/metagenome_assembly

WDL Workflow for metagenome assembly

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Metagenome_assembly

WDL Workflow for metagenome assembly: metagenomics-pipeline drawio Python script to generate a mapping between non-redundant gene catalogue and MAGS

How this works?

The wrapper scripts in Python (located in src) will prepare files and send them to Cromwell. Cromwell executes instructions written in Workflow definition Language (WDL; located in src/wdl). To avoid dependency conflicts Cromwell runs Docker containers with preinstalled software (dockerfiles located in docker).

Introduction to WDL workflow

This pipeline will perform:

  • Pre-processing of reads with Kneaddata
  • Metagenomics assembly with Megahit
  • Gene prediction
  • Mapping of reads against the contigs
  • Metagenome binning using MetaBAT2
  • Quality assessment of genome bins
  • Taxonomic classifications
  • Gene clustering with CD-HIT-EST
  • Mapping of reads to gene clusters and computing gene counts

Requirements

  • Docker
  • conda for building the environment
    • conda env create -f pipeline.yml
  • Python

Running the pipeline

1. Install Cromwell

Use the setup_cromwell.py script to download and install it. - python src/setup_cromwell.py --save_path SAVE_PATH

2. Quality control and assembly

  • Requirements
    • input_folder - path to directory with paired shotgun sequencing files
    • bt2_index - path to a directory with a Bowite2 index. In case the folder doesn't contain an index, the user would be proposed to download GRCh38 index used for decontamination of metagenomic samples from human DNA.
    • output_folder - path to a directory where the results will be saved
  • Optional arguments
    • threads - number of threads to use (default: 1)
    • concurrent_jobs - number of concurrent jobs to run (default: 1)
  • Output
    • quality controlled .fastq.gz files
    • assembled contigs in OUTPUT_DIR/assemble
    • count table with read counts per sample
# Process the data
python src/qc_and_assemble.py -i input_folder -o OUTPUT_DIR -t 8 -c 3 -bt2_index ./GRCh38_bt2

2. (...)

Outputs

This pipeline will produce a number of directories and files

  • assemble; contains assembled contigs
  • predictgenes; gene coordinates file (GFF), protein translations and nucleotide sequences in fasta format
  • metabat2; binned contigs and a summary report
  • CheckM; genome assessment summary report
  • gtdbtk; taxonomic classification summary file
  • cluster_genes; representative sequences and list of clusters

Mapping between gene catalogue, MAGS and eggNOG annotation

Python3 script to map non-redundant gene catalogue back to contigs, MAGS and eggNOG annotations

Input requirements

  • clustering file - tab-delimited file with cluster ID and gene ID
  • Non-redundant gene catalogue (fasta)
  • Contig files in fasta
  • binned contigs (MAGS) in fasta
  • taxonomy files (tsv)
  • EggNOG annotation file (tsv)

Output

mapping table (tsv file) that links the non-redundant gene catalogue back to contigs, MAGs and to eggNOG annotations