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https://github.com/silask/16s-dada2

Snakemake pipeline for amplicon sequencing using dada2
https://github.com/silask/16s-dada2

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Snakemake pipeline for amplicon sequencing using dada2

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# Snakemake workflow: Dada2



[![Snakemake](https://img.shields.io/badge/snakemake-≥5-brightgreen.svg)](https://snakemake.bitbucket.io)

[![dada2](https://img.shields.io/badge/dada2-v1.14-brightgreen.svg)](https://benjjneb.github.io/dada2/index.html)

[![Build Status](https://travis-ci.org/snakemake-workflows/amplicon-seq-dada2.svg?branch=master)](https://travis-ci.org/snakemake-workflows/amplicon-seq-dada2)



This workflow is an implementation of the popular DADA2 tool. I followed the steps in the [Tutorial](https://benjjneb.github.io/dada2/tutorial.html). I use IDtaxa for taxonomic annotation.



![dada2](https://benjjneb.github.io/dada2/images/DADA2_Logo_Text_1_14_640px.png)



## Authors



* Silas Kieser (@silask)



## Usage



### Step 1: Install workflow



If you simply want to use this workflow, download and extract the [latest release](https://github.com/snakemake-workflows/amplicon-seq-dada2/releases).

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](http://www2.decipher.codes/Downloads.html) 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](.test/samples.tsv). 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](https://snakemake.readthedocs.io) 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