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.
Christian M. K. Sieber, Alexander J. Probst, Allison Sharrar, Brian C. Thomas, Matthias Hess, Susannah G. Tringe & Jillian F. Banfield (2018). Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nature Microbiology. https://doi.org/10.1038/s41564-018-0171-1.
DAS_Tool -i methodA.scaffolds2bin,...,methodN.scaffolds2bin
-l methodA,...,methodN -c contigs.fa -o myOutput
-i, --bins Comma separated list of tab separated scaffolds to bin tables.
-c, --contigs Contigs in fasta format.
-o, --outputbasename Basename of output files.
-l, --labels Comma separated list of binning prediction names. (optional)
--search_engine Engine used for single copy gene identification [blast/diamond/usearch].
(default: usearch)
--write_bin_evals Write evaluation for each input bin set [0/1]. (default: 1)
--create_plots Create binning performance plots [0/1]. (default: 1)
--write_bins Export bins as fasta files [0/1]. (default: 0)
--proteins Predicted proteins in prodigal fasta format (>scaffoldID_geneNo).
Gene prediction step will be skipped if given. (optional)
--score_threshold Score threshold until selection algorithm will keep selecting bins [0..1].
(default: 0.5)
--duplicate_penalty Penalty for duplicate single copy genes per bin (weight b).
Only change if you know what you're doing. [0..3]
(default: 0.6)
--megabin_penalty Penalty for megabins (weight c). Only change if you know what you're doing. [0..3]
(default: 0.5)
--db_directory Directory of single copy gene database. (default: install_dir/db)
--resume Use existing predicted single copy gene files from a previous run [0/1]. (default: 0)
--debug Write debug information to log file.
-t, --threads Number of threads to use. (default: 1)
-v, --version Print version number and exit.
-h, --help Show this message.
- Bins [--bins, -i]: Tab separated files of scaffold-IDs and bin-IDs. Scaffolds to bin file example:
Scaffold_1 bin.01
Scaffold_8 bin.01
Scaffold_42 bin.02
Scaffold_49 bin.03
- Contigs [--contigs, -c]: Assembled contigs in fasta format:
>Scaffold_1
ATCATCGTCCGCATCGACGAATTCGGCGAACGAGTACCCCTGACCATCTCCGATTA...
>Scaffold_2
GATCGTCACGCAGGCTATCGGAGCCTCGACCCGCAAGCTCTGCGCCTTGGAGCAGG...
- Proteins (optional) [--proteins]: Predicted proteins in prodigal fasta format. Header contains scaffold-ID and gene number:
>Scaffold_1_1
MPRKNKKLPRHLLVIRTSAMGDVAMLPHALRALKEAYPEVKVTVATKSLFHPFFEG...
>Scaffold_1_2
MANKIPRVPVREQDPKVRATNFEEVCYGYNVEEATLEASRCLNCKNPRCVAACPVN...
- Summary of output bins including quality and completeness estimates (DASTool_summary.txt).
- Scaffolds to bin file of output bins (DASTool_scaffolds2bin.txt).
- Quality and completeness estimates of input bin sets, if
--write_bin_evals 1is set ([method].eval). - Plots showing the amount of high quality bins and score distribution of bins per method, if
--create_plots 1is set (DASTool_hqBins.pdf, DASTool_scores.pdf). - Bins in fasta format if
--write_bins 1is set (DASTool_bins).
Example 1: Run DAS Tool on binning predictions of MetaBAT, MaxBin, CONCOCT and tetraESOMs. Output files will start with the prefix DASToolRun1:
$ ./DAS_Tool -i sample_data/sample.human.gut_concoct_scaffolds2bin.tsv,
sample_data/sample.human.gut_maxbin2_scaffolds2bin.tsv,
sample_data/sample.human.gut_metabat_scaffolds2bin.tsv,
sample_data/sample.human.gut_tetraESOM_scaffolds2bin.tsv
-l concoct,maxbin,metabat,tetraESOM
-c sample_data/sample.human.gut_contigs.fa
-o sample_output/DASToolRun1
Example 2: Run DAS Tool again with different parameters. Use the proteins predicted in Example 1 to skip the gene prediction step, disable writing of bin evaluations, set the number of threads to 2 and score threshold to 0.6. Output files will start with the prefix DASToolRun2:
$ ./DAS_Tool -i sample_data/sample.human.gut_concoct_scaffolds2bin.tsv,
sample_data/sample.human.gut_maxbin2_scaffolds2bin.tsv,
sample_data/sample.human.gut_metabat_scaffolds2bin.tsv,
sample_data/sample.human.gut_tetraESOM_scaffolds2bin.tsv
-l concoct,maxbin,metabat,tetraESOM
-c sample_data/sample.human.gut_contigs.fa
-o sample_output/DASToolRun2
--proteins sample_output/DASToolRun1_proteins.faa
--write_bin_evals 0
--threads 2
--score_threshold 0.6
- R (>= 3.2.3): https://www.r-project.org
- R-packages: data.table (>= 1.9.6), doMC (>= 1.3.4), ggplot2 (>= 2.1.0)
- ruby (>= v2.3.1): https://www.ruby-lang.org
- Pullseq (>= 1.0.2): https://github.com/bcthomas/pullseq
- Prodigal (>= 2.6.3): https://github.com/hyattpd/Prodigal
- coreutils (only macOS/ OS X): https://www.gnu.org/software/coreutils
- One of the following search engines:
- USEARCH* (>= 8.1): http://www.drive5.com/usearch/download.html
- DIAMOND (>= 0.9.14): https://ab.inf.uni-tuebingen.de/software/diamond
- BLAST+ (>= 2.5.0): https://blast.ncbi.nlm.nih.gov/Blast.cgi
*) The free version of USEARCH only can use up to 4Gb RAM. Therefore, the use of DIAMOND or BLAST+ is recommended for big datasets.
# Download and extract DASTool.zip archive:
unzip DAS_Tool-1.x.x.zip
cd ./DAS_Tool-1.x.x
# Install R-packages:
R CMD INSTALL ./package/DASTool_1.x.x.tar.gz
# Unzip SCG database:
unzip ./db.zip -d db
# Run DAS Tool:
./DAS_Tool -h
Installation of dependent R-packages:
$ R
> repo='http://cran.us.r-project.org' #select a repository
> install.packages('doMC', repos=repo, dependencies = T)
> install.packages('data.table', repos=repo, dependencies = T)
> install.packages('ggplot2', repos=repo, dependencies = T)
> q() #quit R-session
After installing all dependent R-packages, the DAS Tool R-functions can be installed in a bash terminal:
$ R CMD INSTALL ./package/DASTool_1.x.x.tar.gz
...or in an R-session:
$ R
> install.packages('package/DASTool_1.x.x.tar.gz')
> q() #quit R-session
DAS Tool now can also be installed via bioconda and homebrew.
Bioconda repository: https://bioconda.github.io/recipes/das_tool/README.html. Thanks @keuv-grvl and @silask!.
Add bioconda channel:
conda config --add channels defaults
conda config --add channels bioconda
conda config --add channels conda-forge
Install DAS Tool using conda:
conda install -c bioconda das_tool
Homebrew-bio repository: https://github.com/brewsci/homebrew-bio. Thanks @gaberoo!
Install DAS Tool using Homebrew:
brew install brewsci/bio/das_tool
Not all binning tools provide results in a tab separated file of scaffold-IDs and bin-IDs. A helper script can be used to convert a set of bins in fasta format to tabular scaffolds2bin file, which can be used as input for DAS Tool: src/Fasta_to_Scaffolds2Bin.sh -h.
Fasta_to_Scaffolds2Bin: Converts genome bins in fasta format to scaffolds-to-bin table.
Usage: Fasta_to_Scaffolds2Bin.sh -e fasta > my_scaffolds2bin.tsv
-e, --extension Extension of fasta files. (default: fasta)
-i, --input_folder Folder with bins in fasta format. (default: ./)
-h, --help Show this message.
$ ls /maxbin/output/folder
maxbin.001.fasta maxbin.002.fasta maxbin.003.fasta...
$ src/Fasta_to_Scaffolds2Bin.sh -i /maxbin/output/folder -e fasta > maxbin.scaffolds2bin.tsv
$ head gut_maxbin2_scaffolds2bin.tsv
NODE_10_length_127450_cov_375.783524 maxbin.001
NODE_27_length_95143_cov_427.155298 maxbin.001
NODE_51_length_78315_cov_504.322425 maxbin.001
NODE_84_length_66931_cov_376.684775 maxbin.001
NODE_87_length_65653_cov_460.202156 maxbin.001
Some binning tools (such as CONCOCT) provide a comma separated tabular output. To convert a comma separated file into a tab separated file a one liner can be used: perl -pe "s/,/\t/g;" scaffolds2bin.csv > scaffolds2bin.tsv.
$ head concoct_clustering_gt1000.csv
NODE_2_length_147519_cov_33.166976,42
NODE_3_length_141012_cov_38.678171,42
NODE_4_length_139685_cov_35.741896,42
$ perl -pe "s/,/\tconcoct./g;" concoct_clustering_gt1000.csv > concoct.scaffolds2bin.tsv
$ head concoct.scaffolds2bin.tsv
NODE_2_length_147519_cov_33.166976 concoct.42
NODE_3_length_141012_cov_38.678171 concoct.42
NODE_4_length_139685_cov_35.741896 concoct.42
Problem: All dependencies are installed and the environmental variables are set but DAS Tool still claims that specific depencendies are missing.
Solution: Make sure that the dependency executable names are correct. For example USEARCH has to be executable with the command
If your USEARCH binary is called differently (e.g. usearch9.0.2132_i86linux32) you can either rename it or add a symbolic link called usearch:
$ ln -s usearch9.0.2132_i86linux32 usearch
Problem: Running DAS Tool with the free version of USEARCH on a large metagenomic dataset results in the following error:
---Fatal error---
Memory limit of 32-bit process exceeded, 64-bit build required
makeblastdb did not work for my_proteins.faa, please check your input file
Solution: Use DIAMOND or BLAST as search engine:
DAS_Tool --search_engine diamond ...
or
DAS_Tool --search_engine blast ...
or install the 64 bit version of USEARCH.
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