DAS Tool

Introduction

DAS Tool is an automated method that integrates the results of a flexible number of binning algorithms to calculate an optimized, non-redundant set of bins from a single assembly (Sieber et al. 2018).

Note: DAS Tool has not been updated since 2023 and, in our experience, tends to be more conservative in its bin selection. Binette is an actively maintained alternative that often recovers more MAGs with little loss in bin quality, so it is worth trying alongside or instead of DAS Tool.

Installation

Installed on Crunchomics: Yes, the following versions of DAS Tool are installed as part of the bioinformatics share:

• DAS Tool v1.1.7

If you have access to Crunchomics and have not yet access to the bioinformatics you can send an email with your Uva netID to Nina Dombrowski. Afterwards, you can add the bioinformatics share as follows (if you have already done this in the past, you don’t need to run this command):

conda config --add envs_dirs /zfs/omics/projects/bioinformatics/software/miniconda3/envs/

If you want to install it yourself, you can run the command below — it doesn’t pin a specific version, so it will install whatever the latest release is at the time you run it.

mamba create -n das_tool_1.1.7 -c bioconda das_tool

Usage

Input file format:

• Bins [–bins, -i]: Tab separated files of contig-IDs and bin-IDs. Contigs to bin file example:

Contig_1    bin.01
Contig_8    bin.01
Contig_42   bin.02
Contig_49   bin.03

• Contigs [–contigs, -c]: Assembled contigs in fasta format:

>Contig_1
ATCATCGTCCGCATCGACGAATTCGGCGAACGAGTACCCCTGACCATCTCCGATTA...
>Contig_2
GATCGTCACGCAGGCTATCGGAGCCTCGACCCGCAAGCTCTGCGCCTTGGAGCAGG...

• Proteins (optional) [–proteins]: Predicted proteins in prodigal fasta format. Header contains contig-ID and gene number:

>Contig_1_1
MPRKNKKLPRHLLVIRTSAMGDVAMLPHALRALKEAYPEVKVTVATKSLFHPFFEG...
>Contig_1_2
MANKIPRVPVREQDPKVRATNFEEVCYGYNVEEATLEASRCLNCKNPRCVAACPVN...

Not all binning tools provide results in a tab separated file of contig-IDs and bin-IDs. A helper script can be used to convert a set of bins in fasta format to tabular contigs2bin file, which can be used as input for DAS Tool: src/Fasta_to_Contigs2Bin.sh -h.

conda activate das_tool_1.1.7

src/Fasta_to_Contigs2Bin.sh -i binner/output_folder_with_fasta_files -e fasta > binner.contigs2bin.tsv

Example for running the consolidation step:

DAS_Tool  -i sample_data/sample.human.gut_concoct_contigs2bin.tsv,\
    sample_data/sample.human.gut_maxbin2_contigs2bin.tsv,\
    sample_data/sample.human.gut_metabat_contigs2bin.tsv,\
    sample_data/sample.human.gut_tetraESOM_contigs2bin.tsv \
    -l concoct,maxbin,metabat,tetraESOM \
    -c sample_data/sample.human.gut_contigs.fa \
    -o sample_output/DASToolRun1 \
    --write_bins

After running this, you fill find all relevant information in the quality_report.tsv file in the output folder.

Options:

DAS_Tool [options] -i <contig2bin> -c <contigs_fasta> -o <outputbasename>

Options:
   -i --bins=<contig2bin>                   Comma separated list of tab separated contigs to bin tables.
   -c --contigs=<contigs>                   Contigs in fasta format.
   -o --outputbasename=<outputbasename>     Basename of output files.
   -l --labels=<labels>                     Comma separated list of binning prediction names.
   --search_engine=<search_engine>          Engine used for single copy gene identification (diamond/blastp/usearch) [default: diamond].
   -p --proteins=<proteins>                 Predicted proteins (optional) in prodigal fasta format (>contigID_geneNo).
                                            Gene prediction step will be skipped.
   --write_bin_evals                        Write evaluation of input bin sets.
   --write_bins                             Export bins as fasta files.
   --write_unbinned                         Write unbinned contigs.
   -t --threads=<threads>                   Number of threads to use [default: 1].
   --score_threshold=<score_threshold>      Score threshold until selection algorithm will keep selecting bins (0..1) [default: 0.5].
   --duplicate_penalty=<duplicate_penalty>  Penalty for duplicate single copy genes per bin (weight b).
                                            Only change if you know what you are doing (0..3) [default: 0.6].
   --megabin_penalty=<megabin_penalty>      Penalty for megabins (weight c). Only change if you know what you are doing (0..3) [default: 0.5].
   --dbDirectory=<dbDirectory>              Directory of single copy gene database [default: db].
   --resume                                 Use existing predicted single copy gene files from a previous run.
   --debug                                  Write debug information to log file.
   -v --version                             Print version number and exit.
   -h --help                                Show this.

References

Sieber, Christian M. K., Alexander J. Probst, Allison Sharrar, Brian C. Thomas, Matthias Hess, Susannah G. Tringe, and Jillian F. Banfield. 2018. “Recovery of Genomes from Metagenomes via a Dereplication, Aggregation and Scoring Strategy.” Nature Microbiology 3 (7): 836–43. https://doi.org/10.1038/s41564-018-0171-1.