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comparison tools/human_genome_variation/beam.xml @ 0:9071e359b9a3

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author xuebing
date Fri, 09 Mar 2012 19:37:19 -0500
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1 <tool id="hgv_beam" name="BEAM" version="1.0.0">
2 <description>significant single- and multi-locus SNP associations in case-control studies</description>
3
4 <command interpreter="bash">
5 BEAM2_wrapper.sh map=${input.extra_files_path}/${input.metadata.base_name}.map ped=${input.extra_files_path}/${input.metadata.base_name}.ped $burnin $mcmc $pvalue significance=$significance posterior=$posterior
6 </command>
7
8 <inputs>
9 <param format="lped" name="input" type="data" label="Dataset"/>
10 <param name="burnin" label="Number of MCMC burn-in steps" type="integer" value="200" />
11 <param name="mcmc" label="Number of MCMC sampling steps" type="integer" value="200" />
12 <param name="pvalue" label="Significance cutoff (after Bonferroni adjustment)" type="float" value="0.05" />
13 </inputs>
14
15 <outputs>
16 <data format="tabular" name="significance" />
17 <data format="tabular" name="posterior" />
18 </outputs>
19
20 <requirements>
21 <requirement type="package">beam</requirement>
22 <requirement type="binary">mv</requirement>
23 <requirement type="binary">rm</requirement>
24 </requirements>
25
26 <!-- broken. will be fixed soon.
27 <tests>
28 <test>
29 <param name='input' value='gpass_and_beam_input' ftype='lped' >
30 <metadata name='base_name' value='gpass_and_beam_input' />
31 <composite_data value='gpass_and_beam_input.ped' />
32 <composite_data value='gpass_and_beam_input.map' />
33 <edit_attributes type='name' value='gpass_and_beam_input' />
34 </param>
35 <param name="burnin" value="200"/>
36 <param name="mcmc" value="200"/>
37 <param name="pvalue" value="0.05"/>
38 <output name="significance" file="beam_output1.tab"/>
39 <output name="posterior" file="beam_output2.tab"/>
40 </test>
41 </tests>
42 -->
43
44 <help>
45 .. class:: infomark
46
47 This tool can take a long time to run, depending on the number of SNPs, the
48 sample size, and the number of MCMC steps specified. If you have hundreds
49 of thousands of SNPs, it may take over a day. The main tasks that slow down
50 this tool are searching for interactions and dynamically partitioning the
51 SNPs into blocks. Optimization is certainly possible, but hasn't been done
52 yet. **If your only interest is to detect SNPs with primary effects (i.e.,
53 single-SNP associations), please use the GPASS tool instead.**
54
55 -----
56
57 **Dataset formats**
58
59 The input dataset must be in lped_ format. The output datasets are both tabular_.
60 (`Dataset missing?`_)
61
62 .. _lped: ./static/formatHelp.html#lped
63 .. _tabular: ./static/formatHelp.html#tabular
64 .. _Dataset missing?: ./static/formatHelp.html
65
66 -----
67
68 **What it does**
69
70 BEAM (Bayesian Epistasis Association Mapping) uses a Markov Chain Monte Carlo (MCMC) method to infer SNP block structures and detect both single-marker
71 and interaction effects from case-control SNP data.
72 This tool also partitions SNPs into blocks based on linkage disequilibrium (LD). The method utilized is Bayesian, so the outputs are posterior probabilities of association, along with block partitions. An advantage of this method is that it provides uncertainty measures for the associations and block partitions, and it scales well from small to large sample sizes. It is powerful in detecting gene-gene interactions, although slow for large datasets.
73
74 -----
75
76 **Example**
77
78 - input map file::
79
80 1 rs0 0 738547
81 1 rs1 0 5597094
82 1 rs2 0 9424115
83 etc.
84
85 - input ped file::
86
87 1 1 0 0 1 1 G G A A A A A A A A A G A A G G G G A A G G G G G G A A A A A G A A G G A G A G A A G G A A G G A A G G A G A A G G A A G G A A A G A G G G A G G G G G A A A G A A G G G G G G G G A G A A A A A A A A
88 1 1 0 0 1 1 G G A G G G A A A A A G A A G G G G G G A A G G A G A G G G G G A G G G A G A A G G A G G G A A G G G G A G A G G G A G A A A A G G G G A G A G G G A G A A A A A G G G A G G G A G G G G G A A G G A G
89 etc.
90
91 - first output file, significance.txt::
92
93 ID chr position results
94 rs0 chr1 738547 10 20 score= 45.101397 , df= 8 , p= 0.000431 , N=1225
95
96 - second output file, posterior.txt::
97
98 id: chr position marginal + interaction = total posterior
99 0: 1 738547 0.0000 + 0.0000 = 0.0000
100 1: 1 5597094 0.0000 + 0.0000 = 0.0000
101 2: 1 9424115 0.0000 + 0.0000 = 0.0000
102 3: 1 13879818 0.0000 + 0.0000 = 0.0000
103 4: 1 13934751 0.0000 + 0.0000 = 0.0000
104 5: 1 16803491 0.0000 + 0.0000 = 0.0000
105 6: 1 17236854 0.0000 + 0.0000 = 0.0000
106 7: 1 18445387 0.0000 + 0.0000 = 0.0000
107 8: 1 21222571 0.0000 + 0.0000 = 0.0000
108 etc.
109
110 id: chr position block_boundary | allele counts in cases and controls
111 0: 1 738547 1.000 | 156 93 251 | 169 83 248
112 1: 1 5597094 1.000 | 323 19 158 | 328 16 156
113 2: 1 9424115 1.000 | 366 6 128 | 369 11 120
114 3: 1 13879818 1.000 | 252 31 217 | 278 32 190
115 4: 1 13934751 1.000 | 246 64 190 | 224 58 218
116 5: 1 16803491 1.000 | 91 160 249 | 91 174 235
117 6: 1 17236854 1.000 | 252 43 205 | 249 44 207
118 7: 1 18445387 1.000 | 205 66 229 | 217 56 227
119 8: 1 21222571 1.000 | 353 9 138 | 352 8 140
120 etc.
121
122 The "id" field is an internally used index.
123
124 -----
125
126 **References**
127
128 Zhang Y, Liu JS. (2007)
129 Bayesian inference of epistatic interactions in case-control studies.
130 Nat Genet. 39(9):1167-73. Epub 2007 Aug 26.
131
132 Zhang Y, Zhang J, Liu JS. (2010)
133 Block-based bayesian epistasis association mapping with application to WTCCC type 1 diabetes data.
134 Submitted.
135
136 </help>
137 </tool>