study-design

name	study-design
description	Study design for shotgun metagenomics across QC, assembly, binning, and annotation. Use this skill when: (1) Deciding co-assembly vs sample-specific assembly, (2) Defining biological strata (time, treatment, habitat, host), (3) Building a sample/assembly/coverage manifest, (4) Planning multi-assembly binning (e.g. BASALT), (5) Avoiding invalid pooling of unrelated samples. For tool routing, use tool-selection. For stage order, use metagenomics-workflow.
license	MIT
category	orchestration
tags	["study-design","co-assembly","pooling","provenance"]

Study Design

Design principle

Analyse assemblies jointly only when coverage remains biologically interpretable. Unrelated samples create ambiguous mappings and invalid selection.

Informative multi-sample designs need:

• enough shared genomes for complementary observation
• enough abundance contrast to discriminate contig profiles

Decision table

Question	If yes	If no
One coherent biological stratum?	May share one recovery run	Split by stratum
Coverage traceable to reads + assembly?	Record mapping	Repair provenance first
Pooled assembly justified?	Pilot with individuals + matched pool	Prefer sample-specific
Pooling erases primary contrast?	Keep strata separate	Pooling may be tested

When multi-assembly helps

Reactor replicates, longitudinal hosts, nearby sites, matched extraction methods, before/after intervention, related growth stages, continuous environmental gradients.

Manifest (required fields)

Field	Purpose
sample_id	Stable ID matching filenames
stratum / Group	Biological partition
host_reference	Host genome for depletion
assembly	Contig FASTA
assembly_type	individual / pool / subgroup
assembly_reads / coverage_reads	Exact provenance
run_directory	Dedicated working directory

Patterns

# Related replicates (feeds stage 3 binning)
sample-specific assemblies ─┐
matched co-assembly ────────┴─> one ensemble binning run (basalt★ / metawrap)

# Longitudinal / treatment
stage1 → run1
stage2 → run2
then compare bins/genomes with declared ANI threshold

Subgroup when samples > ~30 or data > ~300 Gb.

Multi-coverage vs single-coverage binning

Mattock & Watson (Nat. Methods 2023) compared single- vs multi-coverage binning on the same per-sample assemblies: multi-coverage recovered more filtered bins and exposed contaminant/chimeric structure that single-coverage misses (co-abundant contigs within one sample look “genome-like” without cross-sample contrast).

Practical rule: when related samples exist, map all relevant samples to each assembly for binning coverage — even if you keep sample-specific assemblies. Single-coverage is a resource compromise, not a scientific default.

Low-abundance / novelty coassembly

Blind pooling is costly and can worsen assemblies. For large cohorts aimed at rare/novel genomes, consider binchicken (targeted coassembly via shared SingleM markers, optional novelty prioritization). Still enforce stratum rules above — targeting is not a license to mix unrelated biology into one contrast.

Pilot evaluation

Compare designs on retained bins under fixed completeness/contamination, redundancy (ANI), taxonomy, contiguity, and compute cost — not raw bin count.

Related skills

• tool-selection — stage routing; ensemble binning choice
• metagenomics-workflow — QC → assembly → binning → taxonomy → function → mining → research-analysis
• basalt · metawrap · dastool — binning stage
• binchicken — targeted coassembly planning for low-abundance recovery
• lorbin · comebin · semibin2 · vamb — single binners