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/tools/rgenetics/rgGRR.xml

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