concoct

name	concoct
description	Composition and coverage binning of metagenomic contigs. Use this skill when working with concoct: installing, choosing parameters, running commands, interpreting outputs, or troubleshooting. Upstream: https://github.com/BinPro/CONCOCT. For stage routing use tool-selection / metagenomics-workflow.
license	MIT
category	analysis-tools
tags	["binning","concoct"]
upstream	https://github.com/BinPro/CONCOCT
stage	binning

CONCOCT

Upstream: CONCOCT

Citation

Alneberg, J. et al. Binning metagenomic contigs by coverage and composition. Nat. Methods 11, 1144–1146 (2014). https://doi.org/10.1038/nmeth.3103

See also docs/references.md.

Analytical thinking

CONCOCT (Alneberg et al., 2014) pioneered joint composition + multi-sample coverage clustering (Gaussian mixture models). It remains a valuable ensemble member because its inductive bias differs from MetaBAT2/MaxBin2/DL binners.

Operational rules:

• Cut long contigs into chunks (commonly ~10 kb) before clustering, then merge cutup clusters back to contigs — required by the CONCOCT workflow.
• Multi-coverage inputs matter (Mattock & Watson 2023); single-sample coverage weakens the method’s original strength.
• Memory-heavy on complex communities; often optional inside MetaWRAP.

How to run

conda install -c bioconda concoct  # if packaged; else follow upstream

cut_up_fasta.py contigs.fa -c 10000 -o 0 -m > contigs_10k.fa
# then coverage table + concoct + merge_cutup_clustering.py (see upstream)

Key parameters

| contig cut length | 10 kb common | Required prep | | coverage | BAM-derived | |

Decision notes

• Stage: binning — strong ensemble member (composition + multi-coverage)
• Cut contigs (~10 kb) before clustering; prefer multi-coverage inputs
• Feed into basalt ★ / metawrap / dastool for catalogues

Related skills

metabat2 · maxbin2 · dastool · metawrap · tool-selection · metagenomics-workflow