The workflow is based on IMG MAGs pipeline¹ for metagenome assembled genomes generation. It takes assembled contigs, reads mapping result bam file and contigs annotations result to to associate groups of contigs as deriving from a seemingly coherent microbial species (binning) and evaluted by checkM and gtdb-tk.

• CheckM² database is 275MB contains the databases used for the Metagenome Binned contig quality assessment. (requires 40GB+ of memory, included in the image)

  • https://data.ace.uq.edu.au/public/CheckM_databases/checkm_data_2015_01_16.tar.gz

• GTDB-Tk³ requires ~33G of external data that need to be downloaded and unarchived. (requires ~150GB of memory)

  • https://data.gtdb.ecogenomic.org/releases/release95/95.0/auxillary_files/gtdbtk_r95_data.tar.gz

• Prepare the GTDB-Tk Database

    
    wget https://data.gtdb.ecogenomic.org/releases/release95/95.0/auxillary_files/gtdbtk_r95_data.tar.gz
    tar -xvzf gtdbtk_r95_data.tar.gz
    mv release95 refdata/GTDBTK_DB
    
    rm gtdbtk_r95_data.tar.gz

Description of the files:

• .wdl file: the WDL file for workflow definition
• .json file: the example input for the workflow
• .conf file: the conf file for running Cromwell.
• .sh file: the shell script for running the example workflow (sbatch)

microbiomedata/nmdc_mbin

A json files with following entries:

1. Number of CPUs,
2. The number of threads used by pplacer (Use lower number to reduce the memory usage)
3. Output directory
4. Project name
5. Metagenome Assembled Contig fasta file
6. Sam/Bam file from reads mapping back to contigs.
7. Contigs functional annotation result in gff format
8. Optioal: Tab-delimited text file which containing mapping of headers between SAM and FNA (ID in SAM/FNAID in GFF). A two column tab-delimited file. When the annotation and assembly are performed using different identifiers for contigs. The map file is to link the gff file content and mapping result bam file content to the assembled contigs ID.
9. The database directory path which includes checkM_DB and GTDBTK_DB subdirectories.
10. (optional) scratch_dir: use --scratch_dir for gtdbtk disk swap to reduce memory usage but longer runtime

{
  "nmdc_mags.cpu":32,
  "nmdc_mags.pplacer_cpu":1,
  "nmdc_mags.outdir":"/global/cfs/cdirs/m3408/aim2/metagenome/MAGs/output",
  "nmdc_mags.proj_name":"3300037552",
  "nmdc_mags.contig_file":"/global/cfs/cdirs/m3408/aim2/metagenome/MAGs/mbin-nmdc-test-dataset/3300037552.a.fna",
  "nmdc_mags.sam_file":"/global/cfs/cdirs/m3408/aim2/metagenome/MAGs/mbin-nmdc-test-dataset/3300037552.bam.sorted.bam",
  "nmdc_mags.gff_file":"/global/cfs/cdirs/m3408/aim2/metagenome/MAGs/mbin-nmdc-test-dataset/3300037552.a.gff",
  "nmdc_mags.map_file":"/global/cfs/cdirs/m3408/aim2/metagenome/MAGs/mbin-nmdc-test-dataset/3300037552.a.map.txt",
  "nmdc_mags.gtdbtk_database":"/path/to/GTDBTK_DB"
}

The output will have a bunch of output directories, files, including statistical numbers, status log and a shell script to reproduce the steps etc.

The final MiMAG output is in hqmq-metabat-bins directory and its corresponding lineage result in gtdbtk_output directory.

|-- MAGs_stats.json
|-- 3300037552.bam.sorted
|-- 3300037552.depth
|-- 3300037552.depth.mapped
|-- bins.lowDepth.fa
|-- bins.tooShort.fa
|-- bins.unbinned.fa
|-- checkm-out
|   |-- bins/
|   |-- checkm.log
|   |-- lineage.ms
|   `-- storage
|-- checkm_qa.out
|-- gtdbtk_output
|   |-- align/
|   |-- classify/
|   |-- identify/
|   |-- gtdbtk.ar122.classify.tree -> classify/gtdbtk.ar122.classify.tree
|   |-- gtdbtk.ar122.markers_summary.tsv -> identify/gtdbtk.ar122.markers_summary.tsv
|   |-- gtdbtk.ar122.summary.tsv -> classify/gtdbtk.ar122.summary.tsv
|   |-- gtdbtk.bac120.classify.tree -> classify/gtdbtk.bac120.classify.tree
|   |-- gtdbtk.bac120.markers_summary.tsv -> identify/gtdbtk.bac120.markers_summary.tsv
|   |-- gtdbtk.bac120.summary.tsv -> classify/gtdbtk.bac120.summary.tsv
|   `-- ..etc 
|-- hqmq-metabat-bins
|   |-- bins.11.fa
|   |-- bins.13.fa
|   `-- ... etc 
|-- mbin-2020-05-24.sqlite
|-- mbin-nmdc.20200524.log
|-- metabat-bins
|   |-- bins.1.fa
|   |-- bins.10.fa
|   `-- ... etc 

1. Chen IA, Chu K, Palaniappan K, et al. IMG/M v.5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes. Nucleic Acids Res. 2019;47(D1):D666‐D677. doi:10.1093/nar/gky901
2. Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25(7):1043‐1055. doi:10.1101/gr.186072.114
3. Pierre-Alain Chaumeil, Aaron J Mussig, Philip Hugenholtz, Donovan H Parks, GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database, Bioinformatics, Volume 36, Issue 6, 15 March 2020, Pages 1925–1927, https://doi.org/10.1093/bioinformatics/btz848