nf-ducken

Workflow to process amplicon meta-analysis data, from either local FASTQs or NCBI accession IDs to taxonomic classification.

Environment

Conda

Note for users with newer Apple processors (M1/M2): Conda environments require emulation using Rosetta, due to the lack of certain packages for the ARM64 architecture otherwise available with Intel processors. Please follow the installation and setup instructions here for details.

Conda environments are available for all processes. Launch a Conda environment-based run using -profile conda when running the workflow script.

Singularity and Docker

Containers are available for all processes. Launch a container-based run with Singularity or Docker using -profile docker or -profile singularity when running the workflow script.

Outputs

• outDir/taxonomy.qza: Artifact containing frequencies for features collapsed to a given level (default genus).
• outDir/taxonomy.qzv: Visualization containing frequencies for features collapsed to a given level (default genus).
• outDir/feature_table.qza: Artifact containing table of represented features by sample.
• outDir/stats/: Directory containing QC metrics, including FastQC, clustering statistics, denoising statistics, etc.
• outDir/trace/: Directory containing runtime metrics with an execution report and a pipeline DAG.

Process

Steps: 16S analysis

1. Data import (qiime tools import) or FASTQ download (q2-fondue)
2. Optional adapter trimming: q2-cutadapt
3. Initial quality control and denoising: q2-dada2
4. Optional chimera filtering: q2-vsearch
5. Closed reference OTU clustering: q2-vsearch
6. Taxonomy classification: q2-feature-classifier
7. Collapse to taxon of interest and merge final outputs

Steps: ITS analysis

Fungal ITS analysis (params.run_its = true) deviates from the above 16S workflow. These differences integrate standard recommendations for ITS analysis, and include the following:

• Adapter trimming is run on not only the forward and reverse reads, but also on the reverse complements of both to account for potential read-through.
  • Note in execution: These reverse complement sequences are trimmed in a subsequent step i.e. Cutadapt is run twice for a single sample. Internal analysis demonstrates inconsistent trimming when trimmed in a single step.
• Input references and classifier are required:
  • An input pre-trained classifier is required as a QIIME 2 artifact. This step requires users train their own taxonomic classifier on the UNITE database for fungal ITS sequences, instead of using available classifiers pre-trained on Greengenes or SILVA. A public pre-trained classifier can be downloaded from GitHub.
  • Input reference sequences and taxonomy are required as a QIIME 2 artifact to perform feature classification. These are available from the UNITE team as QIIME 2-compatible files.

Execution

nextflow run /path/to/workflow/main.nf -c run.config -profile conda

By default, workflow inputs may be entered as TSV or FASTQ files; the workflow is designed to generate input QIIME 2 artifacts using the import/download processes. This behavior is controlled by the generate_input parameter, set to true by default.

To use an already-created input QIIME 2 artifact, the user should set params.generate_input to false and specify the path to the input artifact using the params.input_artifact parameter. For example:

--generate_input false --input_artifact "path/to/input_artifact"