Production-grade NGS pipeline development and execution — Nextflow, WDL, and Snakemake pipelines for WES, WGS, RNA-Seq, somatic variant calling, and variant annotation — auto-scaling on cloud-native bioinformatics infrastructure with Kubernetes orchestration and full observability.
Six production-grade capabilities. One platform. Built for clinical labs running high-throughput whole exome sequencing, whole genome sequencing, and targeted panel workflows at scale.
WES, WGS, RNA-Seq analysis pipeline, and targeted panel execution — germline and somatic variant calling workflows — running on Nextflow (DSL2), WDL, and Snakemake with configurable profiles for AWS Batch, GCP Life Sciences, and Azure Batch.
Kubernetes genomics pipeline orchestration on EKS, GKE, and AKS — auto-scaling node pools matched to workload profiles, spot instance cost optimisation, and automatic retry on preemption. No idle capacity, no queue delays.
Somatic variant calling pipeline with Mutect2, Strelka2, and ensemble approaches — plus germline calling with GATK HaplotypeCaller and DeepVariant. Benchmarked against SEQC2 and NIST GIAB reference datasets before clinical deployment.
End-to-end variant annotation pipeline development — VEP, ANNOVAR, SnpEff — with gnomAD, ClinVar, COSMIC, OncoKB, and in-silico pathogenicity scoring (REVEL, CADD, SpliceAI) configured per assay type.
Real-time dashboards for run status, per-sample throughput, queue depth, and cost-per-sample. Full audit trail per run — tool versions, parameter sets, reference build, input checksums. Immutable and query-able.
Sequencer-to-pipeline automation with direct LIMS integration — sample sheet parsing on run completion, status push-back to LIMS, and HL7 FHIR result delivery to downstream clinical reporting and EHR systems.
The platform deploys on AWS, GCP, and Azure — HIPAA-compliant genomics infrastructure with VPC isolation, KMS encryption, IAM enforcement, and immutable audit trails across all pipeline stages. Supports hybrid HPC + cloud architectures for organizations with existing on-premises compute investments.
Tell us your assay types, sample volumes, and current compute environment. We will scope the deployment.
How is a managed bioinformatics pipeline platform different from running pipelines in-house?
Running NGS pipelines in-house means your team manages infrastructure provisioning, compute scaling, software versioning, failure recovery, and cost tracking alongside the actual science. A managed bioinformatics pipeline platform abstracts that operational overhead entirely. Pipelines execute on auto-scaling Kubernetes infrastructure — scaling to your sample volume, recovering from failures automatically, tracking cost-per-sample in real time, and maintaining full audit trails per run — without your bioinformatics team spending time on infrastructure. For clinical labs under TAT pressure, this typically translates to 60–80% faster turnaround and a 30–50% reduction in compute costs versus self-managed pipeline infrastructure.
60–80%
Faster TAT vs self-managed
30–50%
Reduction in compute costs
Which NGS pipeline types does the platform support?
The platform supports the full range of clinical and research NGS workloads: whole exome sequencing pipeline software and whole genome sequencing analysis for germline variant discovery; somatic variant calling pipelines for oncology panels and liquid biopsy; RNA-Seq analysis pipeline service for gene expression, fusion detection, and splice variant analysis; targeted gene panel execution for hereditary disease, pharmacogenomics, and cardiology; and variant annotation pipeline development with configurable annotation sources per assay type. All pipeline types support both single-sample and batch processing modes.
