Bioinformatics analysis service

Proteomics analysis services: reproducible quantification from DDA, DIA, single-cell MS, and Olink assays

Proteomics analysis measures protein identity, abundance, and modification at scale using mass spectrometry (MS) or targeted immunoassays. Pepkio uses version-pinned pipelines and delivers QC reports, differential-abundance tables, PDF/SVG figures, documented R and Python code, and a Methods draft for academic, biotech, and pharma teams. Custom workflows, including non-standard inputs, outputs, or analyses beyond standard differential-protein testing, are scoped at kickoff.

Key facts

Key facts about proteomics analysis
FactValue
Data types supportedThermo `.raw`, Bruker `.d`, SCIEX `.wiff` (converted to mzML when needed), mzML/mzXML; label-free quantification (LFQ), TMT/iTRAQ isobaric tags, SILAC, data-dependent acquisition (DDA), data-independent acquisition (DIA); Olink Normalized Protein eXpression (NPX) exports; pre-processed protein abundance matrices
Reference builds or standards usedUniProt Swiss-Prot reviewed proteomes (e.g., human UP000005640); CPTAC-style contaminant sequences; organism-specific FASTA on request; Olink panel lot validation reports
Primary tools (with versions)MaxQuant 2.8.0.0; FragPipe 22+ with MSFragger 4.x; DIA-NN 2.0+; Perseus 2.0; MSstats 2.14; proDA 1.0; Philosopher; limma 3.60; proBatch 1.0
Typical turnaround range2-4 weeks (standard LFQ or DIA cohort, one contrast); 4-8 weeks (TMT multiplex, single-cell MS, multi-panel Olink, phosphoproteomics extensions), confirmed at kickoff
Deliverable formatsProtein and peptide matrices (`.tsv`, `.csv`); Perseus and MSstats contrast tables; PDF/SVG volcano plots, heatmaps, and PCA; HTML QC report; commented R/Python scripts; Methods draft
Regulatory/reproducibility standards followed1% peptide and protein false discovery rate (FDR) defaults; version-pinned conda or R environments; full parameter logs; PRIDE/ProteomeXchange deposition support on request
Custom / bespoke analysisNon-standard inputs, outputs, contrasts, enrichment methods, proteogenomic integration, or platform-specific extensions scoped at kickoff
Key terms:

DDA (data-dependent acquisition) selects precursor ions for fragmentation in real time; DIA (data-independent acquisition) fragments all precursors within defined m/z windows. PEA (proximity extension assay) is Olink's dual-antibody immunoassay with DNA barcode readout. NPX is Olink's log₂-scaled relative quantification unit. MaxLFQ is MaxQuant's label-free quantification algorithm. MNAR (missing not at random) and MCAR (missing completely at random) describe different missing-value mechanisms in proteomics matrices [3].

What is proteomics?

Proteomics identifies and quantifies proteins in biological samples: which proteins are present, how abundant they are, and how they are modified. mRNA measurements often cannot answer those questions well, because translation, protein stability, and post-translational modification are measured more directly at the protein level. The HUPO Human Proteome Project reported that 18,138 of 19,411 GENCODE protein-coding genes (93%) have credible protein-level evidence, while 1,273 "missing proteins" still lack high-stringency evidence [1]. Global investment in proteomics technologies reached $27.6 billion in 2024 [2].

What proteomics analysis can answer

Published examples of questions proteomics can help answer, including cases where RNA-seq on its own was not enough [16]:

Which proteins are dysregulated in ccRCC beyond RNA-seq?

Clark et al. [16]: 820 differential proteins in 103 tumors vs 80 normal adjacent tissues, including oxidative phosphorylation changes visible only at the protein level.

How heterogeneous are macrophage proteomes during differentiation?

Specht et al. [17]: 3,042 proteins across 1,490 single cells (SCoPE2), revealing a continuous proteome gradient.

Which plasma proteins associate with incident coronary events?

Wigren et al. [18]: decreased stem cell factor before events, measured by proximity extension assay.

Which phospho-signaling modules co-vary with genomic instability?

Clark et al. [16]: phosphoproteomics linked kinase modules to a genomic-instability ccRCC subtype.

Can multiplex immunoassays detect neuroinflammatory CSF signatures?

Bäckryd et al. (2017): distinct CSF vs plasma inflammatory profiles in fibromyalgia using a multiplex immunoassay panel.

Services included in this category

Proteomics services offered by Pepkio
ServiceDescriptionPrimary tools
DDA/DIA proteomicsLabel-free, TMT/iTRAQ, or DIA quantification from LC-MS/MS raw files, through peptide identification, protein inference, and differential abundance testingMaxQuant, FragPipe, DIA-NN, MSstats
Single-cell proteomicsCarrier-based single-cell MS quantification with cell-state clustering and QC metrics aligned with community guidelinesSCoPE2-style workflows, DIA-NN, custom QC per Deutsch et al. [19]
Olink proximity extensionTargeted multiplex immunoassay analysis from NPX exports with plate normalization, QC flagging, and differential protein testingOlink NPX pipeline, bridge-sample normalization, limma, MSstats

What Pepkio delivers

Each project includes processed data tables, QC-audited figures, version-pinned code, and a Methods draft, plus reviewer clarification support after delivery.

Processed data

  • Protein/peptide matrices (`.csv`, `.tsv`); Perseus or MSstats contrast tables; imputation strategy in `analysis_parameters.yaml`

Figures (PDF/SVG)

  • Volcano plots per contrast; batch-diagnostic PCA and correlation heatmaps

Code and documentation

  • Commented R/Python scripts with conda or `renv` locks; HTML QC report; README; Methods draft. You retain full ownership

Support

  • Milestone check-ins; reviewer clarification and minor revisions within agreed scope (typically ≤20% of deliverables); GitHub/Zenodo archival on request

How the analysis works — step by step

  1. 1. Validate inputs and experimental design

    Confirm raw file integrity, metadata, contrasts, and batch structure; flag confounded designs before processing [4].

    Tools and outputs

    Output: sample_manifest.csv

  2. 2. Convert and inspect raw data

    Thermo and Bruker MS files load directly; SCIEX `.wiff` converts to mzML when needed. Run-level MS QC; Olink NPX control checks [5].

    Tools and outputs

    Tools used: MaxQuant 2.8.0.0 or FragPipe 22+

  3. 3. Identify peptides and infer proteins

    Search UniProt with decoys; filter at 1% FDR [6].

    Tools and outputs

    Tools used: MaxQuant/Andromeda or MSFragger + Philosopher

  4. 4. Quantify protein abundances

    MaxLFQ (LFQ), MaxQuant isobaric quantification (TMT/iTRAQ), or DIA-NN (DIA) per design [7, 8].

    Tools and outputs

    Tools used: MaxQuant, FragPipe, DIA-NN 2.0+

  5. 5. Normalize and diagnose batch effects

    PCA is used to separate condition from batch; proBatch quantifies batch variance [4].

    Tools and outputs

    Tools used: Perseus 2.0, proBatch 1.0, limma 3.60

  6. 6. Filter proteins and handle missing values

    Exclude low-detection proteins; MNAR/MCAR patterns guide imputation or complete-case analysis [3].

    Tools and outputs

    Tools used: Perseus 2.0, proDA 1.0

  7. 7. Test differential protein abundance

    MSstats, limma, or proDA models contrasts with batch/covariates where permitted [9, 10].

    Tools and outputs

    Output: dep_results_<contrast>.csv

  8. 8. Run pathway enrichment and interpret results

    GO/KEGG over-representation on significant proteins [11].

    Tools and outputs

    Tools used: Perseus 2.0

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

    Assemble figures, tables, scripts, README, and Methods draft into the final bundle.

Tools and standards we use

Core tools for MS and Olink projects. Versions are pinned at kickoff and cited in the Methods draft.

Proteomics tools and standards
ToolVersionRolePrimary citation
MaxQuant2.8.0.0DDA/LFQ/TMT identification and MaxLFQ quantification[6] — https://doi.org/10.1038/nbt.1511
MSFragger (FragPipe)4.x / 22+Fast peptide search[12] — https://doi.org/10.1038/nmeth.4256
DIA-NN2.0+DIA precursor and protein quantification[13] — https://doi.org/10.1038/s41592-020-00998-0
Philosopher5.xFDR filtering and protein inference (FragPipe)[14] — https://doi.org/10.1038/s41596-020-0356-x
Perseus2.0Normalization, imputation, enrichment, visualization[11] — https://doi.org/10.1038/nmeth.3901
MSstats2.14MS-aware differential abundance modeling[9] — https://doi.org/10.1093/bioinformatics/btu305
proDA1.0Probabilistic dropout modeling for MNAR data[15] — https://doi.org/10.1038/s41592-020-00949-9
limma3.60Linear models for Olink NPX and matrix-based DE[10] — https://doi.org/10.1093/nar/gkv007
proBatch1.0Batch-effect diagnostics and correction[4] — https://doi.org/10.15252/msb.202110232
Olink PEA platformPanel-specificMultiplex immunoassay basis for NPX QC and normalization[5] — https://doi.org/10.1371/journal.pone.0095192

Common challenges — and how we handle them

Missing values, batch effects, and platform differences mean method choices need to be written down before differential testing.

Missing values can mean different things biologically

MNAR and MCAR missingness can inflate false positives if they are imputed without checking the pattern [3]. Pepkio profiles missingness and documents the imputation or complete-case strategy before testing.

Batch effects across MS acquisition runs

Column changes, instrument drift, and prep dates can introduce non-biological variance [4]. Batch diagnostics come before modeling. Batch-in-design or proBatch correction is used only when batch is not confounded with condition.

mRNA-protein discordance

Translation, degradation, and localization can decouple transcript and protein abundance [16]. Proteogenomic correlation reports can be added at kickoff when matched RNA is available.

Vendor-specific raw file formats

Thermo `.raw` and Bruker `.d` load in MaxQuant or FragPipe; SCIEX `.wiff` converts to mzML before search when needed.

Over-stringent protein inference filtering

Aggressive grouping can collapse isoforms. Grouping rules are documented in the Methods draft. Strict and expanded protein lists can be provided for reviewer sensitivity analyses.

Common questions

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

Provide raw MS files (`.raw`, `.d`, `.wiff`, or mzML), a sample metadata table with condition and batch columns, and the organism plus UniProt database preference. Olink projects require NPX export files and the panel lot report. Pre-processed protein matrices are accepted when upstream search parameters are documented. Custom input formats and non-standard metadata structures are supported when scoped at kickoff.

How long does proteomics analysis take at Pepkio?

Standard label-free or DIA projects (10-30 samples, one contrast) typically complete in 2-4 weeks. TMT, single-cell MS, multi-panel Olink, or phosphoproteomics extensions may take 4-8 weeks. Milestone check-ins occur after QC and before delivery. Exact timelines are confirmed at kickoff.

What do the deliverables look like?

You receive protein and peptide matrices, differential-abundance tables, volcano plots and PCA figures (PDF/SVG), an HTML QC report, commented R or Python 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, Bruker, or SCIEX raw files?

Pepkio processes Thermo `.raw` and Bruker `.d` files in MaxQuant 2.8.0.0 or FragPipe 22+ with MSFragger. SCIEX `.wiff` files are converted to mzML before search when needed, and mzML or mzXML files are also accepted. The Methods draft records the search engine, database version, and FDR thresholds applied.

What if my data quality is poor or missing values are high?

Pepkio documents identification rates, missing-value fractions, and outlier samples in the QC report before differential testing. High missingness triggers a review of whether MNAR-aware methods (proDA), Perseus imputation, or complete-case analysis is a better fit [3, 11]. Re-analysis after sample re-measurement is discussed when QC may undermine 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 and Python scripts with conda or `renv` lock files listing exact package versions. Scripts are organized by pipeline stage with a README describing execution from raw inputs to final tables.

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, covariate inclusion, imputation choices, and FDR thresholds within agreed scope. A dedicated scientific contact leads the project and is available for scheduled calls.

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, such as additional contrasts, re-imputation with alternate methods, or proteogenomic extensions, are scoped as separate milestones.

Can you analyze Olink NPX files without re-running the assay?

Pepkio imports Olink NPX Signature exports, applies per-sample QC flagging (incubation and detection controls), and performs between-plate normalization using bridge samples or intensity normalization as appropriate for the study design [5]. Differential testing uses limma or MSstats, with the normalization method documented in the Methods draft.

Do you support custom analyses outside standard differential-protein workflows?

Bespoke analyses, including custom contrasts, proteogenomic integration, phosphoproteomics extensions, ML classifiers, or non-standard deliverables, are scoped at kickoff after a feasibility review.

Related services

  • Transcriptomics — Proteogenomic integration when matched RNA-seq and proteomics data are available from the same samples.
  • Metabolomics — Joint pathway interpretation linking protein abundance changes to metabolite-level phenotypes.
  • Multi-omics integration — Cross-layer modeling when proteomics, transcriptomics, and metabolomics datasets share sample IDs.
  • Biomarker discovery — Supervised classifier development and validation on protein panels from MS or Olink data.
  • Bioinformatics consulting — Experimental design, replicate planning, and platform selection before sample preparation and data acquisition.
References
  1. Adkins JN, Baker MS, Bairoch A, et al. The 2024 report on the human proteome from the HUPO Human Proteome Project. Journal of Proteome Research. 2024. https://doi.org/10.1021/acs.jproteome.4c00776 (PMID: 39514846)
  2. BCC Research. Proteomics: Technologies and Global Markets. May 2025. https://www.bccresearch.com/market-research/biotechnology/proteomics-technologies-and-global-markets.html
  3. Lazar C, Gatto L, Ferro M, Bruley C, Burger T. Accounting for the multiple natures of missing values in label-free quantitative proteomics data sets to compare imputation strategies. Journal of Proteome Research. 2016;15(4):1116–1125. https://doi.org/10.1021/acs.jproteome.5b00981 (PMID: 26906401)
  4. Biskup K, Čuklina J, Mehnert M, et al. Diagnostics and correction of batch effects in large-scale proteomic studies: a tutorial. Molecular Systems Biology. 2021;17(8):e10232. https://doi.org/10.15252/msb.202110232 (PMID: 34432947)
  5. Assarsson E, Lundberg M, Holmquist G, et al. Homogenous 96-plex PEA immunoassay exhibiting high sensitivity, specificity, and excellent scalability. PLOS ONE. 2014;9(4):e95192. https://doi.org/10.1371/journal.pone.0095192 (PMID: 24755770)
  6. Cox J, Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nature Biotechnology. 2008;26(12):1367–1372. https://doi.org/10.1038/nbt.1511 (PMID: 19029910)
  7. Cox J, Hein MY, Luber CA, Paron I, Nagaraj N, Mann M. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Molecular & Cellular Proteomics. 2014;13(9):2513–2526. https://doi.org/10.1074/mcp.M113.031591 (PMID: 24942700)
  8. Yu F, Teo GC, Kong AT, et al. Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform. Nature Communications. 2023;14:4154. https://doi.org/10.1038/s41467-023-39869-5 (PMID: 37438352)
  9. Choi M, Chang CY, Clough T, et al. MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments. Bioinformatics. 2014;30(17):2524–2526. https://doi.org/10.1093/bioinformatics/btu305 (PMID: 24794931)
  10. 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)
  11. Tyanova S, Temu T, Sinitcyn P, et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nature Methods. 2016;13(9):731–740. https://doi.org/10.1038/nmeth.3901 (PMID: 27348712)
  12. Kong AT, Leprevost FV, Avtonomov DM, Mellachernou D, Nesvizhskii AI. MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics. Nature Methods. 2017;14(5):513–520. https://doi.org/10.1038/nmeth.4256 (PMID: 28394336)
  13. Demichev V, Messner CB, Vernardis SI, Lilley KS, Ralser M. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nature Methods. 2020;17(1):41–44. https://doi.org/10.1038/s41592-020-00998-0 (PMID: 31768060)
  14. da Veiga Leprevost F, Haynes SE, Avtonomov DM, et al. Philosopher: a versatile toolkit for shotgun proteomics data analysis. Nature Methods. 2020;17(9):869–870. https://doi.org/10.1038/s41596-020-0356-x (PMID: 32669682)
  15. Korkmaz S, Cox J, Mann M. proDA: a powerful and flexible tool for identifying differentially abundant proteins in label-free mass spectrometry data. Nature Methods. 2020;17(5):437–440. https://doi.org/10.1038/s41592-020-00949-9
  16. 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)
  17. Specht H, Emmott E, Petelski AA, et al. Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity using SCoPE2. Genome Biology. 2021;22(1):50. https://doi.org/10.1186/s13059-021-02267-5 (PMID: 33504367)
  18. Wigren M, Söderberg S, Bäckryd E, et al. Decreased levels of stem cell factor in subjects with incident coronary events. Journal of Internal Medicine. 2016;279(2):180–191. https://doi.org/10.1111/joim.12443 (PMID: 26467529)
  19. Deutsch EW, Orsburn BC, Aebersold R, et al. Initial recommendations for performing, benchmarking and reporting single-cell proteomics experiments. Nature Methods. 2023;20(3):375–386. https://doi.org/10.1038/s41592-023-01785-3 (PMID: 36864200)

Individual services

Deep-dive pages for specific proteomics methods and workflows.

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