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comparison macros.xml @ 13:3fbefde449bc draft

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"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/medaka commit 52289bc7b99bfa8a3bda46cb35cea98399419dab"
author iuc
date Thu, 18 Nov 2021 20:01:04 +0000
parents 11fedf536104
children 1d62240feff3
comparison
equal deleted inserted replaced
12:597407d61386 13:3fbefde449bc
1 <?xml version="1.0"?>
2 <macros> 1 <macros>
3 <token name="@TOOL_VERSION@">1.3.2</token> 2 <token name="@TOOL_VERSION@">1.4.4</token>
3 <token name="@VERSION_SUFFIX@">0</token>
4 <token name="@PROFILE@">20.01</token> 4 <token name="@PROFILE@">20.01</token>
5 <xml name="bio_tools"> 5 <xml name="bio_tools">
6 <xrefs> 6 <xrefs>
7 <xref type="bio.tools">khmer</xref> 7 <xref type="bio.tools">medaka</xref>
8 </xrefs> 8 </xrefs>
9 </xml> 9 </xml>
10 <xml name="requirements"> 10 <xml name="requirements">
11 <requirements> 11 <requirements>
12 <requirement type="package" version="@TOOL_VERSION@">medaka</requirement> 12 <requirement type="package" version="@TOOL_VERSION@">medaka</requirement>
50 50
51 <xml name="b" token_argument="-b"> 51 <xml name="b" token_argument="-b">
52 <param argument="@ARGUMENT@" type="integer" value="100" min="1" label="Set inference batch size"/> 52 <param argument="@ARGUMENT@" type="integer" value="100" min="1" label="Set inference batch size"/>
53 </xml> 53 </xml>
54 <xml name="model" token_argument="-m" token_label="Select model"> 54 <xml name="model" token_argument="-m" token_label="Select model">
55 <param argument="@ARGUMENT@" type="select" label="@LABEL@"> 55 <param argument="@ARGUMENT@" type="select" label="@LABEL@" help="For best results it is important to specify the correct model,
56 according to the basecaller used. Medaka models are named to indicate i) the pore type, ii) the sequencing device (MinION
57 or PromethION), iii) the basecaller variant, and iv) the basecaller version">
58 <option value="r103_fast_g507">r103_fast_g507</option>
59 <option value="r103_fast_snp_g507">r103_fast_snp_g507</option>
60 <option value="r103_fast_variant_g507">r103_fast_variant_g507</option>
61 <option value="r103_hac_g507">r103_hac_g507</option>
62 <option value="r103_hac_snp_g507">r103_hac_snp_g507</option>
63 <option value="r103_hac_variant_g507">r103_hac_variant_g507</option>
56 <option value="r103_min_high_g345">r103_min_high_g345</option> 64 <option value="r103_min_high_g345">r103_min_high_g345</option>
57 <option value="r103_min_high_g360">r103_min_high_g360</option> 65 <option value="r103_min_high_g360">r103_min_high_g360</option>
58 <option value="r103_prom_high_g360">r103_prom_high_g360</option> 66 <option value="r103_prom_high_g360">r103_prom_high_g360</option>
59 <option value="r103_prom_snp_g3210">r103_prom_snp_g3210</option> 67 <option value="r103_prom_snp_g3210">r103_prom_snp_g3210</option>
60 <option value="r103_prom_variant_g3210">r103_prom_variant_g3210</option> 68 <option value="r103_prom_variant_g3210">r103_prom_variant_g3210</option>
69 <option value="r103_sup_g507">r103_sup_g507</option>
70 <option value="r103_sup_snp_g507">r103_sup_snp_g507</option>
71 <option value="r103_sup_variant_g507">r103_sup_variant_g507</option>
72 <option value="r104_e81_fast_g5015">r104_e81_fast_g5015</option>
73 <option value="r104_e81_hac_g5015">r104_e81_hac_g5015</option>
74 <option value="r104_e81_sup_g5015">r104_e81_sup_g5015</option>
61 <option value="r10_min_high_g303">r10_min_high_g303</option> 75 <option value="r10_min_high_g303">r10_min_high_g303</option>
62 <option value="r10_min_high_g340">r10_min_high_g340</option> 76 <option value="r10_min_high_g340">r10_min_high_g340</option>
63 <option value="r941_min_fast_g303">r941_min_fast_g303</option> 77 <option value="r941_min_fast_g303">r941_min_fast_g303</option>
78 <option value="r941_min_fast_g507">r941_min_fast_g507</option>
79 <option value="r941_min_fast_snp_g507">r941_min_fast_snp_g507</option>
80 <option value="r941_min_fast_variant_g507">r941_min_fast_variant_g507</option>
81 <option value="r941_min_hac_g507">r941_min_hac_g507</option>
82 <option value="r941_min_hac_snp_g507">r941_min_hac_snp_g507</option>
83 <option value="r941_min_hac_variant_g507">r941_min_hac_variant_g507</option>
64 <option value="r941_min_high_g303">r941_min_high_g303</option> 84 <option value="r941_min_high_g303">r941_min_high_g303</option>
65 <option value="r941_min_high_g330">r941_min_high_g330</option> 85 <option value="r941_min_high_g330">r941_min_high_g330</option>
66 <option value="r941_min_high_g340_rle">r941_min_high_g340_rle</option> 86 <option value="r941_min_high_g340_rle">r941_min_high_g340_rle</option>
67 <option value="r941_min_high_g344">r941_min_high_g344</option> 87 <option value="r941_min_high_g344">r941_min_high_g344</option>
68 <option value="r941_min_high_g351">r941_min_high_g351</option> 88 <option value="r941_min_high_g351">r941_min_high_g351</option>
69 <option value="r941_min_high_g360" selected="true">r941_min_high_g360</option> 89 <option value="r941_min_high_g360" selected="true">r941_min_high_g360</option>
90 <option value="r941_min_sup_g507">r941_min_sup_g507</option>
91 <option value="r941_min_sup_snp_g507">r941_min_sup_snp_g507</option>
92 <option value="r941_min_sup_variant_g507">r941_min_sup_variant_g507</option>
70 <option value="r941_prom_fast_g303">r941_prom_fast_g303</option> 93 <option value="r941_prom_fast_g303">r941_prom_fast_g303</option>
94 <option value="r941_prom_fast_g507">r941_prom_fast_g507</option>
95 <option value="r941_prom_fast_snp_g507">r941_prom_fast_snp_g507</option>
96 <option value="r941_prom_fast_variant_g507">r941_prom_fast_variant_g507</option>
97 <option value="r941_prom_hac_g507">r941_prom_hac_g507</option>
98 <option value="r941_prom_hac_snp_g507">r941_prom_hac_snp_g507</option>
99 <option value="r941_prom_hac_variant_g507">r941_prom_hac_variant_g507</option>
71 <option value="r941_prom_high_g303">r941_prom_high_g303</option> 100 <option value="r941_prom_high_g303">r941_prom_high_g303</option>
72 <option value="r941_prom_high_g330">r941_prom_high_g330</option> 101 <option value="r941_prom_high_g330">r941_prom_high_g330</option>
73 <option value="r941_prom_high_g344">r941_prom_high_g344</option> 102 <option value="r941_prom_high_g344">r941_prom_high_g344</option>
74 <option value="r941_prom_high_g360">r941_prom_high_g360</option> 103 <option value="r941_prom_high_g360">r941_prom_high_g360</option>
75 <option value="r941_prom_high_g4011">r941_prom_high_g4011</option> 104 <option value="r941_prom_high_g4011">r941_prom_high_g4011</option>
76 <option value="r941_prom_snp_g303">r941_prom_snp_g303</option> 105 <option value="r941_prom_snp_g303">r941_prom_snp_g303</option>
77 <option value="r941_prom_snp_g322">r941_prom_snp_g322</option> 106 <option value="r941_prom_snp_g322">r941_prom_snp_g322</option>
78 <option value="r941_prom_snp_g360">r941_prom_snp_g360</option> 107 <option value="r941_prom_snp_g360">r941_prom_snp_g360</option>
108 <option value="r941_prom_sup_g507">r941_prom_sup_g507</option>
109 <option value="r941_prom_sup_snp_g507">r941_prom_sup_snp_g507</option>
110 <option value="r941_prom_sup_variant_g507">r941_prom_sup_variant_g507</option>
79 <option value="r941_prom_variant_g303">r941_prom_variant_g303</option> 111 <option value="r941_prom_variant_g303">r941_prom_variant_g303</option>
80 <option value="r941_prom_variant_g322">r941_prom_variant_g322</option> 112 <option value="r941_prom_variant_g322">r941_prom_variant_g322</option>
81 <option value="r941_prom_variant_g360">r941_prom_variant_g360</option> 113 <option value="r941_prom_variant_g360">r941_prom_variant_g360</option>
82 </param> 114 </param>
83 </xml> 115 </xml>
109 <token name="@WID@"><![CDATA[ 141 <token name="@WID@"><![CDATA[
110 *medaka* is a tool suite to create a consensus sequence from nanopore sequencing data. 142 *medaka* is a tool suite to create a consensus sequence from nanopore sequencing data.
111 143
112 This task is performed using neural networks applied from a pileup of individual sequencing reads against a draft assembly. It outperforms graph-based methods operating on basecalled data, and can be competitive with state-of-the-art signal-based methods, whilst being much faster. 144 This task is performed using neural networks applied from a pileup of individual sequencing reads against a draft assembly. It outperforms graph-based methods operating on basecalled data, and can be competitive with state-of-the-art signal-based methods, whilst being much faster.
113 ]]></token> 145 ]]></token>
146
147 <token name="@MODELS@"><![CDATA[
148
149 ----
150
151 .. class:: infomark
152
153 **Models**
154
155 For best results it is important to specify the correct model, -m in the above, according to the basecaller used. Allowed values can be found by running medaka tools list\_models.
156
157 Medaka models are named to indicate i) the pore type, ii) the sequencing device (MinION or PromethION), iii) the basecaller variant, and iv) the basecaller version, with the format:
158
159 ::
160
161 {pore}_{device}_{caller variant}_{caller version}
162
163 For example the model named r941_min_fast_g303 should be used with data from MinION (or GridION) R9.4.1 flowcells using the fast Guppy basecaller version 3.0.3. By contrast the model
164 r941_prom_hac_g303 should be used with PromethION data and the high accuracy basecaller (termed "hac" in Guppy configuration files). Where a version of Guppy has been used without an exactly corresponding medaka model, the medaka model with the highest version equal to or less than the guppy version should be selected.
165
166 ]]></token>
167
114 <token name="@REFERENCES@"><![CDATA[ 168 <token name="@REFERENCES@"><![CDATA[
115 More information are available in the `manual <https://nanoporetech.github.io/medaka/index.html>`_ and `github <https://github.com/nanoporetech/medaka>`_. 169 More information are available in the `manual <https://nanoporetech.github.io/medaka/index.html>`_ and `github <https://github.com/nanoporetech/medaka>`_.
116 ]]></token> 170 ]]></token>
117 </macros> 171 </macros>