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WisecondorX galaxy wrapper (#696)
* ~64 commits to develop this first version of WisecondorX Galaxy wrapper * Downgrading to release_23.1 for the ci tests !
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.github/workflows/pr.yaml

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- '*'
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env:
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GALAXY_FORK: galaxyproject
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GALAXY_BRANCH: release_23.2
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GALAXY_BRANCH: release_23.1
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MAX_CHUNKS: 4
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MAX_FILE_SIZE: 2M
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concurrency:

tools/wisecondorx/.shed.yml

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# .shed.yml supporting automatic pushes.
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owner: artbio
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name: wisecondorx
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description: WisecondorX
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long_description: |
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Improved copy number detection for routine shallow whole-genome sequencing.
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See homepage URL for manual and code.
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categories:
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- Variant Analysis
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homepage_url: https://github.com/CenterForMedicalGeneticsGhent/WisecondorX/tree/master
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remote_repository_url: https://github.com/ARTbio/tools-artbio/tree/master/tools/wisecondorx
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toolshed:
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- toolshed

tools/wisecondorx/macro.xml

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<macros>
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<token name="@VERSION@">1.2.9</token>
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<token name="@WRAPPER_VERSION@">@VERSION@+galaxy0</token>
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<token name="@PROFILE@">23.0</token>
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<token name="@pipefail@"><![CDATA[set -o | grep -q pipefail && set -o pipefail;]]></token>
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<xml name="requirements">
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<requirements>
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<requirement type="package" version="@VERSION@">wisecondorx</requirement>
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</requirements>
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</xml>
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<token name="@help@"><![CDATA[
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**What it does**
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WisecondorX, which uses a within-sample normalization technique, detects Copy
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Number Variation from BAM input files.
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It is important that **no** read quality filtering is executed prior to running
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WisecondorX: this software requires low-quality reads to distinguish informative
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bins from non-informative ones.
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There are three main stages (converting, reference build and predicting) when
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using WisecondorX:
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**1. Convert .bam files** of aligned reads to .npz files (for both normal and
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tumor samples) using the Galaxy tool **WisecondorX convert bam to npz**
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**2. Buid a reference index** from .npz files from **normal** samples using the
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Galaxy tool **WisecondorX build reference**.
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.. class:: warningmark
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Automated gender prediction, required to consistently analyze sex chromosomes,
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is based on a Gaussian mixture model. If few samples (<20) are included during
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reference creation, or not both male and female samples (for NIPT, this means
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male and female feti) are represented, this process might not be accurate.
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Therefore, alternatively, one can manually tweak the --yfrac parameter.
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.. class:: warningmark
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It is of paramount importance that the reference set consists of exclusively
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negative (normal) control samples that originate from the same sequencer, mapper,
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reference genome, type of material, ... etc, as the test samples. As a rule of
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thumb, think of all laboratory and in silico steps: the more sources of bias that
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can be omitted, the better.
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Try to include at least 50 samples per reference. The more the better, yet, from
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500 on it is unlikely to observe additional improvement concerning normalization.
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**3. Predict Copy Number Variantions** from the reference index and tumor .npz cases
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of interest using the Galaxy tool **WisecondorX predict CNVs**
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]]></token>
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</macros>
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