dastool

name	dastool
description	DAS Tool (Dereplication, Aggregation and Scoring) integrates multiple binning predictions into one non-redundant MAG set for a single assembly. Use this skill when working with DAS_Tool: preparing contig2bin tables, choosing score thresholds, comparing ensemble binning options, or writing Methods. Upstream: https://github.com/cmks/DAS_Tool. For suite-style MetaBAT/MaxBin refinement use metawrap. For recommended multi-assembly refined recovery use basalt. For routing, use tool-selection.
license	MIT
category	analysis-tools
tags	["MAG","ensemble-binning","DAS_Tool","refinement"]
upstream	https://github.com/cmks/DAS_Tool

DAS Tool

Upstream: cmks/DAS_Tool

Citation

Sieber, C. M. K. et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat. Microbiol. 3, 836–843 (2018). https://doi.org/10.1038/s41564-018-0171-1

See also docs/references.md.

Analytical thinking

DAS Tool sits in the ensemble binning family with MetaWRAP and BASALT: run several independent binners (MetaBAT2, MaxBin2, CONCOCT, …) on the same assembly, then select an optimized non-redundant set.

DAS Tool scores candidates with single-copy marker genes and aggregates across binning predictions. It does not invent new contig membership from cross-assembly connectivity or deep learning — that is BASALT’s role.

Choose DAS Tool when you already have multiple contig2bin tables and want a lightweight, classical consensus. Prefer basalt as the default ensemble route for related samples / multi-assembly work; use metawrap when you want a packaged binning + CheckM-style refinement suite.

Prerequisites

Requirement	Minimum	Recommended
Input	Assembly FASTA + ≥2 contig2bin TSVs	MetaBAT2 + MaxBin2 (+ CONCOCT)
Search engine	DIAMOND / BLAST+ / USEARCH	DIAMOND (large datasets)
Threads	1	8–16

How to run

conda install -c bioconda das_tool

# Convert FASTA bins → contig2bin if needed
Fasta_to_Contigs2Bin.sh -i metabat2_bins -e fa > metabat2.contigs2bin.tsv
Fasta_to_Contigs2Bin.sh -i maxbin2_bins -e fasta > maxbin2.contigs2bin.tsv

DAS_Tool \
  -i metabat2.contigs2bin.tsv,maxbin2.contigs2bin.tsv \
  -l metabat,maxbin \
  -c contigs.fa \
  -o temp/dastool/run1 \
  -t 8 \
  --write_bins \
  --write_bin_evals \
  --search_engine diamond

Key parameters

Parameter	Typical	Notes
-i	comma-separated contig2bin TSVs	Tab: contig_id<TAB>bin_id
-l	labels matching -i order	Human-readable binner names
-c	assembly FASTA	Same contigs used for binning
--score_threshold	0.5 default	Higher → fewer, stricter bins
--write_bins	on for MAG export	Writes FASTA bins
-p	optional proteins	Skip Prodigal if reused

Output

*_DASTool_summary.tsv
*_DASTool_contigs2bin.tsv
DASTool_bins/          # if --write_bins
*_allBins.eval         # if --write_bin_evals

Decision tree

Ensemble MAG recovery?
├─ Recommended default (related samples / multi-assembly) → basalt
├─ Packaged multi-binner + CheckM refinement suite → metawrap
└─ Classical SCG aggregation of existing contig2bin sets → dastool

Literature notes (Nat. Microbiol. 2018)

Sieber et al. motivate DAS Tool with observations still true operationally:

• No single binner wins on all ecosystems; tools reconstruct different genomes at different completeness — combining predictions recovers more near-complete genomes than any single method.
• Early work showed manual/curated multi-binner merges help; naive automation can reduce contamination and completeness — DAS Tool’s SCG scoring aims for an optimized non-redundant set from one assembly.
• Input is contig2bin tables on one assembly, not cross-assembly DL refinement (contrast basalt).

Practical rule from the paper’s logic: diversify binners (MetaBAT2 + MaxBin2 + CONCOCT/SemiBin2/VAMB/COMEBin), then aggregate — do not run DAS Tool on a single contig2bin table expecting magic.

Related skills

metawrap · basalt · megahit · metaspades · checkm2 · drep · mag-qc · tool-selection