Bioinformatics
Bioinformatics combines computer science, statistics, and biology to make sense of large-scale biological datasets. We design portable and reproducible workflows using engines like Nextflow and Snakemake to process raw sequences. Our pipelines are engineered for maximum rigour, ensuring transparent and scalable analysis.
What this area is.
Bioinformatics is the engineering backbone of the ecosystem. We design portable, reproducible workflows (Nextflow / Snakemake) that run identically on a laptop, an HPC cluster or the cloud, with QC baked in at every step.
Beyond running tools, we benchmark and build methods — choosing the right aligner, quantifier or statistical model for the question, and documenting every parameter so results can be reproduced and trusted.
Tools & technologies
What we do.
Core methods we apply in bioinformatics.
Reproducible pipelines
Nextflow / Snakemake workflows, containerised and version-pinned.
Quality control
FastQC and MultiQC across cohorts, with automated gating.
Alignment & quantification
Short- and long-read alignment, expression quantification, count matrices.
Differential analysis
Robust statistics for differential expression and abundance.
Scalability
Seamless scaling from laptop to HPC/cloud with the same code.
Method benchmarking
Objective comparison of tools against truth sets and metrics.
From data to insight.
How a bioinformatics project flows end to end.
Raw data
FASTQ / BAM in
QC
FastQC · MultiQC
Process
align · quantify
Model
stats / ML
Visualise
figures & reports
Reproduce
containers · provenance
Publication-grade figures.
Interactive, live-rendered visualisations used in bioinformatics.
Where we go deep.
Workflow engineering
Portable, auditable pipelines that others can rerun exactly.
AMR sequencing
Whole-genome bacterial pipelines (Illumina & Nanopore) for resistance profiling.
Method development
New algorithms where existing tools fall short.
Questions we answer.
A few of the things people ask about bioinformatics — and our short answers. Ask CGB-AI for more.
What makes analysis reproducible?
Pinned tool versions, containers, captured parameters and a workflow engine — so the same inputs always yield the same outputs.
Can you scale to large cohorts?
Yes — the same workflow runs across HPC and cloud with parallelisation, so cohort size is a scheduling problem, not a rewrite.
Publications in Bioinformatics.
Drawn from our full record of 170 papers, filtered to this area.
Start a bioinformatics project.
Tell us the biological question and the data you have — we will map out an approach.
