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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/rseqc commit d7544582d5599c67a284faf9232cd2ccc4daa1de
author iuc
date Tue, 09 Apr 2024 11:24:55 +0000
parents 473382134e56
children
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1 <tool id="rseqc_tin" name="Transcript Integrity Number" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="@GALAXY_VERSION@">
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2 <description>
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3 evaluates RNA integrity at a transcript level
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4 </description>
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5 <macros>
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6 <import>rseqc_macros.xml</import>
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7 </macros>
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8 <expand macro="bio_tools"/>
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9 <expand macro="requirements"/>
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10 <expand macro="stdio"/>
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11 <version_command><![CDATA[tin.py --version]]></version_command>
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12 <!-- Generate output files here because tin.py removes all instances of "bam"
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13 in the filename -->
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14 <command><![CDATA[
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15 #import re
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16 #set $input_list = []
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17 #for $i, $input in enumerate($input):
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18 #set $safename = re.sub('[^\w\-_]', '_', $input.element_identifier)
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19 #if $safename in $input_list:
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20 #set $safename = str($safename) + "." + str($i)
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21 #end if
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22 $input_list.append($safename)
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23 ln -sf '${input}' '${safename}.bam' &&
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24 ln -sf '${input.metadata.bam_index}' '${safename}.bam.bai' &&
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25 echo '${safename}.bam' >> 'input_list.txt' &&
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26 #end for
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27 tin.py -i 'input_list.txt' --refgene='${refgene}' --minCov=${minCov}
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28 --sample-size=${samplesize} ${subtractbackground}
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29 && mv *summary.txt summary.tab
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30 && mv *tin.xls tin.xls
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31 ]]>
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32 </command>
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33 <inputs>
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34 <param name="input" type="data" format="bam" multiple="true" label="Input BAM file" help="(--input-file)"/>
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35 <expand macro="refgene_param"/>
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36 <param name="minCov" type="integer" value="10" label="Minimum coverage (default=10)" help="Minimum number of reads mapped to a transcript (--minCov)."/>
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37 <param name="samplesize" type="integer" value="100" label="Sample size (default=100)" help="Number of equal-spaced nucleotide positions picked from mRNA. Note: if this number is larger than the length of mRNA (L), it will be halved until is's smaller than L. (--sample-size)."/>
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38 <param name="subtractbackground" type="boolean" value="false" falsevalue="" truevalue="--subtract-background" label="Subtract background noise (default=No)" help="Subtract background noise (estimated from intronic reads). Only use this option if there are substantial intronic reads (--subtract-background)."/>
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39 </inputs>
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40 <outputs>
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41 <data name="outputsummary" format="tabular" from_work_dir="summary.tab" label="TIN on ${on_string} (summary)"/>
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42 <data name="outputxls" format="tabular" from_work_dir="tin.xls" label="TIN on ${on_string} (tin)"/>
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43 </outputs>
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44 <!-- PDF Files contain R version, must avoid checking for diff -->
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45 <tests>
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46 <test expect_num_outputs="2">
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47 <param name="input" value="pairend_strandspecific_51mer_hg19_chr1_1-100000.bam"/>
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48 <param name="refgene" value="hg19_RefSeq_chr1_1-100000.bed" ftype="bed12"/>
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49 <output name="outputsummary" file="summary.tin.txt" ftype="tabular"/>
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50 <output name="outputxls" file="output.tin.xls" ftype="tabular"/>
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51 </test>
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52 </tests>
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53 <help><![CDATA[
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54 ## tin.py
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56 This program is designed to evaluate RNA integrity at transcript level. TIN
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57 (transcript integrity number) is named in analogous to RIN (RNA integrity
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58 number). RIN (RNA integrity number) is the most widely used metric to
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59 evaluate RNA integrity at sample (or transcriptome) level. It is a very
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60 useful preventive measure to ensure good RNA quality and robust,
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61 reproducible RNA sequencing. However, it has several weaknesses:
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63 * RIN score (1 <= RIN <= 10) is not a direct measurement of mRNA quality.
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64 RIN score heavily relies on the amount of 18S and 28S ribosome RNAs, which
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65 was demonstrated by the four features used by the RIN algorithm: the
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66 “total RNA ratio” (i.e. the fraction of the area in the region of 18S and
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67 28S compared to the total area under the curve), 28S-region height, 28S
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68 area ratio and the 18S:28S ratio24. To a large extent, RIN score was a
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69 measure of ribosome RNA integrity. However, in most RNA-seq experiments,
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70 ribosome RNAs were depleted from the library to enrich mRNA through either
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71 ribo-minus or polyA selection procedure.
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72
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73 * RIN only measures the overall RNA quality of an RNA sample. However, in real
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74 situation, the degradation rate may differs significantly among
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75 transcripts, depending on factors such as “AU-rich sequence”, “transcript
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76 length”, “GC content”, “secondary structure” and the “RNA-protein
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77 complex”. Therefore, RIN is practically not very useful in downstream
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78 analysis such as adjusting the gene expression count.
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79
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80 * RIN has very limited sensitivity to measure substantially degraded RNA
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81 samples such as preserved clinical tissues. (ref:
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82 https://www.scribd.com/document/352764986/DV200-Technote-Truseq-Rna-Access).
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84 To overcome these limitations, we developed TIN, an algorithm that is able
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85 to measure RNA integrity at transcript level. TIN calculates a score (0 <=
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86 TIN <= 100) for each expressed transcript, however, the medTIN (i.e.
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87 meidan TIN score across all the transcripts) can also be used to measure
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88 the RNA integrity at sample level. Below plots demonstrated TIN is a
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89 useful metric to measure RNA integrity in both transcriptome-wise and
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90 transcript-wise, as demonstrated by the high concordance with both RIN and
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91 RNA fragment size (estimated from RNA-seq read pairs).
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92
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93
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94 ## Inputs
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95
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96 Input BAM/SAM file
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97 Alignment file in BAM/SAM format.
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98
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99 Reference gene model
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100 Gene Model in BED format. Must be standard 12-column BED file.
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101
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102 Minimum coverage
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103 Minimum number of reads mapped to a tracript (default is 10).
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104
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105 Sample size
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106 Number of equal-spaced nucleotide positions picked from mRNA. Note: if
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107 this number is larger than the length of mRNA (L), it will be halved until
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108 it’s smaller than L (default is 100).
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109
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110 Subtract background
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111 Subtract background noise (estimated from intronic reads). Only use this
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112 option if there are substantial intronic reads.
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113
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114
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115 ## Outputs
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116
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117 Text
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118 Table that includes the gene identifier (geneID), chromosome (chrom),
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119 transcript start (tx_start), transcript end (tx_end), and transcript
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120 integrity number (TIN).
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121
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122 Example output:
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123
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124 ------ ----- ---------- --------- -------------
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125 geneID chrom tx_start tx_end TIN
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126 ------ ----- ---------- --------- -------------
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127 ABCC2 chr10 101542354 101611949 67.6446525761
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128 IPMK chr10 59951277 60027694 86.383618429
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129 RUFY2 chr10 70100863 70167051 43.8967503948
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130 ------ ----- ---------- --------- -------------
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131
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132 @ABOUT@
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133
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134 ]]>
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135 </help>
63
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136 <expand macro="citations"/>
51
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137 </tool>