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comparison nanonet_1D.xml @ 0:decd5688d719 draft

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planemo upload for repository https://github.com/jvolkening/galaxy-tools/tree/master/tools/nanonet commit bf5788ad5a3293446a50a3246b44ba09174c9b71
author jdv
date Wed, 30 Aug 2017 02:53:02 -0400
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1 <tool id="nanonet_1D" name="Nanonet 1D" version="2.0.0">
2
3 <description>ONT development basecaller</description>
4
5 <!-- ***************************************************************** -->
6
7 <!--
8 <requirements>
9 <requirement type="package" version="2.0.0">nanonet</requirement>
10 </requirements>
11 -->
12
13 <!-- ***************************************************************** -->
14
15 <version_command>echo "2.0.0"</version_command>
16
17 <!-- ***************************************************************** -->
18
19 <command detect_errors="aggressive">
20 <![CDATA[
21
22 python3 $__tool_directory__/nanonet_1D.py $input $output \${GALAXY_SLOTS:-1}
23
24 ]]>
25 </command>
26
27 <!-- ***************************************************************** -->
28
29 <inputs>
30
31 <param name="input" type="data" format="fast5_archive" label="Input reads" />
32
33 </inputs>
34
35 <!-- ***************************************************************** -->
36
37 <outputs>
38
39 <data name="output" format="fastq" label="${tool.name} on ${on_string} (called.fastq)" />
40
41 </outputs>
42
43 <!-- ***************************************************************** -->
44
45 <tests>
46 <!-- multithreaded output is non-deterministic, so simply compare file
47 sizes -->
48 <test>
49 <param name="input" value="test_data.fast5.tar.gz" ftype="fast5_archive" />
50 <output name="output" file="test_data.fastq" compare="sim_size" delta="0" />
51 </test>
52 </tests>
53
54 <!-- ***************************************************************** -->
55
56 <help>
57 <![CDATA[
58
59 **Description**
60
61 Nanonet provides recurrent neural network basecalling for Oxford Nanopore
62 MinION data. It represents the first generation of such a basecaller from
63 Oxford Nanopore Technologies, and is provided as a technology demonstrator.
64 Nanonet is provided unsupported by Oxford Nanopore Technologies, see
65 LICENSE.md for more information.
66
67 For training networks, Nanonet leverages currennt to run recurrent neural
68 networks. Currennt is generally run with GPUs to aid performance but can be
69 run in a CPU only environment. The basecaller does not require currennt, and
70 is written in pure python with minimal requirements.
71
72 The Galaxy wrapper has modified nanonet to take a gzip tarball of FAST5 reads
73 as input, such as can be produced by `poretools combine`, and always outputs a
74 single FASTQ file.
75
76 This is the 1D basecaller.
77
78 ]]>
79 </help>
80
81 <!-- ***************************************************************** -->
82
83 <citations>
84 </citations>
85
86 </tool>