DADA2-PE

A subworkflow for processing paired-end sequences from metabarcoding projects in order to construct ASV tables using DADA2. The example is based on the data provided by the R package. For more details, see the official website and the tutorial.

Example

This meta-wrapper can be used by integrating the following into your workflow:

# Make sure that you set the `truncLen=` option in the rule `dada2_filter_and_trim_pe` according
# to the results of the quality profile checks (after rule `dada2_quality_profile_pe` has finished on all samples).
# If in doubt, check https://benjjneb.github.io/dada2/tutorial.html#inspect-read-quality-profiles

rule all:
    input:
        # In a first run of this meta-wrapper, comment out all other inputs and only keep this one.
        # Looking at the resulting plot, adjust the `truncLen` in rule `dada2_filter_trim_pe` and then
        # rerun with all inputs uncommented.
        expand(
            "reports/dada2/quality-profile/{sample}-quality-profile.png",
            sample=["a","b"]
        ),
        "results/dada2/taxa.RDS"

rule dada2_quality_profile_pe:
    input:
        # FASTQ file without primer sequences
        expand("trimmed/{{sample}}.{orientation}.fastq.gz",orientation=[1,2])
    output:
        "reports/dada2/quality-profile/{sample}-quality-profile.png"
    log:
        "logs/dada2/quality-profile/{sample}-quality-profile-pe.log"
    wrapper:
        "0.75.0-13-g0997adf/bio/dada2/quality-profile"

rule dada2_filter_trim_pe:
    input:
        # Paired-end files without primer sequences
        fwd="trimmed/{sample}.1.fastq.gz",
        rev="trimmed/{sample}.2.fastq.gz"
    output:
        filt="filtered-pe/{sample}.1.fastq.gz",
        filt_rev="filtered-pe/{sample}.2.fastq.gz",
        stats="reports/dada2/filter-trim-pe/{sample}.tsv"
    params:
        # Set the maximum expected errors tolerated in filtered reads
        maxEE=1,
        # Set the number of kept bases in forward and reverse reads
        truncLen=[240,200]
    log:
        "logs/dada2/filter-trim-pe/{sample}.log"
    threads: 1 # set desired number of threads here
    wrapper:
        "0.75.0-13-g0997adf/bio/dada2/filter-trim"

rule dada2_learn_errors:
    input:
    # Quality filtered and trimmed forward FASTQ files (potentially compressed)
        expand("filtered-pe/{sample}.{{orientation}}.fastq.gz", sample=["a","b"])
    output:
        err="results/dada2/model_{orientation}.RDS",# save the error model
        plot="reports/dada2/errors_{orientation}.png",# plot observed and estimated rates
    params:
        randomize=True
    log:
        "logs/dada2/learn-errors/learn-errors_{orientation}.log"
    threads: 1 # set desired number of threads here
    wrapper:
        "0.75.0-13-g0997adf/bio/dada2/learn-errors"

rule dada2_dereplicate_fastq:
    input:
    # Quality filtered FASTQ file
        "filtered-pe/{fastq}.fastq.gz"
    output:
    # Dereplicated sequences stored as `derep-class` object in a RDS file
        "uniques/{fastq}.RDS"
    log:
        "logs/dada2/dereplicate-fastq/{fastq}.log"
    wrapper:
        "0.75.0-13-g0997adf/bio/dada2/dereplicate-fastq"

rule dada2_sample_inference:
    input:
    # Dereplicated (aka unique) sequences of the sample
        derep="uniques/{sample}.{orientation}.RDS",
        err="results/dada2/model_{orientation}.RDS" # Error model
    output:
        "denoised/{sample}.{orientation}.RDS" # Inferred sample composition
    log:
        "logs/dada2/sample-inference/{sample}.{orientation}.log"
    threads: 1 # set desired number of threads here
    wrapper:
        "0.75.0-13-g0997adf/bio/dada2/sample-inference"

rule dada2_merge_pairs:
    input:
      dadaF="denoised/{sample}.1.RDS",# Inferred composition
      dadaR="denoised/{sample}.2.RDS",
      derepF="uniques/{sample}.1.RDS",# Dereplicated sequences
      derepR="uniques/{sample}.2.RDS"
    output:
        "merged/{sample}.RDS"
    log:
        "logs/dada2/merge-pairs/{sample}.log"
    threads: 1 # set desired number of threads here
    wrapper:
        "0.75.0-13-g0997adf/bio/dada2/merge-pairs"

rule dada2_make_table_pe:
    input:
    # Merged composition
        expand("merged/{sample}.RDS", sample=['a','b'])
    output:
        "results/dada2/seqTab-pe.RDS"
    params:
        names=['a','b'], # Sample names instead of paths
        orderBy="nsamples" # Change the ordering of samples
    log:
        "logs/dada2/make-table/make-table-pe.log"
    threads: 1 # set desired number of threads here
    wrapper:
        "0.75.0-13-g0997adf/bio/dada2/make-table"

rule dada2_remove_chimeras:
    input:
        "results/dada2/seqTab-pe.RDS" # Sequence table
    output:
        "results/dada2/seqTab.nochimeras.RDS" # Chimera-free sequence table
    log:
        "logs/dada2/remove-chimeras/remove-chimeras.log"
    threads: 1 # set desired number of threads here
    wrapper:
        "0.75.0-13-g0997adf/bio/dada2/remove-chimeras"

rule dada2_collapse_nomismatch:
    input:
        "results/dada2/seqTab.nochimeras.RDS" # Chimera-free sequence table
    output:
        "results/dada2/seqTab.collapsed.RDS"
    log:
        "logs/dada2/collapse-nomismatch/collapse-nomismatch.log"
    threads: 1 # set desired number of threads here
    wrapper:
        "0.75.0-13-g0997adf/bio/dada2/collapse-nomismatch"

rule dada2_assign_taxonomy:
    input:
        seqs="results/dada2/seqTab.collapsed.RDS", # Chimera-free sequence table
        refFasta="resources/example_train_set.fa.gz" # Reference FASTA for taxonomy
    output:
        "results/dada2/taxa.RDS" # Taxonomic assignments
    log:
        "logs/dada2/assign-taxonomy/assign-taxonomy.log"
    threads: 1 # set desired number of threads here
    wrapper:
        "0.75.0-13-g0997adf/bio/dada2/assign-taxonomy"

Note that input, output and log file paths can be chosen freely, as long as the dependencies between the rules remain as listed here. For additional parameters in each individual wrapper, please refer to their corresponding documentation (see links below).

When running with

snakemake --use-conda

the software dependencies will be automatically deployed into an isolated environment before execution.

Used wrappers

The following individual wrappers are used in this meta-wrapper:

Please refer to each wrapper in above list for additional configuration parameters and information about the executed code.

Authors

  • Charlie Pauvert