alienzj / 16s-dada2

Snakemake pipeline for amplicon sequencing using dada2

Snakemake workflow: Dada2

This workflow is an implementation of the popular DADA2 tool. I followed the steps in the Tutorial. I use IDtaxa for taxonomic annotation.

Authors

• Silas Kieser (@silask)

Usage

Step 1: Install workflow

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).

Requirements:

The pipeline has some dependencies which an be installed with conda:

conda env create -n dada2_env --file dependencies.yml

Databases:

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.

Step 2: Configure workflow

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

Step 3: Execute workflow

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.

Testing

You can test the pipeline with the script test.py.

Cite

dada2

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

IDtaxa:

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