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planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/wisecondorx commit b391b0f3348bd86a5c276dc4d3ff9dc98890c115
author artbio
date Sun, 15 Dec 2024 16:37:13 +0000
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-1:000000000000 0:bf0ebc9921f2
1 <macros>
2 <token name="@VERSION@">1.2.9</token>
3 <token name="@WRAPPER_VERSION@">@VERSION@+galaxy0</token>
4 <token name="@PROFILE@">23.0</token>
5 <token name="@pipefail@"><![CDATA[set -o | grep -q pipefail && set -o pipefail;]]></token>
6
7 <xml name="requirements">
8 <requirements>
9 <requirement type="package" version="@VERSION@">wisecondorx</requirement>
10 </requirements>
11 </xml>
12 <token name="@help@"><![CDATA[
13 **What it does**
14
15 WisecondorX, which uses a within-sample normalization technique, detects Copy
16 Number Variation from BAM input files.
17
18 It is important that **no** read quality filtering is executed prior to running
19 WisecondorX: this software requires low-quality reads to distinguish informative
20 bins from non-informative ones.
21
22 There are three main stages (converting, reference build and predicting) when
23 using WisecondorX:
24
25 **1. Convert .bam files** of aligned reads to .npz files (for both normal and
26 tumor samples) using the Galaxy tool **WisecondorX convert bam to npz**
27
28 **2. Buid a reference index** from .npz files from **normal** samples using the
29 Galaxy tool **WisecondorX build reference**.
30
31 .. class:: warningmark
32
33 Automated gender prediction, required to consistently analyze sex chromosomes,
34 is based on a Gaussian mixture model. If few samples (<20) are included during
35 reference creation, or not both male and female samples (for NIPT, this means
36 male and female feti) are represented, this process might not be accurate.
37 Therefore, alternatively, one can manually tweak the --yfrac parameter.
38
39 .. class:: warningmark
40
41 It is of paramount importance that the reference set consists of exclusively
42 negative (normal) control samples that originate from the same sequencer, mapper,
43 reference genome, type of material, ... etc, as the test samples. As a rule of
44 thumb, think of all laboratory and in silico steps: the more sources of bias that
45 can be omitted, the better.
46
47 Try to include at least 50 samples per reference. The more the better, yet, from
48 500 on it is unlikely to observe additional improvement concerning normalization.
49
50 **3. Predict Copy Number Variantions** from the reference index and tumor .npz cases
51 of interest using the Galaxy tool **WisecondorX predict CNVs**
52
53 ]]></token>
54 </macros>