Who Bridge Horizon is for
Four roles, one record of how each result was produced. Pick yours.
- 01
Core Facility or NGS Service Provider
Deliver FASTQs and processed outputs with the pipeline that produced them, no shipped drives or shared S3 keys.
- Per-project or per-customer access, revocable with a set expiry date
- Pinned pipeline version, reference, and container digest recorded with every FASTQ and BAM delivered
- 02
Small to Mid-size Biotech
Run RNA-seq, ATAC-seq, or variant-calling pipelines without command-line work.
- Pre-configured nf-core pipelines: bulk/scRNA-seq, ATAC-seq, WGS/WES
- BAM, counts matrix, MultiQC HTML, and VCF returned with the exact run record
- 03
Preclinical CRO
Keep study metadata, sequencing runs, and analyses in one record per study.
- Samples tagged with study arm, time point, and treatment group, joined to each run
- Close-out export per study: FASTQs, BAMs, analysis notebooks, parameters, and access log
- 04
Academic PI, Postdoc, or Graduate Student
Take a count matrix to figures without provisioning compute.
- Start from a 10x count matrix, h5ad, or .rds without a local install
- Seurat / Scanpy / DESeq2 / edgeR notebooks versioned alongside every figure
Why Horizon exists
Seven recurring pains in computational biology, and what Horizon does about each.
- 01
The infrastructure tax.
Before you run a single job, you're filing tickets. Waiting on IT. Explaining what a compute node is to someone who manages Windows laptops. Getting cloud infrastructure stood up is a weeks-long detour that has nothing to do with your science.
No servers to provision. Jobs run on managed AWS Batch; open a browser and submit. - 02
Building the pipeline before you can use it.
A gold-standard RNA-seq pipeline isn't something you download. It's assembled, containerized, stress-tested, and validated — a multi-week effort requiring real computational expertise. Most bench scientists either skip this step and accept substandard results, or wait for an informatician who's already buried.
Horizon ships curated, production-validated pipelines on day one. Built by computational biologists. You start from a validated baseline, not a blank repo. - 03
Cloud integration is its own engineering project.
AWS Batch, IAM roles, S3 permissions, job queues — connecting a bioinformatics pipeline to cloud compute is a discipline unto itself. It's not your discipline, and it shouldn't have to be.
Every pipeline runs on AWS Batch under the hood. You submit a job; Horizon handles the infrastructure, scaling, and teardown invisibly. - 04
Re-running for every parameter tweak.
Adjusting a p-value threshold or fold-change cutoff should take seconds — like tasting a dish and adding salt. Instead it means filing a re-run request, joining a queue, and waiting hours or days. Want to change your sample-to-sample comparison? Same process, all over again.
Parameter changes are fully self-serve. Adjust thresholds, cutoffs, or sample groupings directly in the UI and resubmit in seconds. No ticket. No queue. No middleman. - 05
Sharing results is a multi-day production.
A completed analysis sitting on a server is worth nothing until your collaborators can see it. Exporting figures, packaging outputs, and transmitting securely can take days — during which your collaborator is blocked and the science is stalled.
A shareable link is generated when a run completes. Recipients see figures, outputs, and the exact parameter set without an export step. - 06
Reproducibility lives in someone's memory.
Eighteen months after a run, a reviewer asks exactly which pipeline version, parameter set, and input files produced Figure 3. Or a regulatory submission requires a complete computational audit trail. If that record wasn't captured automatically, reconstructing it is an archaeological dig — and sometimes the artifact is just gone.
Every Horizon run generates an immutable, timestamped log — inputs, parameters, software versions, outputs. Locked. Permanent. Ready for a publication methods section or a regulatory filing, on demand. - 07
Data tied to a person, a server, or a contract.
Datasets stored on an individual's machine, a local server, or a CRO's infrastructure are one offboarding or budget cut away from disappearing. In a field where longitudinal data is irreplaceable, this is an unacceptable fragility.
Horizon provides year-long or perpetual data availability on managed cloud storage — independent of any individual, server, or third-party service relationship.