view dada2_dada.xml @ 3:0b3194ac6a95 draft

planemo upload for repository https://github.com/bernt-matthias/mb-galaxy-tools/tree/topic/dada2/tools/dada2 commit 5b1603bbcd3f139cad5c876be83fcb39697b5613-dirty

<tool id="dada2_dada" name="dada2: dada" version="@DADA2_VERSION@+galaxy@WRAPPER_VERSION@">
    <description>Remove sequencing errors</description>
    <macros>
        <import>macros.xml</import>
    </macros>
    <expand macro="requirements"/>
    <expand macro="version_command"/>
    <command detect_errors="exit_code"><![CDATA[
    #if $batch_cond.batch_select == "no"
        mkdir output &&
    #end if
    Rscript '$dada2_script' \${GALAXY_SLOTS:-1}
    ]]></command>
    <configfiles>
        <configfile name="dada2_script"><![CDATA[
library(ggplot2, quietly=T)
library(dada2, quietly=T)

args <- commandArgs(trailingOnly = TRUE)
nthreads <- as.integer(args[1])

#if $batch_cond.batch_select == "no"
derep <- list()
#for $d in $batch_cond.derep:
derep[["$d.element_identifier"]] <- readRDS('$d')
#end for
#else
derep <- readRDS('$batch_cond.derep')
#end if

err <- readRDS('$errorrates')

#if $batch_cond.batch_select == "yes":
pool <- F
#else
    #if $batch_cond.pool == "TRUE"
pool <- T
    #else if $batch_cond.pool == "FALSE"
pool <- F
    #else
pool <- 'pseudo'
    #end if
#end if
dada_result <- dada(derep, err,
## not needed for end user:    errorEstimationFunction = $errfoo, selfConsist = $selfconsist,
    pool = pool, multithread = nthreads)

    #if $batch_cond.batch_select == "no":
    #if len($batch_cond.derep) > 1:
    for( id in names(dada_result) ){
        saveRDS(dada_result[[id]], file=file.path("output" ,paste(id, "dada2_dada", sep=".")))
    }
    #else
    #for $d in $batch_cond.derep:
        saveRDS(dada_result, file=file.path("output" ,paste('$d.element_identifier', "dada2_dada", sep=".")))
    #end for
    #end if
#else
    saveRDS(dada_result, file='$dada')
#end if

## for( id in names(dada_result) ){
##     tab <- dada_result[[id]]\$clustering
##     tabnames <- names(tab)
##     tab\$sample <- id
##     tabnames<-c("sample", tabnames)
##     tab <- tab[,tabnames]
##     write.table(tab, file = '$dada', append = T, quote = F, sep = "\t", row.names = F, col.names = F)
## }
    ]]></configfile>
    </configfiles>
    <inputs>
        <conditional name="batch_cond">
            <param name="batch_select" type="select" label="Process samples in batches">
                <option value="no">no</option>
                <option value="yes">yes</option>
            </param>
            <when value="yes">
                <param name="derep" type="data" format="dada2_derep" label="Dereplicated reads"/>
            </when>
            <when value="no">
                <param name="derep" type="data" multiple="true" format="dada2_derep" label="Dereplicated reads"/>
                <param argument="pool" type="select" label="Pool samples">
                    <option value="FALSE">process samples individually</option>
                    <option value="TRUE">pool samples</option>
                    <option value="pseudo">pseudo pooling between individually processed samples</option>
                </param>
            </when>
        </conditional>
        <param name="errorrates" type="data" format="dada2_errorrates" label="Error rates"/>
        <!-- not needed for end user I guess
        <expand macro="errorEstimationFunction"/>
        <param name="selfconsist" type="boolean" checked="false" truevalue="TRUE" falsevalue="FALSE" label="Alternate between sample inference and error rate estimation until convergence"/>-->
    </inputs>
    <outputs>
        <data name="dada" format="dada2_dada">
            <filter>batch_cond['batch_select']=="yes"</filter>
        </data>
        <collection name="data_collection" type="list">
            <discover_datasets pattern="__name_and_ext__" directory="output" />
            <filter>batch_cond['batch_select']=="no"</filter>
        </collection>
    </outputs>
    <tests>
        <test>
            <param name="batch_cond|batch_select" value="no"/>
            <param name="batch_cond|derep" value="derepFastq_F3D0_R1.Rdata" ftype="dada2_derep" />
            <param name="errorrates" value="learnErrors_F3D0_R1.Rdata" ftype="dada2_errorrates" />
            <output_collection name="data_collection" type="list">
                <element name="derepFastq_F3D0_R1.Rdata" file="dada_F3D0_R1.Rdata" ftype="dada2_dada"/>
            </output_collection>
        </test>
        <test>
            <param name="batch_cond|batch_select" value="no"/>
            <param name="batch_cond|derep" value="derepFastq_F3D0_R2.Rdata" ftype="dada2_derep" />
            <param name="errorrates" value="learnErrors_F3D0_R2.Rdata" ftype="dada2_errorrates" />
            <output_collection name="data_collection" type="list">
                <element name="derepFastq_F3D0_R2.Rdata" file="dada_F3D0_R2.Rdata" ftype="dada2_dada"/>
            </output_collection>
        </test>
    </tests>
    <help><![CDATA[
Description
...........

The dada function takes as input dereplicated amplicon sequencing reads and returns the inferred
composition of the sample (or samples). Put another way, dada removes all sequencing errors to
reveal the members of the sequenced community.

Usage
.....

**Input:**

- A number of dada2_derep datasets computed with *derepFastq* (given as collection or multiple data sets)
- An dada2_errorrates data set computed with learnErrors

You can decide to compute the data jointly or in batches.

- Jointly (Process "samples in batches"=no): A single Galaxy job is started that processes all derep data sets jointly. You may chose different pooling strategies: if the started dada job processes the samples individually, pooled, or pseudo pooled.
- In batches (Process "samples in batches"=yes): A separate Galaxy job is started for earch derep data set. This is equivalent to joint processing and choosing to process samples individually.

While the single dada job (in case of joint processing) can use multiple cores on one compute node, batched processing distributes the work on a number of jobs (equal to the number of input derep data sets) where each can use multiple cores. Hence, if you intend to or need to process the data sets individually, batched processing is more efficient -- in particular if Galaxy has access to a larger number of compute ressources.

A typical use case of individual processing of the samples are large data sets for which the pooled strategy needs to much time or memory.

**Output**: a data set of type dada2_dada (which is a RData file containing the output of dada2's dada function).

The output of this tool can serve as input for *dada2: mergePairs*, *dada2: removeBimeraDinovo*, and "dada2: makeSequenceTable"

Details
.......

Briefly, dada implements a statistical test for the notion that a specific sequence was seen too many times to have been caused by amplicon errors from currently inferred sample sequences. Overly abundant sequences are used as the seeds of new partitions of sequencing reads, and the final set of partitions is taken to represent the denoised composition of the sample. A more detailed explanation of the algorithm is found in the dada2 puplication (see below) and https://doi.org/10.1186/1471-2105-13-283. dada depends on a parametric error model of substitutions. Thus the quality of its sample inference is affected by the accuracy of the estimated error rates. All comparisons between sequences performed by dada depend on pairwise alignments. This step is the most computationally intensive part of the algorithm, and two alignment heuristics have been implemented in dada for speed: A kmer-distance screen and banded Needleman-Wunsch alignmemt.

@HELP_OVERVIEW@
    ]]></help>
    <expand macro="citations"/>
</tool>