Germline variant calling
SNVs and indels from WGS or WES, with single-sample, trio, and joint-genotyped cohort workflows for inherited and rare-disease studies.
Core Bioinformatics
Genomics & Variant Analysis
From raw reads to an annotated, defensible variant call set. We run whole-genome and whole-exome pipelines for germline and somatic studies—detecting SNVs, indels, copy-number changes, and structural variants—and hand back results documented for publication and review.
FASTQ · BAM/CRAM in
VCF · gVCF · BEDPE out
Typical turnaround 3–7 days
Individual pricing from $149
Illustrative sample output
reads / REF
ALT allele
coverage
Overview
Rigorous variant discovery, without the in-house pipeline overhead
Genomics and variant analysis is the process of turning DNA sequencing reads into a trustworthy list of the ways your sample differs from a reference genome—and then making that list interpretable. The hard part is rarely running a single tool; it is assembling a defensible workflow, choosing callers matched to your data and question, controlling false positives, and documenting every decision so the result survives peer review.
We build and run that workflow for you. Whether you are a PhD researcher with one exome or an industry team with a tumour cohort, we align, process, call, filter, and annotate your data using established, peer-reviewed tools and GATK Best Practices—never opaque in-house black boxes—and return results with the tool versions and parameters recorded for full reproducibility.
Capabilities
What we analyse
One service spanning the full range of DNA variation—from single bases to whole-chromosome rearrangements, germline and somatic.
Somatic & tumour profiling
Matched tumour-normal and tumour-only mutation calling with panel-of-normals and population filtering to isolate true somatic events.
Structural variant detection
Deletions, duplications, inversions, and translocations resolved from split-read, discordant-pair, and assembly-based evidence.
Copy-number analysis
Genome-wide gains and losses called from read-depth and allele-fraction modelling, with segmentation and amplification/deletion classification.
Annotation & interpretation
Functional consequence, population frequency, and pathogenicity evidence layered onto every call so a variant list becomes a shortlist.
Long-read genomics
Structural variants, phasing, repeat expansions, and difficult-region resolution from Oxford Nanopore and PacBio HiFi reads.
De novo assembly
Reference-free genome reconstruction and quality assessment for novel or non-model organisms lacking a good reference.
Cohort & population genomics
Joint callsets, GWAS-ready genotype matrices, and relatedness, ancestry, and sample-QC checks across many samples.
The Pipeline
How a variant analysis runs
A transparent, best-practice sequence—each step chosen for your data type and documented in the final report. Nothing is a black box.
Steps are adapted to your design: germline vs. somatic, short- vs. long-read, single sample vs. cohort. We confirm the plan with you before any compute begins.
Quality control & trimming
Raw-read QC, adapter and quality trimming, and a per-sample quality summary before anything downstream.
Tools: FastQC · MultiQC · fastp · Trimmomatic
Alignment to reference
Reads mapped to GRCh38, T2T-CHM13, or your organism's genome—short-read or long-read aligners as appropriate.
Tools: BWA-MEM2 · Bowtie2 · minimap2
Post-alignment processing
Duplicate marking, base-quality score recalibration, and coverage/insert-size metrics to prepare clean alignments.
Tools: Picard · GATK BQSR · Samtools · mosdepth
Variant calling
Germline or somatic SNV/indel calling, plus dedicated structural-variant and copy-number callers when your study needs them.
Tools: HaplotypeCaller · DeepVariant · Mutect2 · Manta
Filtering & recalibration
VQSR or hard filters, panel-of-normals and tumour-normal subtraction, and frequency filters to control false positives.
Tools: GATK VQSR · bcftools · gnomAD frequency filters
Annotation & interpretation
Consequence prediction and evidence layering against public databases, then prioritisation toward your biological question.
Tools: Ensembl VEP · SnpEff · ANNOVAR · dbNSFP
Reporting & delivery
Annotated tables, a prioritised shortlist, QC report, IGV session, and publication-ready methods with every tool version.
Tools: IGV · MultiQC · versioned methods manifest
Tools & Technologies
Established, peer-reviewed tools—matched to your data
We select from the field's standard toolkit rather than forcing every dataset through one pipeline. A representative set of what we work with:
Read QC & preprocessing
FastQC
MultiQC
fastp
Trimmomatic
Cutadapt
Alignment & processing
BWA-MEM2
Bowtie2
minimap2
Picard
Samtools
GATK BQSR
mosdepth
Qualimap
Germline SNV/indel calling
GATK HaplotypeCaller
DeepVariant
FreeBayes
bcftools call
Somatic calling
Mutect2
Strelka2
VarScan2
MuSE
SomaticSniper
Structural & copy-number
Manta
DELLY
LUMPY
SvABA
GATK-SV
CNVkit
ControlFREEC
Sniffles
Annotation & signatures
Ensembl VEP
SnpEff
ANNOVAR
SnpSift
bcftools csq
SigProfiler
Assembly & long-read
SPAdes
Flye
Hifiasm
QUAST
BUSCO
Benchmarking & review
hap.py
GIAB truth sets
vcfeval
IGV
Reference Resources
Public knowledge behind every annotation
Calls are only as useful as the context around them. We annotate against the community's authoritative, versioned resources.
- dbSNP
- Reference catalogue of known short variants for labelling and cross-referencing calls.
- gnomAD
- Population allele frequencies across genomes and exomes for rarity filtering.
- ClinVar
- Curated clinical-significance assertions to support variant interpretation.
- COSMIC
- Catalogue of somatic mutations in cancer for tumour-focused studies.
- 1000 Genomes & TOPMed
- Population reference panels for frequency context and imputation.
- dbNSFP
- Aggregated in-silico predictors (SIFT, PolyPhen, CADD, REVEL) in one resource.
- Ensembl / RefSeq / GENCODE
- Gene models and transcript annotation underlying consequence calls.
- GWAS Catalog
- Curated trait- and disease-associated variants for downstream context.
- GIAB (NIST) truth sets
- Gold-standard benchmarks used to validate pipeline sensitivity and precision.
Choosing a Design
WGS vs. WES vs. targeted panel
There is no single best design—only the right one for your question and budget. A quick orientation; we will help you decide.
| Dimension | Whole-Genome (WGS) | Whole-Exome (WES) | Targeted Panel |
|---|---|---|---|
| Genomic scope | Entire genome, incl. intronic, intergenic & regulatory regions | Protein-coding exons (~1–2% of the genome) | A defined gene set or region of interest |
| Typical depth | ~30× (germline); higher for somatic | ~100× over targeted exons | 500×–1000×+ for low-frequency variants |
| Variant types resolved | SNVs, indels, CNVs, SVs, repeats, mitochondrial | SNVs and indels in coding regions (CNV with care) | SNVs/indels within the panel; deep, sensitive |
| Relative cost | Highest per sample; most data | Moderate; strong coverage-per-dollar for coding variants | Lowest per sample at scale |
| Best suited to | Discovery, structural/non-coding variation, novel genomes | Mendelian & rare-disease coding-variant studies | Known genes, screening, and deep somatic detection |
What You Receive
A complete, documented deliverable
Not just a VCF dropped in a folder—every output you need to analyse, publish, and reproduce the work.
- Aligned reads (BAM/CRAM), duplicate-marked and recalibrated
- Variant calls in standards-compliant VCF / gVCF
- Structural & copy-number calls (VCF / BEDPE / segments)
- Annotated, filterable variant tables (TSV / XLSX)
- Prioritised candidate shortlist for your question
- QC & coverage report with per-sample metrics
- Publication-ready methods text with tool versions
Built for reproducibility, not just a result
Every pipeline follows documented best practices, and we validate against Genome in a Bottle reference material where it applies, so sensitivity and precision are known rather than assumed. Each run records its tool versions, parameters, and reference builds.
The practical payoff: your methods section writes itself, a reviewer can re-run the analysis, and a result from today can be reproduced a year from now. We will also tell you honestly when a design or sample won't support the conclusion you're after.
FAQ
Genomics & variant analysis questions
What researchers and project leads most often ask before starting a genomics project.
WES targets the protein-coding exome at lower cost and suits Mendelian and coding-variant studies. WGS covers the whole genome — including regulatory, intergenic, and structural regions that exome capture misses. We help you match the design to your biological question and budget.
Single-nucleotide variants (SNVs), small insertions and deletions (indels), copy-number variants (CNVs), and larger structural variants (SVs) such as deletions, duplications, inversions, and translocations — in both germline and somatic (tumour) contexts.
Yes. We run matched tumour-normal and tumour-only somatic calling with callers such as Mutect2 and Strelka2, apply a panel-of-normals and population filters to suppress false positives, and can add copy-number and mutational-signature analysis.
Yes. Long reads span repeats and complex regions, so we use them for structural-variant discovery, phasing, difficult-region resolution, and de novo assembly of novel or non-model genomes, alongside or instead of short-read data.
We start from raw reads (FASTQ) or aligned data (BAM/CRAM) and return standards-compliant VCF and gVCF, BEDPE, and annotated tables. If you only have sequencing files from your core facility and a reference, that is enough to begin.
We follow GATK Best Practices, benchmark against Genome in a Bottle reference sets where relevant, and record every tool and version in the report so your methods section and any reviewer re-run are fully reproducible.
Related
Where genomics work often leads next
Have sequencing data ready to analyse?
Tell us your organism, data type, and question—we'll scope it honestly, including if a different design would serve you better.