DADA2_FILTER_TRIM¶
DADA2
Quality filtering of single or paired-end reads using dada2 filterAndTrim
function. Optional parameters are documented in the manual and the function is introduced in the dedicated tutorial section.
Example¶
This wrapper can be used in the following way:
rule dada2_filter_trim_se:
input:
# Single-end files without primers sequences
fwd="trimmed/{sample}.1.fastq.gz"
output:
filt="filtered-se/{sample}.1.fastq.gz",
stats="reports/dada2/filter-trim-se/{sample}.tsv"
# 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:
# Set the maximum expected errors tolerated in filtered reads
maxEE=1,
# Set the number of kept bases to 7 for the toy example
truncLen=7,
# Set minLen to 1 for the toy example but default is 20
minLen=1
log:
"logs/dada2/filter-trim-se/{sample}.log"
threads: 1 # set desired number of threads here
wrapper:
"v1.23.5/bio/dada2/filter-trim"
rule dada2_filter_trim_pe:
input:
# Paired-end files without primers sequences
fwd="trimmed/{sample}.1.fastq",
rev="trimmed/{sample}.2.fastq"
output:
filt="filtered-pe/{sample}.1.fastq.gz",
filt_rev="filtered-pe/{sample}.2.fastq.gz",
stats="reports/dada2/filter-trim-pe/{sample}.tsv"
# 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:
# Set the maximum expected errors tolerated in filtered reads
maxEE=1,
# Set the number of kept bases in forward and reverse reads
# respectively to 7 for the toy example
truncLen=[7,6],
# Set minLen to 1 for the toy example but default is 20
minLen=1
log:
"logs/dada2/filter-trim-pe/{sample}.log"
threads: 1 # set desired number of threads here
wrapper:
"v1.23.5/bio/dada2/filter-trim"
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=1.26.0
Input/Output¶
Input:
fwd
: a forward FASTQ file (potentially compressed) without primer sequencesrev
: an (optional) reverse FASTQ file (potentially compressed) without primer sequences
Output:
filt
: a compressed filtered forward FASTQ filefilt_rev
: an (optional) compressed filtered reverse FASTQ filestats
: a .tsv file with the number of processed and filtered reads per sample
Params¶
optional arguments for ``filterAndTrim()
, please provide them as pythonkey=value
pairs``:
Authors¶
- Charlie Pauvert
Code¶
# __author__ = "Charlie Pauvert"
# __copyright__ = "Copyright 2020, Charlie Pauvert"
# __email__ = "cpauvert@protonmail.com"
# __license__ = "MIT"
# Snakemake wrapper for filtering single or paired-end reads using dada2 filterAndTrim 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(
fwd = snakemake@input[["fwd"]],
filt = snakemake@output[["filt"]],
multithread=snakemake@threads
)
# Test if paired end input is passed
if(!is.null(snakemake@input[["rev"]]) & !is.null(snakemake@output[["filt_rev"]])){
args<-c(args,
rev = snakemake@input[["rev"]],
filt.rev = snakemake@output[["filt_rev"]]
)
}
# 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) != "" ]
# Check if 'compress=' option is passed
if(!is.null(extra[["compress"]])){
stop("Remove the `compress=` option from `params`.\n",
"The `compress` option is implicitly set here from the file extension.")
} else {
# Check if output files are given as compressed files
# ex: in se version, all(TRUE, NULL) gives TRUE
compressed <- c(
endsWith(args[["filt"]], '.gz'),
if(is.null(args[["filt.rev"]])) NULL else {endsWith(args[["filt.rev"]], 'gz')}
)
if ( all(compressed) ) {
extra[["compress"]] <- TRUE
} else if ( any(compressed) ) {
stop("Either all or no fastq output should be compressed. Please check `output.filt` and `output.filt_rev` for consistency.")
} else {
extra[["compress"]] <- FALSE
}
}
# Add them to the list of arguments
args<-c(args, extra)
} else {
message("No optional parameters. Using default parameters from dada2::filterAndTrim()")
}
# Call the function with arguments
filt.stats<-do.call(filterAndTrim, args)
# Write processed reads report
write.table(filt.stats, snakemake@output[["stats"]], sep="\t", quote=F)
# Proper syntax to close the connection for the log file
# but could be optional for Snakemake wrapper
sink(type="message")
sink()