This workflow is an implementation of the popular DADA2 tool. I followed the steps in the Tutorial. I use IDtaxa for taxonomic annotation.
- Silas Kieser (@silask)
If you simply want to use this workflow, download and extract the latest release. If you intend to modify and further develop this workflow, fork this repository. Please consider providing any generally applicable modifications via a pull request.
In any case, if you use this workflow in a paper, don't forget to give credits to the authors by citing the URL of this repository and, if available, its DOI (see above).
The pipeline has some dependencies which an be installed with conda:
conda env create -n dada2_env --file dependencies.yml
For taxonomic annotation I use IDtaxa. A database e.g. the one from GTDB should be downloaded from here and the path added to the config file.
Configure the workflow according to your needs via editing the file config.yaml.
Create a sample table like this one. You can use the script prepare_sample_table.py for it. The scripts searches for fastq(.gz) files inside a folder (structure). If you have paired end files they should have R1/R2 somewhere in the filename. If might be a good idea to simplify sample names.
./prepare_sample_table.py path/to/fastq(.gz)files
The script creates a samples.tsv in the working directory. Here is an example.
| R1 | R2 | |
|---|---|---|
| sample1 | /path/to/fastqs/sample1/sample1_R1.fastq.gz | /path/to/fastqs/sample1/sample1_R2.fastq.gz |
| sample2 | /path/to/fastqs/sample2_R1.fastq.gz | /path/to/fastqs/sample2_R1.fastq.gz |
Test your configuration by performing a dry-run via
snakemake --configfile path/config.yaml -n
Execute the workflow locally via
snakemake --configfile path/config.yaml --cores $N
using $N cores or run it in a cluster environment via
snakemake --configfile path/config.yaml --cluster qsub --jobs 100
or
snakemake --configfile path/config.yaml --drmaa --jobs 100
See the Snakemake documentation for further details.
You can test the pipeline with the script test.py.
Callahan, B., McMurdie, P., Rosen, M. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods 13, 581–583 (2016). https://doi.org/10.1038/nmeth.3869
Murali, A., Bhargava, A. & Wright, E.S. IDTAXA: a novel approach for accurate taxonomic classification of microbiome sequences. Microbiome 6, 140 (2018). https://doi.org/10.1186/s40168-018-0521-5