Gradient Biotech

Genomic relatedness

Compute pairwise genetic distance between sequenced isolates from an alignment and group closely related isolates into clusters — a minimal, license-safe relatedness signal, not full phylogenetics.

Research question

Which isolates in this collection are genomically close enough to suggest a shared source or lineage, and what clusters does that relatedness form at a given distance threshold?

Who this is for

  • Microbiome and pathogen genomics researchers screening isolate collections for relatedness
  • Infectious Disease investigators who need a genomic-relatedness signal to support (not prove) outbreak-cluster and transmission-network hypotheses
  • Teams who need a dependency-light relatedness method rather than a full alignment + maximum-likelihood phylogenetics pipeline

Data requirements

DataRequiredPurpose
Isolate sequence alignment (FASTA-derived table, one row per isolate)YesPairwise SNP distance computation
SNP distance thresholdNo (has a default)Defines which pairs are grouped into the same cluster

Workflow

Provide an isolate alignment → Run Relatedness
  → Review pairwise SNP distances and clusters
  → (Infectious Disease) link the run to exposure network / cluster investigation

Step 1 — Run Relatedness

The Relatedness analysis computes a gap/N-aware pairwise SNP distance between every pair of isolates in the alignment, then groups isolates into clusters using single-linkage clustering at the configured SNP distance threshold.

Step 2 — Review distances and clusters

The result view reports the pairwise distance matrix and the resulting clusters. This is a coarse, transparent relatedness signal — full multiple-sequence alignment (MAFFT) and maximum-likelihood phylogenetics (IQ-TREE) are later work, not what this pipeline does today.

Step 3 — Cross-area use (optional)

Infectious Disease can link a completed Relatedness run into its exposure-network and cluster-investigation analyses, mapping episodes to isolates by sample ID and annotating candidate epidemiological links with true pairwise genomic distance instead of a coarse shared-lineage proxy.

Expected outputs

  • Pairwise SNP distance matrix across isolates
  • Single-linkage clusters at the configured distance threshold
  • A run artifact that Infectious Disease can reference by run ID for cross-area genomic support

Important caveats

Genomic relatedness is a supporting signal for outbreak and transmission hypotheses, never proof of transmission by itself. It does not replace full phylogenetic inference or formal outbreak genomics review.

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