DADA2_SAMPLE_INFERENCE

DADA2 Inferring sample composition using dada2 dada function. Optional parameters are documented in the manual and the function is introduced in the dedicated tutorial section.

URL:

Example

This wrapper can be used in the following way:

rule dada2_sample_inference:
    input:
    # Dereplicated (aka unique) sequences of the sample
        derep="uniques/{fastq}.RDS",
        err="results/dada2/model_1.RDS" # Error model
    output:
        "denoised/{fastq}.RDS" # Inferred sample composition
    # Even though this is an R wrapper, use named arguments in Python syntax
    # here, to specify extra parameters. Python booleans (`arg1=True`, `arg2=False`)
    # and lists (`list_arg=[]`) are automatically converted to R.
    # For a named list as an extra named argument, use a python dict
    #   (`named_list={name1=arg1}`).
    #params:
    #    verbose=True
    log:
        "logs/dada2/sample-inference/{fastq}.log"
    threads: 1 # set desired number of threads here
    wrapper:
        "v1.2.1/bio/dada2/sample-inference"

Note that input, output and log file paths can be chosen freely.

When running with

snakemake --use-conda

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

Software dependencies

  • bioconductor-dada2==1.16

Input/Output

Input:

  • derep: RDS file with the dereplicated sequences
  • err: RDS file with the error model

Output:

  • RDS file with the stored inferred sample composition

Params

  • optional arguments for ``dada(), please provide them as python key=value pairs``:

Authors

  • Charlie Pauvert

Code

# __author__ = "Charlie Pauvert"
# __copyright__ = "Copyright 2020, Charlie Pauvert"
# __email__ = "cpauvert@protonmail.com"
# __license__ = "MIT"

# Snakemake wrapper for inferring sample composition using dada2 dada function.

# Sink the stderr and stdout to the snakemake log file
# https://stackoverflow.com/a/48173272
log.file<-file(snakemake@log[[1]],open="wt")
sink(log.file)
sink(log.file,type="message")

library(dada2)

# Prepare arguments (no matter the order)
args<-list(
           derep = readRDS(snakemake@input[["derep"]]),
           err = readRDS(snakemake@input[["err"]]),
           multithread = snakemake@threads
           )
# Check if extra params are passed
if(length(snakemake@params) > 0 ){
       # Keeping only the named elements of the list for do.call()
       extra<-snakemake@params[ names(snakemake@params) != "" ]
       # Add them to the list of arguments
       args<-c(args, extra)
} else{
    message("No optional parameters. Using default parameters from dada2::dada()")
}

# Learn errors rates for both read types
inferred_composition<-do.call(dada, args)

# Store the inferred sample composition as RDS files
saveRDS(inferred_composition, snakemake@output[[1]],compress = T)

# Proper syntax to close the connection for the log file
# but could be optional for Snakemake wrapper
sink(type="message")
sink()