# Research Object Crate for metaTAXONx: metataxonomics pipeline for 16S marker-gene sequencing data

Original URL: https://workflowhub.eu/workflows/2054/ro_crate?version=1

[![Nextflow](https://img.shields.io/badge/nextflow%20DSL2-%E2%89%A524.10.0-23aa62.svg?labelColor=000000)](https://www.nextflow.io/) [![run with docker](https://img.shields.io/badge/run%20with-docker-0db7ed?labelColor=000000&logo=docker)](https://www.docker.com/) [![run with singularity](https://img.shields.io/badge/run%20with-singularity-1d355c.svg?labelColor=000000)](https://sylabs.io/docs/) [![nf-test](https://img.shields.io/badge/tested_with-nf--test-337ab7.svg)](https://code.askimed.com/nf-test) ## Introduction **metaTAXONx** This pipeline is a best-practice suite for the pre-processing, denoising, classification and annotation of Illumina short-read sequencing data obtained by 16S rRNA marker-gene sequencing. The pipeline contains [NF-core modules](https://github.com/nf-core/modules) and other local modules that are in the similar format. It can be runned via both docker and singularity containers. ## Pipeline summary The pipeline is able to perform different taxonomic annotation on either (single/paired) reads. The different subworkflows can be defined via `--bypass_` flags, a full overview is shown by running `--help`. The pipeline performs preprocessing of the reads via the removal of primers or adapters via [cutadapt](https://cutadapt.readthedocs.io/en/stable/) and paired-end read merging via either [FLASH](https://www.psc.edu/resources/software/flash/) or [PEAR](https://cme.h-its.org/exelixis/web/software/pear/doc.html). Before and after each step the quality control will be assessed via [fastqc](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and a [multiqc](https://github.com/MultiQC/MultiQC) report is created as output. The denoising of single-end reads is performed via [DADA2](https://benjjneb.github.io/dada2/) in batches or in paralell with the module [run-dada2-batch](https://github.com/agusinac/run-dada2-batch). ### Taxonomy assignment The ASV (generated by DADA2 after denoising) are by default being classified with [VSEARCH](https://github.com/torognes/vsearch) alignment against the [SILVA-138 SSU database](https://www.arb-silva.de/documentation/release-138/) (Ref NR 99; i.e. non-redundant 99% identity), which is a 16S rRNA gene sequences reference database for taxonomic classification. This is part of the _"classify-consensus-vsearch”_ [QIIME2 feature classifier module](https://amplicon-docs.qiime2.org/en/stable/references/plugins/feature-classifier.html). The data can be visualised as a comprehensive report via [OmicFlow](https://github.com/agusinac/OmicFlow) or as a human-readable format via [BiotaViz](https://github.com/ederveen/BiotaViz). > [!NOTE] > Classifiers need to be built with a sklearn version: 1.4.0 Specific pre-built classifiers can be assigned via `--classifier_name`, which are obtained from [QIIME2 pre-built classifiers](https://library.qiime2.org/data-resources#naive-bayes-classifiers). A custom classifier can be supplied via the `--classifier_custom` flag, a thorough guide on how to create your own classifier can be found on this [QIIME2 forum](https://forum.qiime2.org/t/processing-filtering-and-evaluating-the-silva-database-and-other-reference-sequence-data-with-rescript/15494). ### Diversity analysis The pipeline uses [QIIME2](https://amplicon-docs.qiime2.org/en/stable/) for the construction of a phylogentic tree via [fasttree](https://amplicon-docs.qiime2.org/en/stable/references/plugins/phylogeny.html#q2-action-phylogeny-fasttree) and for diversity analysis, such as [alpha diversity](https://amplicon-docs.qiime2.org/en/stable/references/plugins/diversity.html#q2-action-diversity-alpha-rarefaction) and [beta diversity](https://amplicon-docs.qiime2.org/en/stable/references/plugins/diversity.html#q2-action-diversity-beta-rarefaction). Moreover, the pipeline uses rarefied data to ensure consistent sampling depth. ## Installation > [!NOTE] > Make sure you have installed the latest [nextflow](https://www.nextflow.io/docs/latest/install.html#install-nextflow) version! Clone the repository in a directory of your choice: ```bash git clone https://github.com/CMG-GUTS/metataxonx.git ``` The pipeline is containerised, meaning it can be runned via docker or singularity images. No further actions need to be performed when using the docker profile, except a docker registery needs to be set on your local system, see [docker](https://docs.docker.com/engine/install/). In case singularity is used, images are automatically cached within the project directory. ## Usage Since the latest version, metaBIOMx works with both a samplesheet (CSV) format or a path to the input files. Preferably, samplesheets should be provided. ```bash nextflow run main.nf --input -work-dir work -profile singularity nextflow run main.nf --input <'*_{1,R1,2,R2}.{fq,fq.gz,fastq,fastq.gz}'> -work-dir work -profile singularity ``` You can also try to run the test data set in `tests/data` folder, these are also available via the `-profile test` and only work with the `'standard'` classifier name. > [!NOTE] > Tests data should be runned with the flags `--bypass_trim`, which is default on `true` in the `conf/test.config` ### 📋 Sample Metadata File Specification metaTAXONx expects your sample input data to follow a **simple, but strict** structure to ensure compatibility and allow upfront validation. The input should be provided as a **CSV** file where **each entry = one sample** with specified sequencing file paths. Additional properties not mentioned here will be ignored by the validation step. ### **Properties and Validation Rules** #### 🔹 Required properties | Property | Type | Rules / Description | |--------------|--------|----------------------------------------------------------------------------------------------------| | `sample_id` | string | Unique sample ID with no spaces (`^\S+$`). Serves as an identifier. | | `forward_read` | string | File path to forward sequencing read. Must be non-empty string matching FASTQ gzipped pattern. File must exist. | #### 🔹 Optional property | Property | Type | Rules / Description | |----------------|--------|----------------------------------------------------------------------------------------------------| | `reverse_read` | string | File path to reverse sequencing read. Same constraints as `forward_read`. Required if specified. | #### 🔹 Pattern‑based columns You can define extra variables using special prefixes: - **`CONTRAST_...`** → grouping/category labels used in differential comparisons Example: `CONTRAST_Treatment` with values `Drug` / `Placebo` These prefixes are used to generate an automated `OmicFlow` report with alpha, beta diversity and compositional plots. For more information see [OmicFlow](https://github.com/agusinac/OmicFlow). ## Support If you are having issues, please [create an issue](https://github.com/CMG-GUTS/metataxonx/issues)

Author
  Alem Gusinac, Thomas Ederveen
License
  MIT

## Contents

Main Workflow: metaTAXONx: metataxonomics pipeline for 16S marker-gene sequencing data
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Main Workflow Diagram: docs/images/metataxonx_v1.1_drawio.png
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