Mercurial > repos > grau > dimont
comparison DimontWeb.xml @ 1:93f3b1563fa6 draft
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| author | grau |
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| date | Wed, 06 Nov 2013 11:04:57 -0500 |
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| 0:38383332b553 | 1:93f3b1563fa6 |
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| 1 <tool id="Dimont" name="Dimont" version="0.1" force_history_refresh="true"> | |
| 2 <description>Dimont, a universal tool for de-novo motif discovery (beta).</description> | |
| 3 <command>java -Xms256M -Xmx2G -jar /Users/dev/Desktop/ChIP-seq/galaxy/DimontWeb.jar --run $script_file $summary $summary.id $__new_file_path__ $summary.extra_files_path</command> | |
| 4 <inputs> | |
| 5 <param type="text" size="40" name="Dimont_jobname" label="Job name" value="" optional="true" help="Please enter a name for your job that should be used in the history (optional)"> | |
| 6 </param> | |
| 7 <param type="data" format="fasta" name="Dimont_ps_Input_sequences" label="<hr />Input sequences" help="The input sequences for de-novo motif discovery (can be uploaded using "GetData" -> "Upload File"), annotated FastA format. The required format is described in the help section." value="" optional="false"> | |
| 8 </param> | |
| 9 | |
| 10 <param type="text" size="40" name="Dimont_ps_Position_tag" label="Position tag" help="The tag for the position information in the FastA-annotation of the input file" value="" optional="false"> | |
| 11 </param> | |
| 12 | |
| 13 <param type="text" size="40" name="Dimont_ps_Value_tag" label="Value tag" help="The tag for the value information in the FastA-annotation of the input file" value="" optional="false"> | |
| 14 </param> | |
| 15 | |
| 16 <param type="float" name="Dimont_ps_Standard_deviation" label="Standard deviation" help="The standard deviation of the position distribution centered at the position specified by the position tag" value="75.0" optional="false"> | |
| 17 <validator type="in_range" min="1.0" max="10000.0" message="Value is not in the specified range [1.0, 10000.0]."/></param> | |
| 18 | |
| 19 <param type="text" size="40" name="Dimont_ps_Weighting_factor" label="Weighting factor" help="The value for weighting the data; either a value between 0 and 1, or a description relative to the standard deviation (e.g. +4sd)" value="0.2" optional="false"> | |
| 20 </param> | |
| 21 | |
| 22 <param type="integer" name="Dimont_ps_Starts" label="<hr />Starts" help="The number of pre-optimization runs." value="20" optional="false"> | |
| 23 <validator type="in_range" min="1" max="100" message="Value is not in the specified range [1, 100]."/></param> | |
| 24 | |
| 25 <param type="integer" name="Dimont_ps_Initial_motif_width" label="<hr />Initial motif width" help="The motif width that is used initially, may be adjusted during optimization." value="15" optional="false"> | |
| 26 <validator type="in_range" min="1" max="50" message="Value is not in the specified range [1, 50]."/></param> | |
| 27 | |
| 28 <param type="integer" name="Dimont_ps_Markov_order_of_motif_model" label="Markov order of motif model" help="The Markov order of the model for the motif." value="0" optional="false"> | |
| 29 <validator type="in_range" min="0" max="3" message="Value is not in the specified range [0, 3]."/></param> | |
| 30 | |
| 31 <param type="integer" name="Dimont_ps_Markov_order_of_background_model" label="Markov order of background model" help="The Markov order of the model for the background sequence and the background sequence, -1 defines uniform distribution." value="-1" optional="false"> | |
| 32 <validator type="in_range" min="-1" max="5" message="Value is not in the specified range [-1, 5]."/></param> | |
| 33 | |
| 34 <param type="float" name="Dimont_ps_Equivalent_sample_size" label="<hr />Equivalent sample size" help="Reflects the strength of the prior on the model parameters." value="4.0" optional="false"> | |
| 35 <validator type="in_range" min="0.0" max="Infinity" message="Value is not in the specified range [0.0, Infinity]."/></param> | |
| 36 | |
| 37 <param type="boolean" name="Dimont_ps_Delete_BSs_from_profile" label="Delete BSs from profile" help="A switch for deleting binding site positions of discovered motifs from the profile before searching for futher motifs." checked="True" optional="false"> | |
| 38 </param> | |
| 39 | |
| 40 </inputs> | |
| 41 <configfiles> | |
| 42 <configfile name="script_file"> | |
| 43 <Dimont_ps_Input_sequences> | |
| 44 <value> | |
| 45 ${Dimont_ps_Input_sequences}</value> | |
| 46 <extension> | |
| 47 ${Dimont_ps_Input_sequences.ext}</extension> | |
| 48 </Dimont_ps_Input_sequences> | |
| 49 | |
| 50 <Dimont_ps_Position_tag> | |
| 51 ${Dimont_ps_Position_tag}</Dimont_ps_Position_tag> | |
| 52 | |
| 53 <Dimont_ps_Value_tag> | |
| 54 ${Dimont_ps_Value_tag}</Dimont_ps_Value_tag> | |
| 55 | |
| 56 <Dimont_ps_Standard_deviation> | |
| 57 ${Dimont_ps_Standard_deviation}</Dimont_ps_Standard_deviation> | |
| 58 | |
| 59 <Dimont_ps_Weighting_factor> | |
| 60 ${Dimont_ps_Weighting_factor}</Dimont_ps_Weighting_factor> | |
| 61 | |
| 62 <Dimont_ps_Starts> | |
| 63 ${Dimont_ps_Starts}</Dimont_ps_Starts> | |
| 64 | |
| 65 <Dimont_ps_Initial_motif_width> | |
| 66 ${Dimont_ps_Initial_motif_width}</Dimont_ps_Initial_motif_width> | |
| 67 | |
| 68 <Dimont_ps_Markov_order_of_motif_model> | |
| 69 ${Dimont_ps_Markov_order_of_motif_model}</Dimont_ps_Markov_order_of_motif_model> | |
| 70 | |
| 71 <Dimont_ps_Markov_order_of_background_model> | |
| 72 ${Dimont_ps_Markov_order_of_background_model}</Dimont_ps_Markov_order_of_background_model> | |
| 73 | |
| 74 <Dimont_ps_Equivalent_sample_size> | |
| 75 ${Dimont_ps_Equivalent_sample_size}</Dimont_ps_Equivalent_sample_size> | |
| 76 | |
| 77 <Dimont_ps_Delete_BSs_from_profile> | |
| 78 ${Dimont_ps_Delete_BSs_from_profile}</Dimont_ps_Delete_BSs_from_profile> | |
| 79 | |
| 80 </configfile> | |
| 81 </configfiles> | |
| 82 <outputs> | |
| 83 <data format="html" name="summary" label="#if str($Dimont_jobname) == '' then $tool.name + ' on ' + $on_string else $Dimont_jobname#"> | |
| 84 </data> | |
| 85 </outputs> | |
| 86 <help> | |
| 87 **Dimont** is a universal tool for de-novo motif discovery. Dimont has successfully been applied to ChIP-seq, ChIP-exo and protein-binding microarray (PBM) data. | |
| 88 | |
| 89 Input sequences must be supplied in an annotated FastA format as a file uploaded by the "Upload File" task in section "Get Data" of Galaxy. | |
| 90 In the annotation of each sequence, you need to provide a value that reflects the confidence that this sequence is bound by the factor of interest. | |
| 91 Such confidences may be peak statistics (e.g., number of fragments under a peak) for ChIP data or signal intensities for PBM data. In addition, you need to provide an anchor position within the sequence. | |
| 92 In case of ChIP data, this anchor position could for instance be the peak summit. | |
| 93 For instance, an annotated FastA file for ChIP-exo data comprising sequences of length 100 centered around the peak summit could look like:: | |
| 94 | |
| 95 > peak: 50; signal: 515 | |
| 96 ggccatgtgtatttttttaaatttccac... | |
| 97 > peak: 50; signal: 199 | |
| 98 GGTCCCCTGGGAGGATGGGGACGTGCTG... | |
| 99 ... | |
| 100 | |
| 101 where the anchor point is given as 50 for the first two sequences, and the confidence amounts to 515 and 199, respectively. | |
| 102 The FastA comment may contain additional annotations of the format ``key1 : value1; key2: value2;...``. | |
| 103 We also provide an example_ input file and a Perl script_ for preparing data in the format required by Dimont. | |
| 104 | |
| 105 Accordingly, you would need to set the parameter "Position tag" to ``peak`` and the parameter "Value tag" to ``signal`` for the input file. | |
| 106 | |
| 107 For the standard deviation of the position prior, the initial motif length and the number of pre-optimization runs, we provide default values that worked well in our studies on ChIP and PBM data. | |
| 108 However, you may want adjust these parameters to meet your prior information. | |
| 109 | |
| 110 The parameter "Markov order of the motif model" sets the order of the inhomogeneous Markov model used for modeling the motif. If this parameter is set to ``0``, you obtain a position weight matrix (PWM) model. | |
| 111 If it is set to ``1``, you obtain a weight array matrix (WAM) model. You can set the order of the motif model to at most ``3``. | |
| 112 | |
| 113 The parameter "Markov order of the background model" sets the order of the homogeneous Markov model used for modeling positions not covered by a motif. | |
| 114 If this parameter is set to ``-1``, you obtain a uniform distribution, which worked well for ChIP data. For PBM data, orders of up to ``4`` resulted in an increased prediction performance in our case studies. The maximum allowed value is ``5``. | |
| 115 | |
| 116 The parameter "Weighting factor" defines the proportion of sequences that you expect to be bound by the targeted factor with high confidence. For ChIP data, the default value of ``0.2`` typically works well. | |
| 117 For PBM data, containing a large number of unspecific probes, this parameter should be set to a lower value, e.g. ``0.01``. | |
| 118 | |
| 119 The "Equivalent sample size" reflects the strength of the influence of the prior on the model parameters, where higher values smooth out the parameters to a greater extent. | |
| 120 | |
| 121 The parameter "Delete BSs from profile" defines if BSs of already discovered motifs should be deleted, i.e., "blanked out", from the sequence before searching for futher motifs. | |
| 122 | |
| 123 You can also install this web-application within your local Galaxy server. Instructions can be found at the Dimont_ page of Jstacs. | |
| 124 There you can also download a command line version of Dimont. | |
| 125 | |
| 126 If you experience problems using Dimont, please contact_ us. | |
| 127 | |
| 128 .. _example: http://www.jstacs.de/downloads/dimont-example.fa | |
| 129 .. _script: http://www.jstacs.de/index.php/Dimont#Data_preparation | |
| 130 .. _Dimont: http://jstacs.de/index.php/Dimont | |
| 131 .. _contact: mailto:grau@informatik.uni-halle.de | |
| 132 </help> | |
| 133 </tool> | |
| 134 |
