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.
URL:
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:
"v1.2.0/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:
"v1.2.0/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==1.16
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()