[2] | 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="binary">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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