Use cases

Call and annotate variants without a cluster account.

Whole-genome or exome reads aligned, variants called against known sites, and each one annotated with its likely effect.

Join the waitlist
variants.flow5 steps
known sitesINPUTFASTQALIGNBWA-MEMVARIANTSGATKANNOTATEVEPREFknown.vcf

What is Genomics analysis?

Variant calling finds the positions where a sample's DNA differs from a reference genome. A standard short-read pipeline aligns reads with BWA-MEM, calls single-nucleotide variants and small insertions and deletions with GATK, and annotates each variant with its likely effect on genes and proteins using the Ensembl Variant Effect Predictor (VEP).

Whole-genome and exome sequencing read DNA directly, so the question is not how much of something there is but what is different. The difference might be an edit you made, a mutation behind a phenotype, the change that separates a resistant line from its parent, or variation across patient samples.

Alignment places each read on the reference genome; BWA-MEM is the standard aligner for short reads. Variant calling then examines every position where reads disagree with the reference and decides whether the difference is real or sequencing error. GATK's best-practice workflow first recalibrates base quality scores against known variant sites, because the quality scores a sequencer reports are systematically biased, and errors there become false calls.

Annotation is what makes a list of positions readable. VEP reports which gene and transcript each variant falls in and its predicted consequence, from a silent change to one that truncates the protein, so thousands of calls can be filtered down to the handful worth confirming at the bench.

What happens to your data

For groups sequencing patient samples, model organisms or engineered lines.

Questions it answers

  • Did the edit land, and is anything else different?
  • Which variants in this sample are likely to matter?
  • What separates the resistant line from the parent?
  1. 1

    BWA-MEM

    Aligns reads to the reference genome.

    Look at: Coverage across the regions you care about.

  2. 2

    GATK

    Calls variants, using a set of known sites to recalibrate base quality first.

    Look at: The number of calls per sample. An outlier is worth a look.

  3. 3

    VEP

    Annotates each variant with the gene it falls in and its likely consequence.

    Look at: The consequence summary: how many are coding, and how many are predicted to disrupt the protein.

What you take to lab meeting

  • A VCF of variants per sample

  • An annotated table you can filter by gene or consequence

  • Coverage and call-rate summaries

Common questions

What is the difference between whole-genome and exome sequencing?
Exome sequencing captures only the protein-coding regions, a small fraction of the genome, so it costs less per sample and reaches higher depth there. Whole-genome sequencing covers everything, including regulatory and intronic regions and larger structural changes, at a higher cost per sample.
How much sequencing coverage do I need for variant calling?
It depends on the question and the organism. What matters in practice is coverage across the regions you care about: low or patchy coverage there means missed or unreliable calls, so read the coverage summary before trusting the call set.
Why does GATK recalibrate base quality scores?
Sequencers report a confidence score for every base, and those scores are systematically biased. Base quality score recalibration models the bias using known variant sites and corrects it, which reduces false positive variant calls (McKenna et al., 2010; Van der Auwera et al., 2013).
Can variant calling confirm a CRISPR edit?
Yes. Calling variants around the target site shows whether the intended change is present and in what share of reads, and a genome-wide call set can be compared with the parental line for unintended differences. Edits that cause larger rearrangements may need tools beyond small-variant calling.
Do I need a reference genome?
Yes. Variant calling compares reads with a reference, so you need the reference genome for your organism, and a set of known variant sites for quality recalibration where one exists.

Sources

The papers behind the tools in this pipeline, and the advice on this page. Checked 25 October 2026.

  1. Li H (2013). Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv preprint 1303.3997.
  2. McKenna A, et al. (2010). The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Research 20, 1297–1303.
  3. Van der Auwera GA, et al. (2013). From FastQ data to high-confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Current Protocols in Bioinformatics 43.
  4. McLaren W, et al. (2016). The Ensembl Variant Effect Predictor. Genome Biology 17, 122.

Read before you run it

Bioinformaticians say “pipeline” the way bench scientists say “protocol”. Here is the translation, and why the analogy holds better than you might expect.

What a pipeline actually is, for people who have never written one

Explainers, 7 October 2026

Other starter pipelines

Run Genomics on your own data.

OpenBionode is in early access. Tell us what you analyse and we’ll make sure the steps you need are ready when your seat opens.

Early access for research labs. No spam, just one note when your seat opens.