annotate read_NVC.xml @ 60:1421603cc95b draft

planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/rseqc commit 1dfe55ca83685cadb0ce8f6ebbd8c13232376d1d
author iuc
date Sat, 26 Nov 2022 15:19:14 +0000
parents dbedfc5f5a3c
children 5968573462fa
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1 <tool id="rseqc_read_NVC" name="Read NVC" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="@GALAXY_VERSION@">
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2 <description>to check the nucleotide composition bias</description>
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3 <expand macro="bio_tools"/>
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4 <macros>
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5 <import>rseqc_macros.xml</import>
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6 </macros>
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7
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8 <expand macro="requirements" />
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10 <expand macro="stdio" />
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12 <version_command><![CDATA[read_NVC.py --version]]></version_command>
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13
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14 <command><![CDATA[
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15 @BAM_SAM_INPUTS@
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16 read_NVC.py
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17 --input-file 'input.${extension}'
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18 --out-prefix output
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19 ${nx}
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20 --mapq ${mapq}
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21 ]]>
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22 </command>
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23
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24 <inputs>
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25 <expand macro="bam_sam_param" />
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26 <param name="nx" type="boolean" value="false" truevalue="--nx" falsevalue="" label="Include N,X in NVC plot" help="(--nx)"/>
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27 <expand macro="mapq_param" />
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28 <expand macro="rscript_output_param" />
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29 </inputs>
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30
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31 <outputs>
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32 <expand macro="pdf_output_data" filename="output.NVC_plot.pdf" />
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33 <expand macro="xls_output_data" filename="output.NVC.xls" />
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34 <expand macro="rscript_output_data" filename="output.NVC_plot.r" />
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35 </outputs>
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37 <tests>
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38 <test>
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39 <param name="input" value="pairend_strandspecific_51mer_hg19_chr1_1-100000.bam" />
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40 <param name="rscript_output" value="true" />
09846d5169fa planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/rseqc commit 37fb1988971807c6a072e1afd98eeea02329ee83
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41 <output name="outputxls" file="output.NVC.xls" />
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42 <output name="outputr" file="output.NVC_plot_r" />
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43 <output name="outputpdf" file="output.NVC_plot.pdf" compare="sim_size" />
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44 </test>
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45 </tests>
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47 <help><![CDATA[
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48 read_NVC.py
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49 +++++++++++
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50
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51 This module is used to check the nucleotide composition bias. Due to random priming, certain
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52 patterns are over represented at the beginning (5'end) of reads. This bias could be easily
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53 examined by NVC (Nucleotide versus cycle) plot. NVC plot is generated by overlaying all
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54 reads together, then calculating nucleotide composition for each position of read
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55 (or each sequencing cycle). In ideal condition (genome is random and RNA-seq reads is
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56 randomly sampled from genome), we expect A%=C%=G%=T%=25% at each position of reads.
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57
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58 NOTE: this program expect a fixed read length
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60 Inputs
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61 ++++++++++++++
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62
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63 Input BAM/SAM file
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64 Alignment file in BAM/SAM format.
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65
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66 Include N,X in NVC plot
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67 Plots N and X alongside A, T, C, and G in plot.
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68
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69 Output
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70 ++++++++++++++
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71
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72 This module is used to check the nucleotide composition bias. Due to random priming, certain patterns are over represented at the beginning (5'end) of reads. This bias could be easily examined by NVC (Nucleotide versus cycle) plot. NVC plot is generated by overlaying all reads together, then calculating nucleotide composition for each position of read (or each sequencing cycle). In ideal condition (genome is random and RNA-seq reads is randomly sampled from genome), we expect A%=C%=G%=T%=25% at each position of reads.
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75 1. output.NVC.xls: plain text file, each row is position of read (or sequencing cycle), each column is nucleotide (A,C,G,T,N,X)
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76 2. output.NVC_plot.r: R script to generate NVC plot.
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77 3. output.NVC_plot.pdf: NVC plot.
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79
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80 .. image:: $PATH_TO_IMAGES/NVC_plot.png
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81 :height: 600 px
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82 :width: 600 px
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83 :scale: 80 %
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84
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85 @ABOUT@
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86
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87 ]]>
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88 </help>
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89
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90 <expand macro="citations" />
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91
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92 </tool>