Frequently Asked Questions

For larger projects, we define clear milestones (e.g., QC & feasibility, primary analysis, downstream enrichment, final report). An initial payment starts the project, with subsequent payments triggered upon completion of each milestone. For small or simple projects, there may be a single milestone upon project completion.

Currently, we operate on a per-project basis. Long-term or recurring work can be structured as a series of linked projects.

No. We support everything from small tasks (e.g., debugging a script or interpreting a figure) to large, multi-stage studies.

We will discuss any scope changes promptly and agree on updated timelines and costs before proceeding.

The consultation is research-focused. We aim to understand your scientific goals and then discuss feasibility, timelines, and cost.

We regularly support rush projects. Expedited timelines may involve an additional fee, which will be discussed upfront.

We primarily use reproducible Python and R scripts or workflows. If you have preferred tools or formats, we are happy to adapt.

Yes. We have extensive experience with niche platforms and custom or non-standard data types.

We provide both processed data and downstream analysis outputs, as appropriate for the project.

Yes. We can integrate client-provided pipelines with custom downstream analyses.

We design integration strategies based on study design and data structure, focusing on correlation, joint modeling, and biologically meaningful signal integration. Strategies are discussed and agreed upon with you.

We start with exploratory methods (e.g., PCA) and apply established batch-correction tools as needed. More advanced methods can be used for complex designs.

This support covers explaining, justifying, and clarifying analyses we performed, including drafting technical responses to reviewers. There is no strict time limit; most support occurs within the typical peer-review cycle (usually within one year).

Clarification and defense of existing analyses are included. Substantial new analyses requested by reviewers are treated as a new project.

Yes, upon request.

Typically as Python scripts or R scripts. Other formats (e.g., R Markdown, Jupyter Notebooks) are available upon request.

We provide a detailed, publication-level Methods draft and all technical details. Final polishing for top-tier journals may require minor edits by the authors.

No. We operate strictly as a fee-for-service provider unless co-authorship is explicitly discussed in advance.

We primarily use secure cloud infrastructure (e.g., AWS). Depending on project needs, analysis may also be performed on secured workstations operated by authorized Pepkio scientists. All environments follow the same data protection and access-control requirements.

Yes. We follow applicable regional data protection and privacy regulations.

For projects without peer-review support, data is retained for one month after delivery. Immediate deletion can be requested.

Yes.

Competing projects are assigned to separate bioinformaticians with strict access controls and no cross-project visibility.

A PhD-level bioinformatician serves as your primary contact. They are experienced in bioinformatics analysis, though they may not be a deep domain expert in every specific biological niche. When needed, they consult internally to ensure accurate and appropriate analysis.

Email is the default, with additional communication as needed.

Yes.

Yes. Please let us know in advance, as training requires additional time and planning.

If QC is based only on existing reports (e.g., from a sequencing provider), we do not charge. If QC requires additional data analysis, a small, proportional fee applies.

They are a vetted network of experienced bioinformaticians. We maintain strict QC, reproducibility standards, and backup coverage to ensure reliability.

You own all custom-developed scripts and algorithms created specifically for your project. Standard analysis components (e.g., alignment or quantification code) may reuse common internal workflows used across projects.

It connects you with a real scientist. We may use AI internally to improve productivity, but communication is human-led.

Yes.