Sequencing produces a lot of data, and getting it into a form a researcher or clinician can use is engineering work.
We build and maintain the software between the sequencer and the final result. That includes NGS pipelines in Nextflow, WDL and Snakemake, variant calling with GATK, the reference databases and applications around them, and the testing needed to keep runs reproducible. We work with genomics companies, research groups, and diagnostics labs.
We build the pipelines that run the analysis. They use tools such as BWA for aligning reads, GATK for variant calling and SAMtools for working with the resulting files.
The pipeline needs to run the same way on every sample, on a cluster or in the cloud, with nobody watching each run. Two years later, the team should still be able to show how a result was produced.
Inputs may be raw reads, and the output could be a VCF, an annotated report, a database record or an application a clinician uses.
tool versions pinned in containers · clear inputs and outputs between stages · reference samples run again whenever the pipeline changes
We work in whichever engine the team already uses: Nextflow, WDL or Snakemake.
A team on Cromwell and WDL can stay there. A lab with years of Snakemake rules does not have to move either. Our part is how the pipeline is built, tested and maintained afterwards, and we keep that separate from the engine because it makes changes easier to review.
For new pipelines, we usually recommend Nextflow. nf-core gives the team a tested starting point, and the same workflow can run on a laptop, an HPC scheduler, or AWS Batch. GenXFlo also lets a scientist put a pipeline together without having to wait for an engineer.
Getting the same result from the same analysis six months later is usually the hard part.
Tool versions change, reference genomes get updated, and a dependency inside a container can shift without anyone noticing. The pipeline still runs. The result may have moved slightly, though, and sometimes that only shows up when someone compares an older result with a new one.
Tool versions are pinned in the container.
Each stage has clear inputs and outputs, so a bad file should fail at that stage and go no further.
Reference samples with known results run again in continuous integration whenever the pipeline changes, and we check concordance against those results.
Raw inputs are kept, so any output can be traced back to the exact file and software version that produced it.
We use the same controls when a pipeline has to stand up to validation or a submission.
Common pipeline projects
A pipeline that works, but does not scale
We make the existing pipeline's runs reproducible, then add the tests and documentation the team needs to maintain it.
The science is done but the software around it is not
We build the web application, APIs, reference databases and job orchestration with the client's engineering team. Some of those engagements last for years.
A pipeline that has not been built yet
For a new assay, panel or data type, we usually start with Nextflow and use nf-core components where they fit. Validation runs in parallel with development.
Sometimes one engineer joins an existing team, and in other cases we run a dedicated team of five or more. One genotyping platform engagement started with one engineer and grew to four. The mutation calling platform had a team of five.
Clients
Our genomics clients include platform and instrument companies, cancer diagnostics companies, the research organisation of a large pharma company, and oncology data companies. A client with a large bioinformatics team may only need extra engineers. When the client's team is small, we cover much more of the software work.
The science belongs to the client. We build the software around it, then do the engineering to run it at the scale and under the controls the work requires. We do not name clients on this site, and the case studies have client details removed.
NGS pipeline development
The workflow from raw sequencer output to analysis ready data, plus the testing and operational work that keeps it running.
Variant calling and analysis
The step where a sample's differences from a reference genome are identified, filtered, and annotated, and where germline and somatic workflows diverge.
Statistical analysis for biological data
Analysis of variants and expression values once they are available, carried through to a result the people reviewing it can use.
Companion diagnostics
When a test influences treatment, the analysis has validation and traceability requirements that are different from research work.
GenXFlo
Our tool for designing and running Nextflow workflows visually, for teams that want to build pipelines faster without a dedicated pipeline engineer for every change.
Read about GenXFlo
A modular mutation calling pipeline, alignment through reporting, validated stage by stage in TypeScript, with 98 percent automated test coverage on the web application.
Read the case study →End to end engineering for a custom genotyping assay design platform: Nextflow pipelines, NCBI reference databases, Angular and .NET. Time to market up 50 percent.
Read the case study →Common questions
Do you work in Nextflow, WDL or Snakemake?
All three. New pipelines are usually Nextflow, and an existing pipeline stays in whatever engine it already runs in. The reproducibility practices do not depend on the engine: pinned containers, staged contracts and reference sample tests.
Can you take over a pipeline someone else wrote?
Yes, and we do it often. We start with a set of reference samples with known outputs, then rerun them as we make changes to check the pipeline still gives the same answers.
Do you build the web application as well as the pipeline?
Usually. Most engagements include the application researchers use to configure runs and review results, along with the APIs that connect it to other systems and the databases underneath.
What is GenXFlo and do we have to use it?
GenXFlo is our tool for building and running Nextflow pipelines visually. You do not have to use it. We bring it in where it makes the work faster, or easier for the client's scientists to maintain.
How do you handle validation for clinical or regulated use?
We set the pipeline up for validation from the first day, with pinned versions, traceable inputs and automated tests whose evidence is kept. For work headed to a submission, our regulatory and validation team runs the validation while the build is under way.
A pipeline that needs to scale, move or pass validation
We work with genomics and bioinformatics teams on the pipeline itself and on the platform and applications around it.
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