# HG changeset patch # User grau # Date 1383756843 18000 # Node ID 23b912162e3b463cc92b733797aa3811e0e6d7f9 # Parent 9076b1e4dcbfd39851c3847df6e1f1f14281a8f9 Uploaded diff -r 9076b1e4dcbf -r 23b912162e3b ._DimontWeb.jar Binary file ._DimontWeb.jar has changed diff -r 9076b1e4dcbf -r 23b912162e3b DimontWeb.jar Binary file DimontWeb.jar has changed diff -r 9076b1e4dcbf -r 23b912162e3b DimontWeb.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/DimontWeb.xml Wed Nov 06 11:54:03 2013 -0500 @@ -0,0 +1,137 @@ + +Dimont, a universal tool for de-novo motif discovery (beta). +java -Xms256M -Xmx2G -jar \$JAR_PATH/DimontWeb.jar --run $script_file $summary $summary.id $__new_file_path__ $summary.extra_files_path + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + JAR_PATH + + + +<Dimont_ps_Input_sequences> +<value> +${Dimont_ps_Input_sequences}</value> +<extension> +${Dimont_ps_Input_sequences.ext}</extension> +</Dimont_ps_Input_sequences> + +<Dimont_ps_Position_tag> +${Dimont_ps_Position_tag}</Dimont_ps_Position_tag> + +<Dimont_ps_Value_tag> +${Dimont_ps_Value_tag}</Dimont_ps_Value_tag> + +<Dimont_ps_Standard_deviation> +${Dimont_ps_Standard_deviation}</Dimont_ps_Standard_deviation> + +<Dimont_ps_Weighting_factor> +${Dimont_ps_Weighting_factor}</Dimont_ps_Weighting_factor> + +<Dimont_ps_Starts> +${Dimont_ps_Starts}</Dimont_ps_Starts> + +<Dimont_ps_Initial_motif_width> +${Dimont_ps_Initial_motif_width}</Dimont_ps_Initial_motif_width> + +<Dimont_ps_Markov_order_of_motif_model> +${Dimont_ps_Markov_order_of_motif_model}</Dimont_ps_Markov_order_of_motif_model> + +<Dimont_ps_Markov_order_of_background_model> +${Dimont_ps_Markov_order_of_background_model}</Dimont_ps_Markov_order_of_background_model> + +<Dimont_ps_Equivalent_sample_size> +${Dimont_ps_Equivalent_sample_size}</Dimont_ps_Equivalent_sample_size> + +<Dimont_ps_Delete_BSs_from_profile> +${Dimont_ps_Delete_BSs_from_profile}</Dimont_ps_Delete_BSs_from_profile> + + + + + + + + +**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. + +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. +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. +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. +In case of ChIP data, this anchor position could for instance be the peak summit. +For instance, an annotated FastA file for ChIP-exo data comprising sequences of length 100 centered around the peak summit could look like:: + + > peak: 50; signal: 515 + ggccatgtgtatttttttaaatttccac... + > peak: 50; signal: 199 + GGTCCCCTGGGAGGATGGGGACGTGCTG... + ... + +where the anchor point is given as 50 for the first two sequences, and the confidence amounts to 515 and 199, respectively. +The FastA comment may contain additional annotations of the format ``key1 : value1; key2: value2;...``. +We also provide an example_ input file and a Perl script_ for preparing data in the format required by Dimont. + +Accordingly, you would need to set the parameter "Position tag" to ``peak`` and the parameter "Value tag" to ``signal`` for the input file. + +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. +However, you may want adjust these parameters to meet your prior information. + +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. +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``. + +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. +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``. + +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. +For PBM data, containing a large number of unspecific probes, this parameter should be set to a lower value, e.g. ``0.01``. + +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. + +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. + +You can also install this web-application within your local Galaxy server. Instructions can be found at the Dimont_ page of Jstacs. +There you can also download a command line version of Dimont. + +If you experience problems using Dimont, please contact_ us. + +.. _example: http://www.jstacs.de/downloads/dimont-example.fa +.. _script: http://www.jstacs.de/index.php/Dimont#Data_preparation +.. _Dimont: http://jstacs.de/index.php/Dimont +.. _contact: mailto:grau@informatik.uni-halle.de + + + diff -r 9076b1e4dcbf -r 23b912162e3b galaxy/DimontWeb.jar Binary file galaxy/DimontWeb.jar has changed diff -r 9076b1e4dcbf -r 23b912162e3b galaxy/DimontWeb.xml --- a/galaxy/DimontWeb.xml Wed Nov 06 11:52:25 2013 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,137 +0,0 @@ - -Dimont, a universal tool for de-novo motif discovery (beta). -java -Xms256M -Xmx2G -jar \$JAR_PATH/DimontWeb.jar --run $script_file $summary $summary.id $__new_file_path__ $summary.extra_files_path - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - JAR_PATH - - - -<Dimont_ps_Input_sequences> -<value> -${Dimont_ps_Input_sequences}</value> -<extension> -${Dimont_ps_Input_sequences.ext}</extension> -</Dimont_ps_Input_sequences> - -<Dimont_ps_Position_tag> -${Dimont_ps_Position_tag}</Dimont_ps_Position_tag> - -<Dimont_ps_Value_tag> -${Dimont_ps_Value_tag}</Dimont_ps_Value_tag> - -<Dimont_ps_Standard_deviation> -${Dimont_ps_Standard_deviation}</Dimont_ps_Standard_deviation> - -<Dimont_ps_Weighting_factor> -${Dimont_ps_Weighting_factor}</Dimont_ps_Weighting_factor> - -<Dimont_ps_Starts> -${Dimont_ps_Starts}</Dimont_ps_Starts> - -<Dimont_ps_Initial_motif_width> -${Dimont_ps_Initial_motif_width}</Dimont_ps_Initial_motif_width> - -<Dimont_ps_Markov_order_of_motif_model> -${Dimont_ps_Markov_order_of_motif_model}</Dimont_ps_Markov_order_of_motif_model> - -<Dimont_ps_Markov_order_of_background_model> -${Dimont_ps_Markov_order_of_background_model}</Dimont_ps_Markov_order_of_background_model> - -<Dimont_ps_Equivalent_sample_size> -${Dimont_ps_Equivalent_sample_size}</Dimont_ps_Equivalent_sample_size> - -<Dimont_ps_Delete_BSs_from_profile> -${Dimont_ps_Delete_BSs_from_profile}</Dimont_ps_Delete_BSs_from_profile> - - - - - - - - -**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. - -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. -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. -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. -In case of ChIP data, this anchor position could for instance be the peak summit. -For instance, an annotated FastA file for ChIP-exo data comprising sequences of length 100 centered around the peak summit could look like:: - - > peak: 50; signal: 515 - ggccatgtgtatttttttaaatttccac... - > peak: 50; signal: 199 - GGTCCCCTGGGAGGATGGGGACGTGCTG... - ... - -where the anchor point is given as 50 for the first two sequences, and the confidence amounts to 515 and 199, respectively. -The FastA comment may contain additional annotations of the format ``key1 : value1; key2: value2;...``. -We also provide an example_ input file and a Perl script_ for preparing data in the format required by Dimont. - -Accordingly, you would need to set the parameter "Position tag" to ``peak`` and the parameter "Value tag" to ``signal`` for the input file. - -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. -However, you may want adjust these parameters to meet your prior information. - -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. -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``. - -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. -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``. - -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. -For PBM data, containing a large number of unspecific probes, this parameter should be set to a lower value, e.g. ``0.01``. - -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. - -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. - -You can also install this web-application within your local Galaxy server. Instructions can be found at the Dimont_ page of Jstacs. -There you can also download a command line version of Dimont. - -If you experience problems using Dimont, please contact_ us. - -.. _example: http://www.jstacs.de/downloads/dimont-example.fa -.. _script: http://www.jstacs.de/index.php/Dimont#Data_preparation -.. _Dimont: http://jstacs.de/index.php/Dimont -.. _contact: mailto:grau@informatik.uni-halle.de - - - diff -r 9076b1e4dcbf -r 23b912162e3b galaxy/tool_dependencies.xml --- a/galaxy/tool_dependencies.xml Wed Nov 06 11:52:25 2013 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,6 +0,0 @@ - - - - $REPOSITORY_INSTALL_DIR - - \ No newline at end of file diff -r 9076b1e4dcbf -r 23b912162e3b tool_dependencies.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tool_dependencies.xml Wed Nov 06 11:54:03 2013 -0500 @@ -0,0 +1,6 @@ + + + + $REPOSITORY_INSTALL_DIR + + \ No newline at end of file