Custom Bioinformatics Analysis Services — Bespoke, Version-Pinned Workflows for Non-Standard Data and Research Questions

Key facts

Key terms: 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 (Sandve et al., 2013). A reproducible research compendium bundles data manifests, code, parameters, and outputs (Wilson et al., 2017). Multi-omics integration combines measurements from multiple molecular layers to model shared and layer-specific variation (Argelaguet et al., 2018).

What Is Custom Analysis?

Custom bioinformatics analysis is the outsourced execution of computational workflows that do not map to a fixed catalog pipeline — novel assays, mixed modalities, client-specified methods, or extended modeling on data you already hold. It answers: How do we answer this 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) as teams outsource specialized analysis (Research and Markets, 2025). Pepkio scopes each project with milestone-based pricing and a dedicated PhD-level scientific contact.

What Custom Analysis Can Answer

Custom analysis links heterogeneous data and non-standard methods to concrete biological decisions. Representative questions with published examples:

• Which protein-level changes in ccRCC are invisible to RNA-seq alone? Clark et al. (2019) profiled 103 tumors and 80 normal adjacent tissues, identifying 820 differentially abundant proteins — including oxidative phosphorylation uncoupling not captured at the mRNA level.
• How can spatial transcriptomics be deconvolved without a matched single-cell reference? Miller et al. (2022) developed STdeconvolve, recovering cell-type profiles from 10x Visium, Slide-seq, and DBiT-seq data without external scRNA-seq.
• Which latent factors drive variation across jointly profiled omics layers? Argelaguet et al. (2018) applied MOFA to 200 CLL samples, integrating somatic mutations, RNA, DNA methylation, and ex vivo drug response.
• Can graph machine learning integrate bulk multi-omics when concatenation fails? Valous et al. (2024) reviewed graph-based workflows that model cross-layer dependencies rather than naive feature concatenation.
• How should a lab port an internal script into a reproducible pipeline? Mölder et al. (2021) showed Snakemake with conda and containers enables portable analysis from laptops to HPC.

Services included in this category

This category has no sub-spoke pages; each project is individually scoped at intake. Common project archetypes (tools selected when scoped):

What Pepkio delivers

Every custom project returns version-pinned code, auditable outputs, and a Methods draft — plus reviewer clarification within agreed scope.

How the analysis works — step by step

1. 1. Intake and scope definition

   Confirm the biological question, input inventory, desired outputs, and success criteria; record metadata and data-transfer logistics.

   Tools and outputs

   Output: signed scope document; `sample_manifest.csv` draft

2. 2. Feasibility and method selection

   Review literature and benchmark candidate tools against your data structure; document assumptions and limitations before coding (Sandve et al., 2013).

   Tools and outputs

   Output: written method recommendation with tool/version rationale

3. 3. Workflow design

   Draft a modular pipeline specification with checkpoint milestones and compute requirements (Wilson et al., 2017).

   Tools and outputs

   Output: pipeline specification; milestone schedule

4. 4. Environment pinning

   Build a conda environment or container with exact software versions; lock dependencies before production runs (Grüning et al., 2018).

   Tools and outputs

   Output: `conda-lock.yml` or container digest in `analysis_parameters.yaml`

5. 5. QC and preprocessing

   When raw sequencing or vendor outputs are in scope, apply modality-specific QC with documented exclusion thresholds (Wilson et al., 2017).

   Tools and outputs

   Tools used: fastp; FastQC; MultiQC 1.25; custom QC scripts as scoped

   Output: per-sample QC tables; `multiqc_report.html` when applicable

6. 6. Core analysis execution

   Run project-specific statistical, integration, or modeling steps using tools selected at feasibility review — not a fixed catalog stack.

   Tools and outputs

   Output: intermediate and final result tables

7. 7. Validation and sensitivity checks

   Test parameter robustness or hold-out samples where the study design supports it; document instability before figures are finalized.

   Tools and outputs

   Output: sensitivity summary in QC report or README

8. 8. Figure and table generation

   Produce manuscript-ready panels with source data exported alongside each figure.

   Tools and outputs

   Tools used: ggplot2; matplotlib; ComplexHeatmap (when scoped)

   Output: PDF/SVG figures; `figure_data/` plotting tables

9. 9. Documentation and handoff

   Assemble README with reproduction instructions, Methods draft, and optional walkthrough with your scientific contact.

   Tools and outputs

   Tools used: Quarto or RMarkdown; Git tag for delivery snapshot

   Output: README; Methods draft; tagged workflow release

10. 10. Post-delivery support

    Respond to reviewer questions about methods, parameters, and outputs within agreed scope using the archived environment.

    Tools and outputs

    Output: clarification memos; minor revisions when scoped

Tools and standards we use

Infrastructure tools are version-pinned on every custom project. Modality-specific software is selected per scope and listed in the Methods draft.

Common challenges — and how we handle them

Bespoke projects face scope, reproducibility, and integration risks that standard catalog pipelines do not — Pepkio addresses each with documented milestones and version-pinned deliverables.

Undefined scope leads to rework on bespoke projects

Pepkio locks deliverables, milestones, and acceptance criteria in a written scope document before analysis begins.

Missing version pins make results impossible to reproduce

More than 70% of 1,576 surveyed researchers reported failing to reproduce another scientist's experiment (Baker, 2016). Pepkio archives exact software versions, parameters, and random seeds in lockfiles and `analysis_parameters.yaml`.

Integrating heterogeneous multi-omics data raises computational and biological challenges

Combining datasets from different batches or labs requires careful method selection (Rappoport & Shamir, 2018). Pepkio runs exploratory QC and applies agreed correction only after you review batch structure.

Proprietary in-house code lacks portability across machines

Pepkio wraps client modules in containerized Snakemake or Nextflow steps with documented inputs and outputs.

Reviewers request methods detail that standard pipeline descriptions do not cover

Pepkio delivers a Methods draft listing tool versions, parameters, and QC thresholds, plus clarification support after delivery.

Common questions

Related services

• Transcriptomics — RNA-seq or single-cell modules within a broader custom project.
• Genomics — Variant or SV analysis feeding multi-omics integration.
• Proteomics — Protein quantification for custom multi-omics designs.
• Metagenomics — Microbiome profiling combined with host omics.
• Machine learning — Predictive modeling for custom classifier builds.
• Statistical analysis — Experimental design before custom execution.
• Bioinformatics consulting — Feasibility review before committing to a custom project.