Bioinformatics analysis service
When your question, file format, or deliverables do not fit a catalog pipeline, Pepkio runs a scoped workflow with pinned code, figures, and a Methods draft.
| Category | Details |
|---|---|
| Data types supported | FASTQ, BAM, VCF, mzML, `.h5ad`/Seurat objects, Olink NPX exports, spatial count matrices, image arrays, CSV/TSV abundance tables, and client proprietary formats. Feasibility confirmed at intake |
| Reference builds or standards used | Project-specific references and community standards when applicable (e.g., GRCh38/GATK Best Practices, ENCODE RNA-seq, UniProt reviewed proteomes); FAIR metadata for all deliverables [2] |
| Primary tools (with versions) | Snakemake; Nextflow 24.x+; conda/mamba 24.x; Docker or Singularity (current LTS); Git 2.x; R 4.4.x; Python 3.12. Exact versions pinned per project and listed in the Methods draft |
| Typical turnaround range | 3-6 weeks (focused custom module, single contrast or extension); 6-12 weeks (multi-omics integration, novel assay, or client pipeline port). Confirmed at kickoff |
| Deliverable formats | Result tables (`.csv`, `.tsv`); figures (PDF/SVG with source data); HTML QC reports; Snakemake/Nextflow workflow files; `conda-lock.yml` or `renv.lock`; README; Methods draft |
| Regulatory/reproducibility standards followed | Sandve et al. [3] reproducibility rules; Wilson et al. [4] project organization; FAIR principles [2]; version-pinned environments; optional Zenodo DOI archival |
| Custom / bespoke analysis | Core service scope: non-standard inputs, outputs, statistical models, visualization packages, and analyses outside Pepkio's catalog spokes are defined in a written milestone plan at kickoff |
A workflow is an ordered sequence of computational steps from raw data to results. Version pinning records exact software builds so results can be reproduced months later [3]. A reproducible research compendium bundles data manifests, code, parameters, and outputs [4]. Multi-omics integration combines measurements from multiple molecular layers to model shared and layer-specific variation [5].
Custom bioinformatics analysis is outsourced work on computational workflows that do not map to a fixed catalog pipeline: novel assays, mixed modalities, client-specified methods, or further modeling on data you already hold. The usual question is how to answer a specific biological question when no off-the-shelf workflow exists. The global bioinformatics services market was estimated at USD 3.6 billion in 2025 and projected to reach USD 11.2 billion by 2033 (15.4% CAGR from 2026 to 2033) [1]. Pepkio scopes each project with milestone-based pricing and a dedicated PhD-level scientific contact.
Custom analysis connects mixed data and non-standard methods to a specific biological question. Published examples:
Clark et al. [13] profiled 103 tumors and 80 normal adjacent tissues, identifying 820 differentially abundant proteins, including oxidative phosphorylation uncoupling not captured at the mRNA level.
Miller et al. [14] developed STdeconvolve, recovering cell-type profiles from 10x Visium, Slide-seq, and DBiT-seq data without external scRNA-seq.
Argelaguet et al. [5] applied MOFA to 200 CLL samples, integrating somatic mutations, RNA, DNA methylation, and ex vivo drug response.
Valous et al. [15] reviewed graph-based workflows that model cross-layer dependencies rather than naive feature concatenation.
Mölder et al. (2021) showed Snakemake with conda and containers enables portable analysis from laptops to HPC.
This category has no sub-spoke pages. Each project is scoped at intake. Common project types (tools selected when scoped):
| Service | Description | Primary tools |
|---|---|---|
| Multi-omics integration | Joint modeling across RNA, protein, metabolite, or epigenomic layers with batch-aware harmonization | MOFA+; mixOmics; custom preprocessing in R/Python |
| Client pipeline extension | Wrap, containerize, or extend in-house scripts into reproducible workflows | Snakemake; Nextflow; conda; Git |
| Novel platform or assay analysis | QC and analysis for non-catalog instruments, file formats, or assay readouts | Custom Python/R modules; community tools identified at feasibility review |
| Downstream analysis on processed data | Differential testing, clustering, pathway enrichment, or ML on matrices you already hold | DESeq2; Seurat; scikit-learn, as scoped |
| Custom reporting and visualization | Manuscript-ready or regulatory-facing figure and table packages with traceable source data | ggplot2; matplotlib; Quarto / RMarkdown |
Each custom project returns version-pinned code, auditable outputs, and a Methods draft, plus reviewer clarification within agreed scope.
Confirm the biological question, input inventory, desired outputs, and success criteria. Record metadata and data-transfer logistics.
Output: signed scope document; `sample_manifest.csv` draft
Review literature and benchmark candidate tools against your data structure. Document assumptions and limitations before coding [3].
Output: written method recommendation with tool/version rationale
Draft a modular pipeline specification with checkpoint milestones and compute requirements [4].
Output: pipeline specification; milestone schedule
Build a conda environment or container with exact software versions. Lock dependencies before production runs [6].
Output: `conda-lock.yml` or container digest in `analysis_parameters.yaml`
When raw sequencing or vendor outputs are in scope, apply modality-specific QC with documented exclusion thresholds [4].
Tools used: fastp; FastQC; MultiQC 1.25; custom QC scripts as scoped
Output: per-sample QC tables; `multiqc_report.html` when applicable
Run project-specific statistical, integration, or modeling steps using tools selected at feasibility review, not a fixed catalog stack.
Output: intermediate and final result tables
Test parameter robustness or hold-out samples where the study design supports it. Document instability before figures are finalized.
Output: sensitivity summary in QC report or README
Produce manuscript-ready panels with source data exported alongside each figure.
Tools used: ggplot2; matplotlib; ComplexHeatmap (when scoped)
Output: PDF/SVG figures; `figure_data/` plotting tables
Assemble a README with reproduction instructions, a Methods draft, and an optional walkthrough with your scientific contact.
Tools used: Quarto or RMarkdown; Git tag for delivery snapshot
Output: README; Methods draft; tagged workflow release
Respond to reviewer questions about methods, parameters, and outputs within agreed scope using the archived environment.
Output: clarification memos; minor revisions when scoped
Infrastructure tools are version-pinned on every custom project. Modality-specific software is chosen per scope and listed in the Methods draft.
| Tool | Version | Role | Primary citation |
|---|---|---|---|
| Snakemake | Pinned per project | Workflow orchestration; reproducible rule-based pipelines | [8] — https://doi.org/10.12688/f1000research.29032.2 |
| Nextflow | 24.x+ | Portable workflow execution; HPC and cloud scaling | [9] — https://doi.org/10.1038/nbt.3820 |
| conda / mamba | 24.x | Environment and dependency management | [6] — https://doi.org/10.1038/s41592-018-0046-7 |
| Docker / Singularity | Current LTS | Containerized execution environments | [10] — https://doi.org/10.1371/journal.pcbi.1014197 |
| Git | 2.x | Version control for scripts and workflow files | [4] — https://doi.org/10.1371/journal.pcbi.1005510 |
| MultiQC | 1.25+ | Aggregated QC reporting across pipeline steps | [7] — https://doi.org/10.1093/bioinformatics/btw354 |
| MOFA+ | When scoped | Multi-omics factor integration | [11] — https://doi.org/10.1186/s13059-020-02015-1 |
| R / Python | 4.4.x / 3.12 | Statistical analysis, visualization, and custom scripting | R Core Team [12]; Python Software Foundation [12] |
Bespoke projects often have unclear scope, weak reproducibility, and messy integration. Pepkio uses written milestones and version-pinned deliverables.
Pepkio locks deliverables, milestones, and acceptance criteria in a written scope document before analysis begins.
More than 70% of 1,576 surveyed researchers reported failing to reproduce another scientist's experiment [16]. Pepkio archives exact software versions, parameters, and random seeds in lockfiles and `analysis_parameters.yaml`.
Combining datasets from different batches or labs requires careful method selection [17]. Pepkio runs exploratory QC and applies agreed correction only after you review batch structure.
Pepkio wraps client modules in containerized Snakemake or Nextflow steps with documented inputs and outputs.
Pepkio delivers a Methods draft listing tool versions, parameters, and QC thresholds, plus clarification support after delivery.
Provide data files, sample metadata with conditions and covariates, the biological question, and any preferred methods or publications. For proprietary formats, include a data dictionary or parser example. Pepkio confirms feasibility and lists required fields in `sample_manifest.csv` before work begins.
Focused modules typically complete in 3-6 weeks. Multi-omics integration, novel assay QC, or pipeline ports may take 6-12 weeks. Checkpoints occur after feasibility, QC, and before delivery. Exact timelines are confirmed at kickoff.
You receive result tables, PDF/SVG figures with source data, workflow scripts, environment lockfiles, a README, and a Methods draft. QC reports and optional Git or Zenodo archival are included when scoped.
Yes, when feasibility is confirmed at intake. Pepkio supports common sequencing, mass-spec, proteomics, and spatial platforms after a pilot review validates data structure. Novel formats require subset QC before scope is locked.
Yes. That is the main purpose of this service. Non-catalog analyses receive milestone-scoped quotes after intake and feasibility review.
Low-quality samples are flagged with explicit metrics in the QC report. Pepkio proceeds with agreed exclusions and documents the impact on statistical power before testing. Re-sequencing needs are identified at the QC milestone.
Yes. You retain full ownership of deliverables. Pepkio provides commented scripts with lockfiles or container digests so your team can rerun the workflow when the execution environment matches the pinned setup.
Yes, when scope allows. Pepkio can wrap client scripts as containerized modules and connect them to downstream steps within agreed confidentiality boundaries.
Yes. Checkpoint reviews occur after feasibility, QC, and before final delivery. You can review metadata, filtering, and contrast definitions within agreed scope with your dedicated scientific contact.
Methods clarification and minor revisions within agreed scope (typically up to 20% of deliverables) are covered under Pepkio's reviewer-support policy. Substantial new analyses are scoped as separate milestones.
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