Bioinformatics Pipeline
Reproducible bioinformatics pipelines that transform raw biological and sequencing data into analysis-ready results with consistent, traceable workflows.
TYPICAL CLIENT
A genomics or sequencing team drowning in one-off scripts instead of a real pipeline
Usually a Head of Bioinformatics or Lab Director whose current workflow depends on a handful of scripts one person understands, and who needs results that come out the same way regardless of who runs the pipeline or which machine it runs on.
Bioinformatics Pipeline, broken into eleven services
Each can be scoped on its own, or combined into a single end-to-end engagement.
Whole Genome Sequencing
Full-genome alignment, variant calling and structural variant detection.
View WESWhole Exome Sequencing
Deep coding-region coverage with clinical-grade variant annotation.
View AMPAmplicon Sequencing
Primer-aware analysis for targeted gene panels and pathogen genotyping.
View CHIPChIP Sequencing
Peak calling and reproducibility QC for protein-DNA binding studies.
View WGMShotgun Metagenomics
Taxonomic and functional profiling straight from raw community DNA.
View 16S16S rRNA
Fast, cost-effective microbial community composition profiling.
View RNATotal RNA-Sequencing
Coding and non-coding transcriptome profiling with differential expression.
View ATACATAC Sequencing
Chromatin accessibility profiling with fragment-aware QC and differential peaks.
View SCSingle-Cell RNA Sequencing
Cell-level expression with ambient RNA, doublet and batch correction handled.
View METHDNA Methylation (Bisulfite / EM-seq)
Bisulfite and EM-seq methylation calling with conversion QC and DMR detection.
View LRSLong-Read Sequencing (ONT / PacBio)
ONT and PacBio pipelines for structural variants, assembly, isoforms and phasing.
ViewSix concrete deliverables, not a slide deck.
This is exactly what transfers to you at the end of this Bioinformatics Pipeline engagement.
Containerized pipeline code
Every pipeline delivered as versioned, containerized code your team can run without our involvement.
Reference data & config management
Reference genomes, indexes and configuration files version-locked alongside the pipeline.
Automated QC reports
Every run generates a QC report flagging anomalies before results reach a researcher.
Validation against truth sets
Benchmark results against known-answer or reference samples, documented for reviewers.
Compute cost estimate & scaling guide
A guide to running the pipeline cost-effectively at your expected data volume.
Full documentation & handoff session
Written documentation plus a recorded walkthrough so your team can operate the pipeline independently.
What this service includes
Pipeline design
Nextflow and Snakemake pipelines built around your assay — NGS, RNA-seq, single-cell, or multi-omic workflows.
Containerized & portable
Every pipeline runs identically on a laptop, an HPC cluster, or the cloud, with dependencies locked and versioned.
QC & variant calling automation
Automated quality-control gates catch failed runs before they reach a researcher’s desk.
Full provenance tracking
Every output is traceable to the exact code, container and reference-data version that produced it.
What this looks like in a real engagement
Three concrete examples of the kind of work this service covers.
Turning raw FASTQ files into a variant report
An automated, versioned workflow from raw sequencer output to an analysis-ready VCF and QC report, instead of a chain of manual steps.
Scaling a working pipeline from 10 samples to 10,000
Re-architecting a script that works on a laptop into a workflow that runs reliably on a cluster or the cloud without falling over.
Making a published pipeline reproducible for reviewers
Containerizing and versioning an existing pipeline so a reviewer — or a collaborator two years from now — gets the exact same output.
Reproducibility isn’t a nice-to-have — it’s the whole point.
A pipeline that gives a different answer depending on who runs it, or when, isn’t a scientific tool — it’s a liability. We treat reproducibility as a hard requirement, not an aspiration.
That means locked container versions, versioned reference data, and automated tests that catch silent failures before they reach downstream analysis.
How an engagement runs
Four stages, from initial scope to delivery.
Map the assay
We document your wet-lab protocol and existing analysis steps before writing pipeline code.
Containerize each stage
Every tool and dependency is pinned and containerized for exact reproducibility.
Benchmark against truth sets
Pipelines are validated against reference datasets and known-answer samples.
Deploy at your compute scale
Pipelines are tuned to run cost-effectively on HPC or cloud compute, at your data volume.
What we build it with
We stopped asking whether a result was reproducible. With Nuclyra’s pipelines, it just is.
Before you reach out
Yes. We often inherit a partially-built pipeline and bring it up to reproducibility and QC standards rather than starting from scratch.
Yes. Pipelines are built to run on Slurm-based HPC clusters, AWS/GCP/Azure, or a hybrid of both, without a rewrite.
We work within your existing data use agreements and licensed reference sets; we don’t introduce new third-party data without your sign-off.
Still wrangling pipelines by hand?
Tell us about your assay and current workflow — we’ll show you what a reproducible version looks like.