TADA - Targeted Amplicon Diversity Analysis using DADA2, implemented in Nextflow

NOTE: We are working on a DSL2 implementation using the nf-core tools on separate branches. Based on some differences in focus we don't currently anticipate combining this with the nf-core ampliseq workflow, though we may revisit this in the future. In the meantime: We will continue to address critical bugs on this branch, but the majority of effort will be in converting the workflow to DSL2

A dada2-based workflow using the Nextflow workflow manager for Targeted Amplicon Diversity Analysis.

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Basic usage:

The latest help menu can be accessed using nextflow run h3abionet/TADA --help.

``` Usage:

This pipeline can be run specifying parameters in a config file or with command line flags. The typical example for running the pipeline with command line flags is as follows:

nextflow run h3abionet/TADA --reads '*_R{1,2}.fastq.gz' --trimFor 24 --trimRev 25 \
  --reference 'gg_13_8_train_set_97.fa.gz' -profile uct_hex

The typical command for running the pipeline with your own config (instead of command line flags) is as follows:

nextflow run h3abionet/TADA -c dada2_user_input.config -profile uct_hex

where 'dada2userinput.config' is the configuration file (see example 'dada2userinput.config')

NB: '-profile uct_hex' still needs to be specified from the command line

Parameters

Mandatory arguments: -profile Hardware config to use. Currently profile available for UCT's HPC 'ucthex' and UIUC's 'uiucsingularity' - create your own if necessary NB -profile should always be specified on the command line, not in the config file

Input (mandatory): Additionally, only one of the following must be specified: --reads Path to FASTQ read input data. If the data are single-end, set '--single-end' to true. --input Path to a sample sheet (CSV); sample sheet columns must have a headers with 'id,fastq1,fastq2'.
--seqTables Path to input R/dada2 sequence tables. Only sequence tables with the original ASV sequences as the identifier are supported

Output location: --outdir The output directory where the results will be saved

Read preparation parameters: --trimFor integer. Headcrop of read1 (set 0 if no trimming is needed) --trimRev integer. Headcrop of read2 (set 0 if no trimming is needed) --truncFor integer. Truncate read1 here (i.e. if you want to trim 10bp off the end of a 250bp R1, truncFor should be set to 240). Enforced before trimFor/trimRev --truncRev integer. Truncate read2 here ((i.e. if you want to trim 10bp off the end of a 250bp R2, truncRev should be set to 240). Enforced before trimFor/trimRev --maxEEFor integer. After truncation, R1 reads with higher than maxEE "expected errors" will be discarded. EE = sum(10^(-Q/10)), default=2 --maxEERev integer. After truncation, R1 reads with higher than maxEE "expected errors" will be discarded. EE = sum(10^(-Q/10)), default=2 --truncQ integer. Truncate reads at the first instance of a quality score less than or equal to truncQ; default=2 --maxN integer. Discard reads with more than maxN number of Ns in read; default=0 --maxLen integer. Maximum length of trimmed sequence; maxLen is enforced before trimming and truncation; default=Inf (no maximum) --minLen integer. Minimum length enforced after trimming and truncation; default=50 --rmPhiX {"T","F"}. remove PhiX from read

In addition due to modifications needed for variable-length sequences (ITS), the following are also supported.  Note if these are set,
one should leave '--trimFor/--trimRev' set to 0.

--fwdprimer                   Provided when sequence-specific trimming is required (e.g. ITS sequences using cutadapt).  Experimental
--revprimer                   Provided when sequence-specific trimming is required (e.g. ITS sequences using cutadapt).  Experimental

Read merging: --minOverlap integer. minimum length of the overlap required for merging R1 and R2; default=20 (dada2 package default=12) --maxMismatch integer. The maximum mismatches allowed in the overlap region; default=0 --trimOverhang {"T","F"}. If "T" (true), "overhangs" in the alignment between R1 and R2 are trimmed off. "Overhangs" are when R2 extends past the start of R1, and vice-versa, as can happen when reads are longer than the amplicon and read into the other-direction primer region. Default="F" (false)

Error models: --qualityBinning Binned quality correction (e.g. NovaSeq/NextSeq). default: false --errorModel NYI. Error model to use (one of 'illumina', 'illumina-binned', 'pacbio-ccs', 'custom'). This will replace '--qualityBinning'

Denoising using dada: --dadaOpt.XXX Set as e.g. --dadaOpt.HOMOPOLYMERGAPPENALTY=-1 Global defaults for the dada function, see ?setDadaOpt in R for available options and their defaults --pool Should sample pooling be used to aid identification of low-abundance ASVs? Options are pseudo pooling: "pseudo", true: "T", false: "F"

Merging arguments (optional): --minOverlap The minimum length of the overlap required for merging R1 and R2; default=20 (dada2 package default=12) --maxMismatch The maximum mismatches allowed in the overlap region; default=0. --trimOverhang If "T" (true), "overhangs" in the alignment between R1 and R2 are trimmed off. "Overhangs" are when R2 extends past the start of R1, and vice-versa, as can happen when reads are longer than the amplicon and read into the other-direction primer region. Default="F" (false) --minMergedLen Minimum length of fragment after merging; default = 0 (no minimum) --maxMergedLen Maximum length of fragment after merging; default = 0 (no maximum)

ASV identifiers: --idType The ASV IDs are renamed to simplify downstream analysis, in particular with downstream tools. The default is "md5" which will run MD5 on the sequence and generate a QIIME2-like unique hash. Alternatively, this can be set to "ASV", which simply renames the sequences in sequencial order.

Taxonomic arguments. If unset, taxonomic assignment is skipped --taxassignment Taxonomic assignment method. default = 'rdp' --reference Path to taxonomic database to be used for annotation (e.g. gg138trainset_97.fa.gz). default = false --species Specify path to fasta file. See dada2 addSpecies() for more detail. default = false --minBoot Minimum bootstrap value. default = 50 --taxLevels Listing of taxonomic levels for 'assignTaxonomy'. Experimental.

Chimera detection: --skipChimeraDetection Skip chimera detection/removal; default = false --removeBimeraDenovoOpts Additional removeBimeraDenovo options; default = ''

ASV multiple sequence alignment: --skipAlignment Skip alignment step; note this also skips ML phylogenetic analysis. default = false --aligner Aligner to use, options are 'DECIPHER' or 'infernal'. default = 'DECIPHER' --infernalCM Covariance model (Rfam-compliant) to use. default = false.

Phylogenetic analysis: --runTree Tool for ML phylogenetic analysis. Options are 'phangorn' and 'fasttree'. default = 'phangorn'

Additional output: --toBIOM Generate a BIOM v1 compliant output. default = true --toQIIME2 Generate QZA artifacts for all data for use in QIIME2. default = false

Sample names: --sampleRegex Modify sample names based on a regular expression. default = false. Note this option is deprecated in favor of using a sample sheet.

Additional options: --email Set this parameter to your e-mail address to get a summary e-mail with details of the run sent to you when the workflow exits -name Name for the pipeline run. If not specified, Nextflow will automatically generate a random mnemonic.

Help: --help Will print out summary above when executing nextflow run uct-cbio/16S-rDNA-dada2-pipeline ```

Prerequisites

Nextflow (>=20.11.0) with either Singularity (>3.4.1) or Docker (>20.10.1, though we recommend the latest based on security updates). We don't directly support non-containerized options (locally installed tools, bioconda) though these could work based on configuration.

Documentation

The h3abionet/TADA pipeline comes with documentation about the pipeline, found in the docs/ directory:

1. Installation
2. Running the pipeline

Built With

• Nextflow
• Docker
• Singularity

Credits

The initial implementation of the DADA2 pipeline as a Nextflow workflow (https://github.com/HPCBio/16S-rDNA-dada2-pipeline) was done by Chris Fields from the High Performance Computating in Biology group at the University of Illinois (http://www.hpcbio.illinois.edu). Please remember to cite the authors of DADA2 when using this pipeline. Further development to the Nextflow workflow and containerisation in Docker and Singularity for implementation on UCT's HPC was done by Dr Katie Lennard and Gerrit Botha, with inspiration and code snippets from Phil Ewels http://nf-co.re/

Contributors

The following have contributed to the development, testing, and deployment of this workflow. For the most up-to-date listing see the Contributors link.

• Katie Lennard
• Gerrit Botha
• Chris Fields
• Jessica Holmes
• Gloria Rendon
• Lindsay Clark
• Wojtek Bażant
• Negin Valizadegan

License

This project is licensed under the MIT License - see the LICENSE.md file for details