binchicken

name	binchicken
description	Bin Chicken — targeted coassembly planning for recovering low-abundance / novel MAGs by grouping metagenomes that share marker-gene signal (SingleM). Use when: (1) Public or large cohorts where all-vs-all coassembly is impossible, (2) Prioritizing sample subsets for coassembly to recover rare lineages, (3) Novelty-focused recovery against reference genomes, (4) Study-design of coassembly strata. Upstream: https://github.com/AroneyS/binchicken. For general coassembly vs individual design use study-design. For binning after coassembly use basalt / metabat2 / etc.
license	MIT
category	analysis-tools
tags	["coassembly","low-abundance","BinChicken","SingleM","MAG"]
upstream	https://github.com/AroneyS/binchicken
stage	assembly

Bin Chicken

Upstream: AroneyS/binchicken

Citation

Aroney, S. T. N., Newell, R. J. P., Tyson, G. W. & Woodcroft, B. J. Bin Chicken: targeted metagenomic coassembly for the efficient recovery of novel genomes. Nat. Methods (2025). https://doi.org/10.1038/s41592-025-02901-1

See also docs/references.md.

Analytical thinking

Low-abundance genomes are often below recoverable coverage in any single sample. Blind coassembly of many samples increases graph complexity and compute; random subsets explode combinatorially.

Bin Chicken plans targeted coassemblies: match SingleM marker sequences across metagenomes, optionally prioritize markers divergent from references, then greedily cluster samples into coassembly groups. It is a sample-selection / assembly-planning tool — not a replacement for MetaBAT2/BASALT after contigs exist.

Use when:

• Mining public repositories or large private cohorts for rare/novel lineages
• You need a principled alternative to metadata-only pooling
• You want novelty-weighted recovery (markers distant from known genomes)

Do not use Bin Chicken to justify pooling unrelated biological strata for a single hypothesis-driven contrast study — see study-design.

Prerequisites

Requirement	Notes
Inputs	Many metagenomes (reads) + SingleM/Bin Chicken DB stack
Compute	Coassembly of selected groups still expensive — plan RAM
Downstream	Assemble groups → map → bin (basalt ★ / single binners) → QC

How to run

Confirm subcommands against upstream (workflow is multi-step):

# high-level pattern — see upstream docs for exact CLI
binchicken \
  # 1) singlem / marker search across samples
  # 2) cluster samples into coassembly targets
  # 3) emit assembly recipes / sample lists

Then run your assembler (megahit / metaspades) on each planned group and continue with mapping + binning.

Decision tree

Low-abundance / novelty MAG recovery across many samples?
├─ Need automated coassembly targeting → binchicken
├─ Few related samples, clear strata → study-design + matched co-assembly
├─ After contigs exist → basalt ★ / metawrap / dastool / single binners
└─ Only abundance of known refs → coverm / sylph / metaphlan (not coassembly)

Methods caveats

• Targeted coassembly can still create chimeras — multi-coverage binning and GUNC/CheckM2 remain mandatory.
• Report marker DB, novelty filter, group size, and assembler.
• Genome counts from large public runs are not transferable to your biome.

Related skills

singlem · study-design · megahit · metaspades · basalt · coverm · gtdbtk · tool-selection · metagenomics-workflow