Absolutely. Real projects change once the data starts speaking. We build in decision points, propose alternative approaches when needed, and help you follow the science instead of forcing the work into a rigid pre-set plan.

Yes. We can keep your analysis moving while you recruit, and we also offer contingency recruiting for internal bioinformatics hires.

Very likely. We support a wide range of therapeutic areas, study designs, and data types. When a project is highly specialized, we match you with the closest-fit domain expert and propose an analysis plan aligned to your platform, endpoints, and scientific goals.

We start with a clear analysis plan, defined checkpoints, and agreed deliverables. Work is performed in reproducible environments with documented parameters and code, and we share interim outputs early so you can review direction before final delivery.

Yes. Our website highlights common requests, but a large share of our work is custom. Tell us your goal, study design, and data type, and we’ll outline what is feasible, where the risk is, and what we recommend.

We support projects across single-cell, bulk transcriptomics, genetics, proteomics, spatial data, and multi-modal studies, as well as statistical modeling and applied machine learning. When a project does not fit neatly into a standard category, we scope the work around the biological question rather than forcing it into a template.

Hiring is slow, expensive, and rarely gives you every specialty a project needs. The Bioinformatics CRO gives you immediate access to senior, PhD-level expertise across single-cell & bulk omics including multiomics, genetics, proteomics, spatial biology, and machine learning, all without the overhead of building a full internal team. You can scale support up or down as your needs change.

Yes. You work directly with the senior bioinformatician(s) assigned to your project, not through layers of account management. That keeps communication fast, makes it easier to ask technical questions, and helps the analysis stay aligned with the science.

Yes. We support analysis plans, feasibility and power considerations, preliminary analyses when appropriate, letters of support, and scoped budgets tied to project aims.

Yes. We can work inside your cloud account, secure workspace, or institutional environment so the data stays where it already lives.

No. Many projects start with goals, constraints, and partially defined questions, not a finished technical spec. We translate that into a practical analysis plan with assumptions, milestones, and decision points.

We begin with a short scoping call and a quick review of your goals, study design, and available data. From there, we propose a plan with deliverables, timeline, and an estimate. When uncertainty is high, we may recommend a small discovery block to de-risk the full project.

We support secure cloud buckets such as S3, encrypted file transfer methods, institutional transfer tools, or direct work in your environment through your cloud or VPN. We align the setup to your security, IT, and governance requirements.

We identify issues early during QC, explain the likely impact, and recommend options. Depending on the findings, that may include reprocessing, filtering, batch correction strategies, revised inclusion criteria, or narrowing the scope so the final output remains scientifically defensible.

That is common. We often begin with a short discovery phase focused on data intake, QC, and exploratory analysis to assess readiness and turn ambiguity into a concrete project plan.

Yes. We expect priorities to shift as new findings emerge. When a scope change materially affects budget or timeline, we discuss the tradeoffs and the revised plan before proceeding.

We can support either model depending on the project. Fixed-fee works well when scope is clear; phased or time-based engagements are often better when the science is exploratory or likely to evolve.

Smaller analyses may run from a few days to a couple of weeks, while larger multi-omics programs or workflow builds may span multiple milestones. After scoping, we provide a realistic schedule and share interim readouts so decisions can happen before the final package is delivered.

Cost depends on the data type, sample count, study design complexity, and deliverables. After scoping, we provide a clear estimate or estimate range, and we can structure work around budget caps, phased milestones, or fixed-fee components when scope is well defined.

You should expect clear milestones, regular communication, and interim outputs. We tailor the exact cadence to the project, but the goal is always the same: fast feedback, early visibility, and no surprises.

Yes. We can package methods, code, assumptions, and environment details in a way that makes internal continuation much easier. We can also brief your team directly so important context does not get lost.

No. Authorship is never required. If you determine our contribution meets journal criteria, we’re happy to be coauthors; otherwise, an acknowledgment is completely fine.

You receive a results package tailored to the engagement. Typically this is a report plus figures, tables, and interpretation of the key findings. When requested, we also provide reproducible assets such as code or notebooks, parameters, and documented environments such as Conda or Docker so the analysis can be rerun.

We include a walkthrough of the results and can schedule follow-up sessions to translate findings into next steps. We also support validation planning, manuscript figures, future analyses, and internal readouts to scientific or leadership teams.

Yes. You retain ownership of your data and project outputs, subject to the governing NDA, MSA, and SOW terms. Our role is to generate insight and deliverables while protecting confidentiality and ownership.

Yes, when that is part of the scope. We can provide code, notebooks, parameter settings, and environment documentation sufficient to support reproducibility and handoff.