view qualimap_rnaseq.xml @ 1:ce0da6c9f49e draft

"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/qualimap commit 08beeabf7a8e91d0e95f5cdb71249dd6efe2bd46"

<tool id="qualimap_rnaseq" name="QualiMap RNA-Seq QC" version="@VERSION@+galaxy1">
    <macros>
        <import>qualimap_macros.xml</import>
    </macros>
    <expand macro="requirements" />
    <expand macro="version_command" />
    <command detect_errors="exit_code"><![CDATA[
        @SET_JAVA_OPTS@ &&

        ## Have qualimap detect file format by suffix
        ## since its autodetection is unreliable.
        ln -s '$features' features.gtf &&

        qualimap rnaseq
        -bam '${seq_info.input}'
        -gtf features.gtf
        ${seq_info.treat_as_pe}
        ${seq_info.sorted}
        ${counts_out.report_counts}
        --sequencing-protocol ${read_filtering.library_type}
        --algorithm ${read_filtering.treat_multimappers}
        -outdir results -outformat html &&

        #set $report_name = 'qualimapReport'
        #set $summary_report = 'rnaseq_qc_results.txt'
        #if str($counts_out.report_counts):
            #set $ccol_name = str($counts_out.ccol_name).strip() or str($seq_info.input.name).replace(' ', '_')
            printf '#GeneID\t%s\n' '$ccol_name' > '$output_counts' &&
            cat results/counts.txt >> '$output_counts' &&
        #end if
        @MASSAGE_OUTPUT@
    ]]></command>
    <inputs>
        <conditional name="seq_info">
            <param argument="-pe" name="treat_as_pe" type="select"
            label="Counting mode"
            help="You will usually want to choose 'Count fragments' for paired-end data. For single-end data, choose 'Count reads'. See tool help below.">
                <option value="">Count reads</option>
                <option value="--paired">Count fragments</option>
            </param>
            <when value="">
                <param argument="-bam" name="input" type="data" format="bam"
                label="Mapped reads input dataset" />
                <param name="sorted" type="hidden" value="" />
            </when>
            <when value="--paired">
                <param argument="-bam" name="input" type="data" format="qname_sorted.bam"
                label="Mapped reads input dataset" />
                <param name="sorted" type="hidden" value="--sorted" />
            </when>
        </conditional>
        <param argument="-gtf" name="features" type="data" format="gtf"
        label="Genome annotation data" />
        <conditional name="counts_out">
            <param argument="-oc" name="report_counts" type="select"
            label="Keep the per-gene counts data?"
            help="The resulting dataset can, for example, serve as input to QualiMap Counts QC for further assessment.">
                <option value="">No, just report statistics</option>
                <option value="-oc counts.txt">Yes, generate separate counts output</option>
            </param>
            <when value="" />
            <when value="-oc counts.txt">
                <param name="ccol_name" type="text"
                label="Name to use for the counts column"
                help="Consider using the name of the analyzed sample here. Default: Name of the mapped reads input dataset in the history" />
            </when>
        </conditional>
        <section name="read_filtering" title="Read selection for counting" expanded="true">
            <param argument="-p" name="library_type" type="select" display="radio"
            label="Strandedness">
                <option value="non-strand-specific">Count reads/fragments independent of strandedness</option>
                <option value="strand-specific-forward">Count only reads/fragments expected in forward-stranded data</option>
                <option value="strand-specific-reverse">Count only reads/fragments expected in reverse-stranded data</option>
            </param>
            <param argument="-a" name="treat_multimappers" type="select" display="radio"
            label="Multimapping reads">
                <option value="uniquely-mapped-reads">Count uniquely mapped reads only</option>
                <option value="proportional">Count also multimapping reads</option>
            </param>
        </section>
    </inputs>
    <outputs>
        <data name="output_html" format="html"
        label="${tool.name} report on ${on_string}" />
        <data name="output_counts" format="tsv"
        label="${tool.name} counts on ${on_string}">
            <filter>str(counts_out['report_counts'])</filter>
        </data>
        <collection name="raw_data" type="list"
        label="Raw data for ${tool.name} on ${on_string}">
            <data name="rnaseq_qc_results" format="txt" from_work_dir="results/summary_report.txt" />
            <data name="coverage_profile_along_genes_high" format="tsv" from_work_dir="results/coverage_profile_along_genes_high.txt" />
            <data name="coverage_profile_along_genes_low" format="tsv" from_work_dir="results/coverage_profile_along_genes_low.txt" />
            <data name="coverage_profile_along_genes_total" format="tsv" from_work_dir="results/coverage_profile_along_genes_total.txt" />
        </collection>
    </outputs>
    <tests>
        <test expect_num_outputs="6">
            <conditional name="seq_info">
                <param name="treat_as_pe" value="" />
                <param name="input" value="test_mapped_reads.bam" />
            </conditional>
            <param name="features" value="features.gtf" />
            <output name="output_html" ftype="html">
                <assert_contents>
                    <has_text text="Qualimap report: RNA Seq QC" />
                </assert_contents>
            </output>
            <output_collection name="raw_data" type="list">
                <element name="rnaseq_qc_results" file="rnaseq_qc_results_default.txt" ftype="txt" compare="diff" lines_diff="4" />
            </output_collection>
        </test>
        <test expect_num_outputs="7">
            <conditional name="seq_info">
                <param name="treat_as_pe" value="--paired" />
                <param name="input" value="test_mapped_reads.bam" />
            </conditional>
            <param name="features" value="features.gtf" />
            <conditional name="counts_out">
                <param name="report_counts" value="-oc counts.txt" />
                <param name="ccol_name" value="try_this" />
            </conditional>
            <section name="read_filtering">
                <param name="library_type" value="strand-specific-forward" />
                <param name="treat_multimappers" value="proportional" />
            </section>
            <output name="output_html" ftype="html">
                <assert_contents>
                    <has_text text="Qualimap report: RNA Seq QC" />
                </assert_contents>
            </output>
            <output name="output_counts" file="rnaseq_qc_counts_custom.txt" ftype="tsv" />
            <output_collection name="raw_data" type="list">
                <element name="rnaseq_qc_results" file="rnaseq_qc_results_custom.txt" ftype="txt" compare="diff" lines_diff="4" />
            </output_collection>
        </test>
    </tests>
    <help><![CDATA[
**What it does**

**Qualimap RNA-Seq QC** reports quality control metrics and bias estimations
which are specific for whole transcriptome sequencing, including reads genomic
origin, junction analysis, transcript coverage and 5’-3’ bias computation.
As such, the tool complements the more general analysis with QualiMap BamQC,
and its (optional) gene counts output can be analyzed further with QualiMap
Counts QC.


Input
=====

*Mapped reads input dataset*

The dataset holding the mapped reads to carry out the analysis with. Typically,
this will have been produced by a splicing-aware aligner like *HISAT2* or *RNA
STAR*.

*Genome annotation data*

A GTF dataset of genomic features that mapped reads should be counted for.


Parameters
----------

*Counting mode*

Determines whether reads should be counted individually, or whether multiple
reads originating from the same sequencing template (*i.e.*, the read and its
mate in paired-end sequencing) should be counted as one.

You will usually want to choose ``Count fragments`` for paired-end data. For
single-end data, choose ``Count reads``.

*Keep the per-gene counts data?*

Controls whether the optional Counts output dataset should be produced, or not.

If you choose to produce this dataset, you can use:

*Name to use for the counts column* to specify the name of the second column in
that output.

Using, for example, the name of the analyzed sample here can help you keep
track of your data, especially when joining several counts datasets into a
count matrix later on. In addition, *Qualimap Counts QC* will reuse the
names of counts columns as sample names.

**Read selection for counting** section

*Strandedness*

Choose here the option that fits the strand-specificity of your sequencing
library.

The Galaxy Training Material has an excellent discussion of sequencing
data strandedness included in the
`Reference-based RNA-Seq data analysis <https://galaxyproject.github.io/training-material/topics/transcriptomics/tutorials/ref-based/tutorial.html#count-the-number-of-reads-per-annotated-gene>`__
tutorial.

*Multimapping reads*

Choose here how to treat reads that are mapped ambiguously to several genome locations.

- *Count uniquely mapped reads only* excludes multi-mapping reads

- *Count also multimapping reads* activates *proportional* counting of
  multi-mapping reads.

  In this mode, each read is weighted according to the number of mapped
  locations. For example, a read mapped to 4 different locations will add 0.25
  to the "counts" of each of the locations it maps to. The final calculated
  counts per feature will be converted to integer numbers.

Note: Detection of multi-mapping reads by the tool relies on the ``NH`` tag of
reads in the BAM input, so make sure the aligner used to produce the dataset is
configured to write this tag.


Outputs
=======

HTML Report
-----------

**Summary Section**

*Reads alignment*

Summarizes the mapping characteristics of the reads in the input:

- total number of mapped reads

  reported as left/right read mates in case of paired-end reads; excludes
  secondary alignments

  If you accidentally selected `Count fragments` as the *Counting mode* for
  single-end data these and the following count of *Number of aligned pairs*
  will be zero.

- total number of alignments

  reports all alignment records found, including secondary alignments

- number of secondary alignments

- number of non-unique alignments

  reports the number of alignment records with an ``NH`` tag greater than one;
  corresponds to the number of alignments that will have been skipped during
  counting when *Count uniquely mapped reads only* is selected

- number of reads aligned to genes

- number of ambiguous alignments

  This is the number of mapped reads that span multiple annotated genes.
  Such reads are always skipped during counting.

- no feature assigned

  reports the number of alignments that are not overlapping any annotated
  feature; these may represent alignments to introns or intergenic regions, or,
  if the number is really high, may indicate a problem with your genome
  annotations

- not aligned

  number of reads not mapped by the aligner (but included in the BAM input)

- strand specificity estimation (fwd/rev)

  computed if *Count reads/fragments independent of strandedness* is selected;
  estimate of the proportion of alignments in line with forward- and reverse-
  strand-specificitiy of the sequencing library

  Balanced proportions (*i.e.* ~ 0.5 forward- and ~ 0.5 reverse-strand support)
  can be interpreted as likely non-strand-specificity of the sequencing library,
  while a strand-specific library would manifest itself in a large fraction of
  reads supporting that specific strand-specificity.

*Reads genomic origin*

Lists how many alignments (absolute number/fraction) fall into

- exonic,
- intronic,
- intergenic

regions, or are at least

- overlapping an exon.

*Transcript coverage profile*

The profile provides ratios between mean coverage of 5’ regions, 3’ regions and whole transcripts.

- 5’ bias

  the ratio of coverage median of 5’ regions (defined as the first 100 nts) to whole transcripts

- 3' bias

  the ratio of coverage median of 3’ regions (defined as the last 100 nts) to whole transcripts

- 5’-3’ bias

  the ratio of 5' bias to 3' bias.

*Junction analysis*

Lists the total number of reads with splice junctions and the relative
frequency of the (up to) 10 most frequent junction sequences.


**Plots**

*Reads Genomic Origin*

A pie chart showing how many read alignments fall into exonic, intronic and
intergenic regions.

*Coverage Profile Along Genes (Total)*

This plot shows the mean coverage profile of all genes with non-zero
overall coverage.

*Coverage Profile Along Genes (Low)*

The plot shows the mean coverage profile of the 500 genes with the lowest, but non-zero overall coverage.

*Coverage Profile Along Genes (High)*

The plot shows the mean coverage profile of the 500 genes with the highest
overall coverage.

*Coverage Histogram (0-50x)*

Coverage of genes from 0 to 50x. Genes with >50x coverage are added to the 50x
bin.

*Junction Analysis*

This pie chart shows an analysis of the splice junctions observed in the
alignments. It consists of three categories:

- Known

  observed splice junctions both sides of which are in line with the genome
  annotation data

- Partly known

  observed splice junctions for which only one junction side can be deduced
  from the genome annotation data

- Novel

  observed splice junctions not predicted on either side by the genome
  annotation data


Raw data
--------

This is a *Collection* of 4 individual datasets.

Of these, the *rnaseq_qc_results* dataset provides a plain-text version of the
*HTML report* *Summary* section.

The other 3 datasets hold the tabular raw data underlying the three coverage
profile plots in the *HTML Report*.


Counts data
-----------

Optional. This is a 2-column tabular dataset of read or fragment counts
(depending on the chosen *Counting mode*) per annotated gene. The first column
lists the gene identifiers found in the *Genome annotation data*, the second
the associated counts.

This dataset represents valid (single-sample) input for the QualiMap Counts QC
tool.
    ]]></help>
    <expand macro="citations"/>
</tool>