-16-plex) | Ratio compression; isobaric interference; complex designs need careful planning | Higher reagent and MS cost; strong for paired, multiplexed designs |
Pepkio confirms raw file integrity (or matrix dimensions), acquisition mode (DDA, DIA, TMT), organism, and contrast definitions. Sample IDs, biological replicate IDs, batch, and condition are recorded in sample_manifest.csv. Designs with batch fully confounded by condition, or with very few biological replicates, are flagged before processing [5, 6].
Tools used: Custom validation scripts; MSstats annotation schema check
Output: sample_manifest.csv with raw file paths, replicate IDs, condition labels, and design notes
Thermo `.raw` and Bruker `.d` files load in MaxQuant, FragPipe, or DIA-NN; SCIEX `.wiff` converts to mzML when needed. Run-level QC checks TIC stability, scan counts, and precursor distribution before search [2].
Tools used: MaxQuant 2.8+; FragPipe 22+; msconvert when required
Output: ms_qc_summary.csv; per-run QC plots; mzML when applicable
Library strategy is set at kickoff: experimental DDA library, DIA-NN library-free spectra, or hybrid DDA+DIA library [1, 2]. Coverage is logged against benchmark libraries (for example, 174,115 precursors mapped to 11,725 proteins in [1]).
Tools used: FragPipe 22+ / EasyPQP; DIA-NN 2.0+; Spectronaut when client supplies exports
Output: Spectral library (`.speclib`, `.tsv`, or native format); library_qc_report.csv
Searches use UniProt Swiss-Prot with reversed decoys at 1% peptide and protein FDR [3, 9]. DDA uses Andromeda or MSFragger; DIA uses DIA-NN or MSFragger-DIA [2, 10].
Tools used: MaxQuant 2.8+; MSFragger 4.x + Philosopher 5.x; DIA-NN 2.0+
Output: proteinGroups.txt / report.parquet; psm.tsv; protein_inference_log.csv
DDA uses MaxLFQ or FragPipe LFQ [11]. DIA uses DIA-NN quantities, with directLFQ when the design matches Zhang et al. [12] workflows. TMT/iTRAQ uses MaxQuant reporter-ion extraction with isotope impurity correction.
Tools used: MaxQuant 2.8+; DIA-NN 2.0+; directLFQ; FragPipe 22+
Output: protein_matrix_raw.csv; peptide_matrix_raw.csv; quantification log
Detection rates, log-intensity distributions, and correlations are summarized. PCA and clustering are used to separate condition from batch when that is possible. proBatch quantifies batch variance before correction [13].
Tools used: Perseus 2.0; proBatch 1.0; custom R diagnostics
Output: sample_qc_summary.csv; PCA/correlation plots; proBatch report
Low-detection proteins are filtered. MNAR versus MCAR patterns guide imputation choice [14]. The strategy is logged in analysis_parameters.yaml.
Tools used: Perseus 2.0; proDA 1.0
Output: Filtered matrix; missing_value_profile.csv; protein_filter_log.csv
MSstats, proDA, or limma fit the agreed design matrix with Benjamini-Hochberg FDR control [5, 15, 16].
Tools used: MSstats 2.14+; proDA 1.0; limma 3.60
Output: dep_results_<contrast>.csv (ProteinID, Gene, log2FC, SE, DF, pvalue, adj.pvalue)
Significant proteins are tested for GO/KEGG over-representation. Dot plots summarize enriched terms [17].
Tools used: Perseus 2.0
Output: go_enrichment.csv, kegg_enrichment.csv; enrichment plots (PDF/SVG)
Figures, scripts, README, and a Methods draft are bundled. Phospho, affinity, and Spectronaut-native extensions are scoped separately [2].
Tools used: Perseus 2.0; MSstats 2.14+; custom R/Python scripts
Output: PDF/SVG bundle; scripts; README; Methods draft
Multiple biological replicates per condition, often three or more, are recommended to estimate protein-level variance for differential testing [5, 6]. MSstats `designSampleSize` can estimate replicate count or power from pilot data for a target fold change. Pepkio can analyze smaller designs and documents reduced statistical power in the QC report. Sample-size targets are confirmed at kickoff.
Yes, with caveats documented in the QC report. Runs with low PSM counts, unstable TIC, or poor precursor coverage are flagged before differential testing. Outlier samples in PCA are discussed with you. Re-acquisition is recommended when yield threatens the study question. Sensitivity-optimized DIA acquisition may be relevant for low-input samples when instrument settings are available [8].
Yes, for formats we can load after kickoff review. Thermo `.raw` files from Orbitrap Exploris, Q Exactive, Fusion Lumos, and Astral load in MaxQuant, FragPipe, and DIA-NN. Bruker timsTOF `.d` files support dia-PASEF workflows in DIA-NN. SCIEX TripleTOF `.wiff` files convert to mzML before search when needed. ZenoTOF and Waters SYNAPT/Xevo data are supported when scoped at kickoff.
A standard project (roughly 4-24 samples, one primary contrast, LFQ or DIA quantification, and GO/KEGG enrichment) typically completes in 2-4 weeks from data receipt. TMT multiplex designs, phosphoproteomics, multi-contrast studies, or cohorts exceeding 48 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 MSstats or limma design formula [5]. PCA and proBatch variance reports are reviewed before modeling. Post-hoc batch correction with proBatch or other methods is scoped separately when the design requires it [13].
Yes. You retain full ownership of all code, scripts, and results delivered under the project agreement. Pepkio provides commented R or Python scripts with environment lock files so you can rerun analyses when the execution environment matches the pinned setup. Matrices use standard `.csv` and `.tsv` formats. R Markdown delivery is available on request.
Yes. Checkpoint reviews occur after MS QC, after protein matrix audit, and before final delivery. You can review contrast definitions, library strategy, imputation choices, and FDR thresholds within agreed scope. A dedicated scientific contact leads the project, coordinates milestone feedback, and documents decisions in the QC report and Methods draft.
Clarification of methods, QC thresholds, library construction, and minor figure or table revisions within agreed scope (typically ≤20% of deliverables), consistent with Pepkio's standard post-delivery policy. It does not include open-ended reanalysis or new biological contrasts. Substantial new work such as additional contrasts, alternate imputation, or phosphoproteomics extensions is scoped as separate milestones with updated deliverables and timeline.
No. Pepkio operates as a fee-for-service provider unless co-authorship is discussed and agreed in advance. You retain ownership of results and code delivered under the project agreement. Authorship is not a default requirement for computational analysis of client-generated data, and billing is separate from publication credit.
DIA is often a better fit for large cohorts that need consistent quantification across many runs. DDA LFQ is often a better fit for flexible discovery, phospho-enriched workflows, or when MaxQuant compatibility is required [1, 2]. nDIA on Orbitrap Astral quantified about 10,000 protein groups in a 30-minute gradient in a high-throughput design [4]. Pepkio documents trade-offs in the kickoff feasibility review.
Not always. DIA-NN library-free mode uses predicted spectra from the UniProt FASTA and does not require a dedicated DDA run [1, 10]. Experimental DDA libraries can increase depth for complex proteomes or PTMs. Hybrid DDA+DIA library building via MSFragger-DIA in FragPipe is available when instrument time permits [2].
TMT reporter intensities are extracted with isotope impurity correction in MaxQuant. Ratio compression from co-isolation is documented in the QC report. SPS-MS3 or alternative quantification can be scoped when needed. Missing values are profiled for MNAR patterns. proDA or group-wise imputation replaces blind Perseus defaults when MNAR dominates [14, 15].
Yes, when a feasibility review confirms inputs, outputs, and timeline. Custom contrasts, phosphoproteomics localization, affinity proteomics, proteogenomic correlation, client-specified search parameters, or non-standard deliverables are scoped at kickoff with documented milestones rather than assumed as part of the standard DDA/DIA pipeline. Feasibility covers data format, cohort size, and deliverable fit before work begins.
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