jianhong/16S_pipeline

16S RNA microbiome pipeline

Introduction

16S_pipeline is a bioinformatics best-practice analysis pipeline for 16S rRNA gene sequencing.

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The Nextflow DSL2 implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. Where possible, these processes have been submitted to and installed from nf-core/modules in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community!

Pipeline summary

0. Prepare fastq files (bcl2fastq)
1. Read QC (FastQC)
2. Remove primers (trimmomatic)
3. Sync barcodes (fastq_pair_filter.py)
4. Demultiplex (qiime2::demux)
5. Filter reads (DATA2)
6. Run dada2 (DATA2)
7. Visualization (Krona)
8. Present QC for raw reads (MultiQC)

Quick Start

1. Install Nextflow (>=21.10.3)

2. Install any of Docker, Singularity, Podman, Shifter or Charliecloud for full pipeline reproducibility (please only use Conda as a last resort; see docs)

3. Download the pipeline and test it on a minimal dataset with a single command:

   nextflow run jianhong/16S_pipeline -profile test,YOURPROFILE

   Note that some form of configuration will be needed so that Nextflow knows how to fetch the required software. This is usually done in the form of a config profile (YOURPROFILE in the example command above). You can chain multiple config profiles in a comma-separated string.

   • The pipeline comes with config profiles called docker, singularity, podman, shifter, charliecloud and conda which instruct the pipeline to use the named tool for software management. For example, -profile test,docker.
   • Please check nf-core/configs to see if a custom config file to run nf-core pipelines already exists for your Institute. If so, you can simply use -profile <institute> in your command. This will enable either docker or singularity and set the appropriate execution settings for your local compute environment.
   • If you are using singularity and are persistently observing issues downloading Singularity images directly due to timeout or network issues, then you can use the --singularity_pull_docker_container parameter to pull and convert the Docker image instead. Alternatively, you can use the nf-core download command to download images first, before running the pipeline. Setting the NXF_SINGULARITY_CACHEDIR or singularity.cacheDir Nextflow options enables you to store and re-use the images from a central location for future pipeline runs.
   • If you are using conda, it is highly recommended to use the NXF_CONDA_CACHEDIR or conda.cacheDir settings to store the environments in a central location for future pipeline runs.

4. Start running your own analysis!

   nextflow run jianhong/16S_pipeline -profile <docker/singularity/podman/shifter/charliecloud/conda/institute> --input '[path to raw reads files]' --barcodes '[path to barcodes tsv file]' --metadata '[path to metadata csv file]'

   Run it on cluster.

   First prepare a profile config file named as profile.config.

   // submit by slurm
   process.executor = "slurm"
   process.clusterOptions = "-J microbiome"
   params {
       // Input data
       input  = 'path/to/your/fastqfiles'
       skip_bcl2fastq = true
       barcodes = 'path/to/your/barcodes.tsv'
       metadata = 'path/to/your/metadata.csv'
   
       // Genome references
       silva_nr99 = 'https://zenodo.org/record/4587955/files/silva_nr99_v138.1_train_set.fa.gz?download=1'
       silva_tax = 'https://zenodo.org/record/4587955/files/silva_species_assignment_v138.1.fa.gz?download=1'
   }

   Then run:

   nextflow run jianhong/16S_pipeline -profile <docker/singularity/podman/shifter/charliecloud/conda/institute> -c profile.config

Create conda container for nextflow

1. Install conda.

   wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh
   bash Miniconda3-latest-Linux-x86_64.sh

2. Create nextflow environment.

   conda create -y --name microbiome bioconda::nextflow=21.10.6

3. Create profile config file named as profile.config.

   // submit by slurm
   process.executor = "slurm"
   process.clusterOptions = "-J JL21"
   params {
       // Input data
       input  = 'Spinach_done' // replace Spinach_done by your own file
       barcodes = '16S_pipeline_JL21/0_mapping/barcodes.tsv'
       metadata = '16S_pipeline_JL21/0_mapping/metadata.csv'
   
       // report email
       email = 'your@email.addr'
   }

4. Activate nextflow environment and Run the pipeline.

   conda activate microbiome
   module load bcl2fastq/2.20
   nextflow run jianhong/16S_pipeline -r main -profile conda -c profile.config

Documentation

The 16S_pipeline pipeline comes with documentation about the pipeline usage, and output.

Credits

16S_pipeline was originally written by Jianhong Ou, and Jeff Letourneau.

We thank the following people for their extensive assistance in the development of this pipeline:

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

Citations

If you use 16S_pipeline for your analysis, please cite it using the following doi: 10.5281/zenodo.451935888

An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.

You can cite the nf-core publication as follows:

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.