Mercurial > repos > iuc > transit_gumbel

annotate transit_gumbel.xml @ 3:ecce0cbe659f draft

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"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/transit/ commit bd061799ebd49ada1a666d0815720c18291728da"
author iuc
date Wed, 16 Oct 2019 04:32:08 -0400
parents c980be2c002c
children f50807720df1
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1 <?xml version="1.0"?>
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2 <tool id="transit_gumbel" name="Gumbel" version="@VERSION@+galaxy2">
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3 <description>- determine essential genes</description>
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4 <macros>
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5 <import>macros.xml</import>
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6 </macros>
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7 <expand macro="requirements" />
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8 <command detect_errors="exit_code"><![CDATA[
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9 @LINK_INPUTS@
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10 transit gumbel $input_files annotation.dat transit_out.txt
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11 @STANDARD_OPTIONS@
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12 -s $samples -b $burnin -m $smallest -t $trim
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13 ]]>
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14 </command>
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15 <inputs>
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16 <expand macro="standard_inputs">
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17 <expand macro="handle_replicates" />
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18 <param name="samples" argument="-s" type="integer" value="10000" label="Number of samples" />
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19 <param name="burnin" argument="-b" type="integer" value="500" label="Number of Burn-in samples" />
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20 <param name="smallest" argument="-m" type="integer" value="1" label="Smallest read-count to consider" />
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21 <param name="trim" argument="-t" type="integer" value="1" label="Trimming interval for values" />
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22 </expand>
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23 </inputs>
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24 <outputs>
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25 <expand macro="outputs" />
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26 </outputs>
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27 <tests>
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28 <test>
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29 <param name="inputs" ftype="wig" value="transit-in1-rep1.wig,transit-in1-rep2.wig" />
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30 <param name="annotation" ftype="tabular" value="transit-in1.prot" />
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31 <param name="samples" value="1000" />
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32 <param name="burnin" value="100" />
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33 <param name="replicates" value="Replicates" />
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34 <output name="sites" file="gumbel-sites1.txt" ftype="tabular" compare="sim_size" />
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35 </test>
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36 </tests>
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38 <help><![CDATA[
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39 .. class:: infomark
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41 **What it does**
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43 -------------------
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45 The **Gumbel** method can be used to determine which genes are essential in a single condition. It does a gene-by-gene analysis of the insertions at TA sites with each gene, makes a call based on the longest consecutive sequence of TA sites without insertion in the genes, calculates the probability of this using a Bayesian model.
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46
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47 Note : Intended only for Himar1 datasets.
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49 -------------------
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51 **Inputs**
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53 -------------------
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54
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55 Input files for Gumnbel need to be:
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57 - .wig files: Tabulated files containing one column with the TA site coordinate and one column with the read count at this site.
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58 - annotation .prot_table: Annotation file generated by the `Convert Gff3 to prot_table for TRANSIT` tool.
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61 -------------------
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63 **Parameters**
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65 -------------------
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66
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67 Optional Arguments:
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68 -s <integer> := Number of samples. Default: -s 10000
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69 -b <integer> := Number of Burn-in samples. Default -b 500
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70 -m <integer> := Smallest read-count to consider. Default: -m 1
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71 -t <integer> := Trims all but every t-th value. Default: -t 1
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72 -r <string> := How to handle replicates. Sum or Mean. Default: -r Mean
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73 --iN <float> := Ignore TAs occuring at given fraction of the N terminus. Default: -iN 0.0
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74 --iC <float> := Ignore TAs occuring at given fraction of the C terminus. Default: -iC 0.0
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75 -n <string> := Determines which normalization method to use. Default -n TTR
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78 - Samples: Gumbel uses Metropolis-Hastings (MH) to generate samples of posterior distributions. The default setting is to run the simulation for 10,000 iterations. This is usually enough to assure convergence of the sampler and to provide accurate estimates of posterior probabilities. Less iterations may work, but at the risk of lower accuracy.
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79 - Burn-In: Because the MH sampler many not have stabilized in the first few iterations, a “burn-in” period is defined. Samples obtained in this “burn-in” period are discarded, and do not count towards estimates.
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80 - Trim: The MH sampler produces Markov samples that are correlated. This parameter dictates how many samples must be attempted for every sampled obtained. Increasing this parameter will decrease the auto-correlation, at the cost of dramatically increasing the run-time. For most situations, this parameter should be left at the default of “1”.
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81 - Minimum Read: The minimum read count that is considered a true read. Because the Gumbel method depends on determining gaps of TA sites lacking insertions, it may be susceptible to spurious reads (e.g. errors). The default value of 1 will consider all reads as true reads. A value of 2, for example, will ignore read counts of 1.
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82 - Replicates: Determines how to deal with replicates by averaging the read-counts or summing read counts across datasets. This should not have an affect for the Gumbel method, aside from potentially affecting spurious reads.
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83 - Normalisation :
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84 - TTR (Default) : Trimmed Total Reads (TTR), normalized by the total read-counts (like totreads), but trims top and bottom 5% of read-counts. This is the recommended normalization method for most cases as it has the beneffit of normalizing for difference in saturation in the context of resampling.
c980be2c002c "planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/transit/ commit f63413d629e4de3c69984b3a96ad8ccfe0d47ada"
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85 - nzmean : Normalizes datasets to have the same mean over the non-zero sites.
c980be2c002c "planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/transit/ commit f63413d629e4de3c69984b3a96ad8ccfe0d47ada"
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86 - totreads : Normalizes datasets by total read-counts, and scales them to have the same mean over all counts.
c980be2c002c "planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/transit/ commit f63413d629e4de3c69984b3a96ad8ccfe0d47ada"
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87 - zinfnb : Fits a zero-inflated negative binomial model, and then divides read-counts by the mean. The zero-inflated negative binomial model will treat some empty sites as belonging to the “true” negative binomial distribution responsible for read-counts while treating the others as “essential” (and thus not influencing its parameters).
c980be2c002c "planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/transit/ commit f63413d629e4de3c69984b3a96ad8ccfe0d47ada"
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88 - quantile : Normalizes datasets using the quantile normalization method described by Bolstad et al. (2003). In this normalization procedure, datasets are sorted, an empirical distribution is estimated as the mean across the sorted datasets at each site, and then the original (unsorted) datasets are assigned values from the empirical distribution based on their quantiles.
c980be2c002c "planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/transit/ commit f63413d629e4de3c69984b3a96ad8ccfe0d47ada"
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89 - betageom : Normalizes the datasets to fit an “ideal” Geometric distribution with a variable probability parameter p. Specially useful for datasets that contain a large skew. See Beta-Geometric Correction .
c980be2c002c "planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/transit/ commit f63413d629e4de3c69984b3a96ad8ccfe0d47ada"
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90 - nonorm : No normalization is performed.
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91
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92
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93 -------------------
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94
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95 **Outputs**
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96
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97 -------------------
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98
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99 ============================================= ========================================================================================================================
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100 **Column Header** **Column Definition**
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101 --------------------------------------------- ------------------------------------------------------------------------------------------------------------------------
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102 Orf Gene ID
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103 Name Gene Name
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104 Desc Gene Description
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105 k Number of Transposon Insertions Observed within the ORF.
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106 n Total Number of TA dinucleotides within the ORF.
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107 r Span of nucleotides for the Maximum Run of Non-Insertions.
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108 s Span of nucleotides for the Maximum Run of Non-Insertions.
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109 zbar Posterior Probability of Essentiality.
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110 State Call Essentiality call for the gene. Depends on FDR corrected thresholds. E=Essential U=Uncertain, NE=Non-Essential, S=too short
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111 ============================================= ========================================================================================================================
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112
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113
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114
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115 Note: Technically, Bayesian models are used to calculate posterior probabilities, not p-values (which is a concept associated with the frequentist framework). However, we have implemented a method for computing the approximate false-discovery rate (FDR) that serves a similar purpose. This determines a threshold for significance on the posterior probabilities that is corrected for multiple tests. The actual thresholds used are reported in the headers of the output file (and are near 1 for essentials and near 0 for non-essentials). There can be many genes that score between the two thresholds (t1 < zbar < t2). This reflects intrinsic uncertainty associated with either low read counts, sparse insertion density, or small genes. If the insertion_density is too low (< ~30%), the method may not work as well, and might indicate an unusually large number of Uncertain or Essential genes.
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116
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117 -------------------
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118
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119 **More Information**
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120
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121 -------------------
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122
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123 See `TRANSIT documentation`
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124
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125 - TRANSIT: https://transit.readthedocs.io/en/latest/index.html
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126 - `TRANSIT Gumbel`: https://transit.readthedocs.io/en/latest/transit_methods.html#gumbel
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127 ]]></help>
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128
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129 <expand macro="citations" />
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130
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131 </tool>