Every research organization eventually hits the same wall: a pipeline that worked fine on a single workstation or a small on-premises cluster no longer holds up. Sample volume grows. A key contributor leaves, taking undocumented knowledge with them. Regulatory requirements evolve. Leadership wants results in hours, not weeks.
Over more than a decade, BioTeam has helped research organizations navigate these moments. Our experience spans dozens of pipeline and workflow migrations across pharma, biotech, diagnostics, and government research, ranging from single-team proof-of-concept migrations to population-scale genomics workflows processing hundreds of thousands of samples.
No two migrations look alike. A clinical genomics pipeline moving to a cloud environment has little in common, technically, with a computational chemistry team standardizing a collection of research scripts into reusable modules. But across these engagements, certain patterns emerge.
The ten de-identified examples below illustrate distinct technical challenges and the lessons we have learned solving them.
1. Recovering a Failed Migration with a High-Volume Clinical Diagnostics Client
A previous attempt to move a high-volume clinical genetics pipeline to the cloud had already failed. The cloud implementation was significantly slower than the original on-premises deployment; the deployment approach was not reliably reproducible, and the bioinformatics team had lost confidence in the migration.
BioTeam came in not to start over, but to determine why the first attempt failed. We then helped re-architect the pipeline using a modern workflow engine and conducted a phased evaluation of managed cloud options.
Lesson: Sometimes the hardest part of a migration is not the technology. It is rebuilding trust after a previous attempt has failed.
2. Consolidating One Pipeline Out of Many with a Global Pharma (Top-10) Client
A drug discovery research group had accumulated more than a dozen single-purpose scripts, developed by different scientists for different models, with no shared structure.
BioTeam converted the collection into standardized, parallelizable workflow modules with configurable compute resources, creating a more consistent and reusable foundation for the research team.
Lesson: Migration is not always about moving somewhere new. Sometimes it is about giving many one-off tools a common structure for the first time.
3. Choosing a Workflow Language Before Writing a Line of Code with a Global Biotech (Top-Tier) Client
Rather than immediately selecting a workflow technology, this organization wanted a structured, vendor-neutral comparison of leading workflow languages and frameworks, including CWL, WDL, Snakemake, and Nextflow.
BioTeam evaluated the options against the organization’s scientific, technical, and operational requirements. The resulting assessment helped establish the technical foundation for a broader pipeline modernization strategy.
Lesson: The best migration decision is sometimes the one you make before you migrate anything.
4. Migrating a Pipeline as Part of a Larger Data Platform with a Global Pharma (Top-10) Client
This organization was migrating genomic and patient data from on-premises storage into a large-scale cloud data lake. Pipeline work couldn’t be designed in isolation. It had to fit within a multi-account data governance model that covers data movement, access control, and analysis methods across the command line, CWL, and Nextflow.
BioTeam helped design the data lake architecture and the analysis methods layered on top of it.
Lesson: Pipeline migration is sometimes one piece of a much larger data-platform migration, and it has to be designed alongside data governance, not bolted on afterward.
5. Choosing Not to Rebuild the Pipeline at All with a High-Volume Clinical Diagnostics Client
Facing tens of thousands of exome samples a year, this organization needed a rigorous, apples-to-apples comparison of building a cloud-native version of its existing pipeline versus adopting a commercial diagnostic-grade platform. BioTeam ran the evaluation across nine secondary-analysis platforms, weighing cost, capability, and regulatory complexity.
The client ultimately chose to adopt a commercial platform rather than replicate its on-premises pipeline in the cloud.
Lesson: The right migration decision is sometimes not to migrate the existing pipeline at all, but to replace it with something purpose-built.
6. Migrating a Validated Pipeline Without Breaking It with a High-Volume Clinical Diagnostics Client
A clinical pipeline already in production needed to move onto a fully managed cloud workflow service. BioTeam reprogrammed the existing scripts into a standard workflow definition language, containerized each step, and built the orchestration layer needed to run it reliably at production scale.
Lesson: When a pipeline is already validated and in clinical use, the goal of a migration isn’t to improve the pipeline; it’s to ensure that the process remains stable and continues to produce the same results throughout the transition.
7. Simplifying a Pipeline at Population Scale with a Global Biotech (Top-10) Client
A legacy phasing pipeline required multiple manual steps to execute and was not designed for the scale the research program had reached.
BioTeam consolidated the workflow into a single automated process, incorporated a faster phasing approach, and scaled it to population-genomics volumes involving hundreds of thousands of samples. The resulting workflow supported downstream scientific discovery at a scale the original pipeline could not practically accommodate.
Lesson: At real scale, the biggest improvement is sometimes simplification, not additional infrastructure.
8. Moving a Specialized Imaging Pipeline, Not Just a Genomics One with a Global Biotech (Top-10) Client
Cryo-EM data processing pipelines do not behave like standard NGS pipelines. They involve different data characteristics, compute profiles, storage requirements, and performance bottlenecks.
BioTeam assessed the environment and developed recommendations for scaling pipeline execution for a scientific domain that many standard cloud and workflow migration approaches do not address well.
Lesson: Pipeline migration experience in genomics does not automatically transfer to scientific imaging, or vice versa. Domain expertise matters as much as tooling expertise.
9. Moving the Pipeline First, Improving It Second with a Biotech Client
This organization needed to quickly unlock scalable cloud compute for a standard NGS mapping and variant-calling pipeline. Rather than rewrite it, BioTeam ported the existing pipeline as-is, then layered in automation to minimize the ongoing manual effort of running it.
Lesson: Not every migration needs a rewrite. Sometimes the fastest path to scale is moving what already works, and only improving it once it’s there.
10. Migrating a Pipeline With an Institutional Memory Gap with a Biotech Client
A research organization needed to modernize an elastic HPC and workflow environment, but much of the original pipeline architecture had been developed by people who were no longer with the company.
Before BioTeam could modernize the environment, we first had to reconstruct how it worked and understand the intent behind the original design from the remaining artifacts.
Lesson: A surprising number of migrations begin with archaeology, not engineering.
What Ties These Together
Ten engagements. Different organizations, scientific domains, technologies, and technical problems. But several principles consistently shape BioTeam’s approach.
- Validation before speed. For migrations involving clinical or regulatory-adjacent data, BioTeam prioritizes validation and concordance against established results. A faster pipeline is not an improvement if the team cannot trust the output.
- Workflow-language agnosticism. BioTeam does not arrive with a predetermined favorite tool. Nextflow, WDL, CWL, Snakemake, and other approaches are evaluated based on the actual requirements of the scientific team, data, infrastructure, and operating environment.
- Willingness to pick up where someone else left off. Some engagements begin after a migration has stalled or a previous approach has failed. Understanding why it failed can be one of the most valuable parts of the work.
- Domain expertise matters. Genomics, computational chemistry, structural biology, clinical diagnostics, and other scientific domains create different requirements. Successful pipeline modernization requires understanding both the science and the infrastructure.
- Range matters too. The same underlying engineering discipline can apply to a research group’s collection of individual scripts or a population-scale workflow processing hundreds of thousands of samples.
Is Your Pipeline Outgrowing Its Architecture?
Whether you are planning your first migration, modernizing a legacy workflow, scaling an existing pipeline, or trying to recover a stalled migration, BioTeam can help you determine what needs to change and build a path forward.
Contact BioTeam at info@bioteam.net to start a conversation.
