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1 <tool id="hgv_pass" name="PASS" version="1.0.0">
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2 <description>significant transcription factor binding sites from ChIP data</description>
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3
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4 <command interpreter="bash">
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5 pass_wrapper.sh "$input" "$min_window" "$max_window" "$false_num" "$output"
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6 </command>
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7
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8 <inputs>
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9 <param format="gff" name="input" type="data" label="Dataset"/>
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10 <param name="min_window" label="Smallest window size (by # of probes)" type="integer" value="2" />
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11 <param name="max_window" label="Largest window size (by # of probes)" type="integer" value="6" />
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12 <param name="false_num" label="Expected total number of false positive intervals to be called" type="float" value="5.0" help="N.B.: this is a <em>count</em>, not a rate." />
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13 </inputs>
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14
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15 <outputs>
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16 <data format="tabular" name="output" />
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17 </outputs>
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18
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19 <requirements>
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20 <requirement type="package">pass</requirement>
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21 <requirement type="binary">sed</requirement>
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22 </requirements>
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23
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24 <!-- we need to be able to set the seed for the random number generator
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25 <tests>
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26 <test>
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27 <param name="input" ftype="gff" value="pass_input.gff"/>
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28 <param name="min_window" value="2"/>
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29 <param name="max_window" value="6"/>
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30 <param name="false_num" value="5"/>
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31 <output name="output" file="pass_output.tab"/>
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32 </test>
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33 </tests>
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34 -->
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35
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36 <help>
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37 **Dataset formats**
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38
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39 The input is in GFF_ format, and the output is tabular_.
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40 (`Dataset missing?`_)
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41
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42 .. _GFF: ./static/formatHelp.html#gff
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43 .. _tabular: ./static/formatHelp.html#tab
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44 .. _Dataset missing?: ./static/formatHelp.html
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45
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46 -----
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47
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48 **What it does**
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49
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50 PASS (Poisson Approximation for Statistical Significance) detects
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51 significant transcription factor binding sites in the genome from
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52 ChIP data. This is probably the only peak-calling method that
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53 accurately controls the false-positive rate and FDR in ChIP data,
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54 which is important given the huge discrepancy in results obtained
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55 from different peak-calling algorithms. At the same time, this
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56 method achieves a similar or better power than previous methods.
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57
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58 <!-- we don't have wrapper support for the "prior" file yet
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59 Another unique feature of this method is that it allows varying
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60 thresholds to be used for peak calling at different genomic
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61 locations. For example, if a position lies in an open chromatin
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62 region, is depleted of nucleosome positioning, or a co-binding
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63 protein has been detected within the neighborhood, then the position
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64 is more likely to be bound by the target protein of interest, and
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65 hence a lower threshold will be used to call significant peaks.
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66 As a result, weak but real binding sites can be detected.
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67 -->
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68
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69 -----
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70
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71 **Hints**
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72
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73 - ChIP-Seq data:
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74
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75 If the data is from ChIP-Seq, you need to convert the ChIP-Seq values
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76 into z-scores before using this program. It is also recommended that
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77 you group read counts within a neighborhood together, e.g. in tiled
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78 windows of 30bp. In this way, the ChIP-Seq data will resemble
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79 ChIP-chip data in format.
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80
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81 - Choosing window size options:
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82
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83 The window size is related to the probe tiling density. For example,
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84 if the probes are tiled at every 100bp, then setting the smallest
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85 window = 2 and largest window = 6 is appropriate, because the DNA
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86 fragment size is around 300-500bp.
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87
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88 -----
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89
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90 **Example**
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91
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92 - input file::
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93
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94 chr7 Nimblegen ID 40307603 40307652 1.668944 . . .
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95 chr7 Nimblegen ID 40307703 40307752 0.8041307 . . .
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96 chr7 Nimblegen ID 40307808 40307865 -1.089931 . . .
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97 chr7 Nimblegen ID 40307920 40307969 1.055044 . . .
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98 chr7 Nimblegen ID 40308005 40308068 2.447853 . . .
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99 chr7 Nimblegen ID 40308125 40308174 0.1638694 . . .
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100 chr7 Nimblegen ID 40308223 40308275 -0.04796628 . . .
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101 chr7 Nimblegen ID 40308318 40308367 0.9335709 . . .
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102 chr7 Nimblegen ID 40308526 40308584 0.5143972 . . .
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103 chr7 Nimblegen ID 40308611 40308660 -1.089931 . . .
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104 etc.
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105
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106 In GFF, a value of dot '.' is used to mean "not applicable".
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107
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108 - output file::
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109
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110 ID Chr Start End WinSz PeakValue # of FPs FDR
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111 1 chr7 40310931 40311266 4 1.663446 0.248817 0.248817
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112
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113 -----
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114
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115 **References**
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116
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117 Zhang Y. (2008)
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118 Poisson approximation for significance in genome-wide ChIP-chip tiling arrays.
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119 Bioinformatics. 24(24):2825-31. Epub 2008 Oct 25.
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120
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121 Chen KB, Zhang Y. (2010)
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122 A varying threshold method for ChIP peak calling using multiple sources of information.
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123 Submitted.
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124
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125 </help>
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126 </tool>
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