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1 <tool id="rgGRR1" name="GRR:">
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2 <description>Pairwise Allele Sharing</description>
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3 <command interpreter="python">
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4 rgGRR.py $i.extra_files_path/$i.metadata.base_name "$i.metadata.base_name"
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5 '$out_file1' '$out_file1.files_path' "$title" '$n' '$Z'
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6 </command>
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7 <inputs>
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8 <param name="i" type="data" label="Genotype data file from your current history"
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9 format="ldindep" />
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10 <param name='title' type='text' size="80" value='rgGRR' label="Title for this job"/>
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11 <param name="n" type="integer" label="N snps to use (0=all)" value="5000" />
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12 <param name="Z" type="float" label="Z score cutoff for outliers (eg 2)" value="6"
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13 help="2 works but for very large numbers of pairs, you might want to see less than 5%" />
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14 </inputs>
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15 <outputs>
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16 <data format="html" name="out_file1" label="${title}_rgGRR.html"/>
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17 </outputs>
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18
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19 <tests>
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20 <test>
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21 <param name='i' value='tinywga' ftype='ldindep' >
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22 <metadata name='base_name' value='tinywga' />
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23 <composite_data value='tinywga.bim' />
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24 <composite_data value='tinywga.bed' />
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25 <composite_data value='tinywga.fam' />
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26 <edit_attributes type='name' value='tinywga' />
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27 </param>
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28 <param name='title' value='rgGRRtest1' />
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29 <param name='n' value='100' />
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30 <param name='Z' value='6' />
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31 <param name='force' value='true' />
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32 <output name='out_file1' file='rgtestouts/rgGRR/rgGRRtest1.html' ftype='html' compare="diff" lines_diff='350'>
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33 <extra_files type="file" name='Log_rgGRRtest1.txt' value="rgtestouts/rgGRR/Log_rgGRRtest1.txt" compare="diff" lines_diff="170"/>
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34 <extra_files type="file" name='rgGRRtest1.svg' value="rgtestouts/rgGRR/rgGRRtest1.svg" compare="diff" lines_diff="1000" />
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35 <extra_files type="file" name='rgGRRtest1_table.xls' value="rgtestouts/rgGRR/rgGRRtest1_table.xls" compare="diff" lines_diff="100" />
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36 </output>
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37 </test>
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38 </tests>
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39
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40
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41 <help>
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42
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43 .. class:: infomark
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44
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45 **Explanation**
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46
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47 This tool will calculate allele sharing among all subjects, one pair at a time. It outputs measures of average alleles
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48 shared and measures of variability for each pair of subjects and creates an interactive image where each pair is
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49 plotted in this mean/variance space. It is based on the GRR windows application available at
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50 http://www.sph.umich.edu/csg/abecasis/GRR/
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51
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52 The plot is interactive - you can unselect one of the relationships in the legend to remove all those points
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53 from the plot for example. Details of outlier pairs will pop up when the pointer is over them. e found by moving your pointer
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54 over them. This relies on a working browser SVG plugin - try getting one installed for your browser if the interactivity is
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55 broken.
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56
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57 -----
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58
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59 **Syntax**
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60
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61 - **Genotype file** is the input pedigree data chosen from available library Plink binary files
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62 - **Title** will be used to name the outputs so make it mnemonic and useful
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63 - **N** is left 0 to use all snps - otherwise you get a random sample - much quicker with little loss of precision > 5000 SNPS
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64
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65 **Summary**
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66
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67 Warning - this tool works pairwise so slows down exponentially with sample size. An LD-reduced dataset is
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68 strongly recommended as it will give good resolution with relatively few SNPs. Do not use all million snps from a whole
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69 genome chip - it's overkill - 5k is good, 10k is almost indistinguishable from 100k.
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70
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71 SNP are sampled randomly from the autosomes - otherwise parent/child pairs will be separated by gender.
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72 This tool will estimate mean pairwise allele shareing among all subjects. Based on the work of Abecasis, it has
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73 been rewritten so it can run with much larger data sets, produces cross platform svg and runs
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74 on a Galaxy server, instead of being MS windows only. Written in is Python, it uses numpy, and the innermost loop
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75 is inline C so it can calculate about 50M SNPpairs/sec on a typical opteron server.
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76
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77 Setting N to some (fraction) of available markers will speed up calculation - the difference is most painful for
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78 large subject N. The real cost is that every subject must be compared to every other one over all genotypes -
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79 this is an exponential problem on subjects.
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80
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81 If you don't see the genotype data set you want here, it can be imported using one of the methods available from
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82 the Rgenetics Get Data tool.
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83
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84 -----
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85
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86 **Attribution**
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87
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88 Based on an idea from G. Abecasis implemented as GRR (windows only) at http://www.sph.umich.edu/csg/abecasis/GRR/
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89
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90 Ross Lazarus wrote the original pdf writer Galaxy tool version.
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91 John Ziniti added the C and created the slick svg representation.
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92 Copyright Ross Lazarus 2007
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93 Licensed under the terms of the LGPL as documented http://www.gnu.org/licenses/lgpl.html
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94 </help>
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95 </tool>
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