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This site contains projects that use Jstacs.
This site contains projects that use Jstacs.
 
* [[MotifAdjuster]]
= MotifAdjuster =
* [[DiPoMM]]
 
by Jens Keilwagen, Jan Baumbach, Thomas A. Kohl and Ivo Grosse.
 
== Description ==
Valuable binding site annotation data are stored in databases. However, several types of errors can, and do, occur in the process of manually incorporating annotation data from scientific literature into these databases. Here, we introduce MotifAdjuster, a software that helps to detect these errors, and we demonstrate its efficacy on public data sets.
 
== Paper ==
The paper [http://genomebiology.com/2009/10/5/R46/abstract '''''MotifAdjuster: a tool for computational reassessment of transcription factor binding site annotations'''''] has been published in [http://genomebiology.com/ Genome Biology].
 
== Download ==
MotifAdjuster can be downloaded [http://www.jstacs.de/download.php?which=MotifAdjuster here].
 
== Start instructions ==
 
If you have unzipped the archive, you can start the MotifAdjuster by invoking
 
<p><code>java -cp ./:./jstacs-1.2.2.jar:./numericalMethods.jar MotifAdjuster <font color="green">&lt;file&gt; &lt;ignoreChar&gt; &lt;length&gt; &lt;fgOrder&gt; &lt;fgEss&gt; &lt;bothStrands&gt; &lt;output&gt; &lt;sigma&gt; &lt;p(no motif)&gt;</font></code></p>
 
In Windows, you have to use &quot;;&quot; instead of &quot;:&quot; in the class path.
 
The arguments have the following meaning
 
<table border=0 cellpadding=10 align="center">
<tr>
<td>name</td>
<td>comment</td>
<td>type</td>
</tr>
<tr><td colspan=3><hr></td></tr>
<tr>
<td><font color="green">file</font></td>
<td>the location of the data set</td>
<td>String</td>
</tr>
<tr>
<td><font color="green">ignoreChar</font></td>
<td>char for comment lines (e.g. for a FastA-file '&gt;')</td>
<td>char</td>
</tr>
<tr>
<td><font color="green">length</font></td>
<td>the motif length</td>
<td>int</td>
</tr>
<tr>
<td><font color="green">fgOrder</font></td>
<td>the order of the inhomogeneous Markov model that is uses for the motif; 0 yields in a PWM</td>
<td>byte</td>
</tr>
<tr>
<td><font color="green">ess</font></td>
<td>the equivalent sample size that is used for the mixture model</td>
<td>double &gt;= 0</td>
</tr>
<tr>
<td><font color="green">bothStrands</font></td>
<td>use both strands</td>
<td>boolean</td>
</tr>
<tr>
<td><font color="green">output</font></td>
<td>output of the EM</td>
<td>boolean</td></tr>
<tr>
<td><font color="green">sigma</font></td>
<td>the sigma of the truncated discrete Gaussian distribution</td>
<td>double&gt;0</td>
</tr>
<tr>
<td><font color="green">p(no motif)</font></td>
<td>the probability for finding no motif</td>
<td>0&lt;=double&lt;1</td>
</tr>
</table>
 
 
== Remark ==
The authors of [http://favorov.imb.ac.ru/SeSiMCMC/ SeSiMCMC] informed us that the performance of SeSiMCMC could be improved by using the latest version of SeSiMCMC (version 4.31 from 2009-04-23) and by using the advanced option ''Background model is common (static)'' set to the  value ''TRUE''. Hence, we provide an updated version of [http://www.jstacs.de/downloads/comparison_new.xls additional file 1].
 
= DiPoMM =
 
by Jens Keilwagen, Jan Grau, Stefan Posch, Marc Strickert and Ivo Grosse.
 
== Description ==
Transcription factors are one main component of gene regulation, as they activate or repress gene expression by binding to their binding sites. The de-novo discovery of transcription factor binding sites in the promoters of target genes is a challenging problem in bioinformatics, which has not yet been solved satisfactorily.
We present DiPoMM, a discriminative de-novo motif discovery tool that models existing positional preferences of binding sites and adjusts the length of the motif in the learning process.
 
== Paper ==
The paper '''''DiPoMM: Discriminative de-novo motif discovery utilizing positional preference''''' has been submitted to [http://www.iscb.org/ismbeccb2009/ ISMB 2009].
 
== Download ==
DiPoMM can be downloaded [http://www.jstacs.de/download.php?which=DiPoMM here].
 
== Start instructions ==
Once you have unzipped the archive, you can start DiPoMM e.g. by invoking
 
<code>java -cp .:jstacs-1.2.2.jar:lib/numericalMethods.jar:lib/bytecode.jar:lib/biojava-live.jar projects.DiPoMM home=path/to/data/directory/ fg=fgfile.txt bg=bgfile.txt init=best-random=100 p-val=1E-4</code>
 
to search for motifs that are over-represented in <code>path/to/data/directory/fgfile.txt</code> but not in <code>path/to/data/directory/bgfile.txt</code>, initialize DiPoMM with the best from 100 randomly drawn starting values, and search for motif occurrences with a p-value less than <code>1E-4</code>.
 
Under Windows, you must use &quot;;&quot; instead of &quot;:&quot; in the class path.
 
The arguments have the following meaning
<table border=0 cellpadding=10 align="center">
<tr>
<td>name</td>
<td>comment</td>
<td>type</td>
</tr>
<tr><td colspan=3><hr></td></tr>
<tr>
<td><font color="green">home</font></td>
<td>the path to the data directory, default = ./</td>
<td>String</td>
</tr>
<tr>
<td><font color="green">ignore</font></td>
<td>the char that is used to mask comment lines in data files, e.g., '>' in a FASTA-file, default = ></td>
<td>Character</td>
</tr>
<tr>
<td><font color="green">fg</font></td>
<td>the file name of the foreground data file (the file containing sequences which are expected to contain binding sites of a common motif)</td>
<td>String</td>
</tr>
<tr>
<td><font color="green">bg</font></td>
<td>the file name of the background data file</td>
<td>String</td>
</tr>
<tr>
<td><font color="green">length</font></td>
<td>the motif length that is used at the beginning, valid range = [1, 50], default = 15</td>
<td>Integer</td>
</tr>
<tr>
<td><font color="green">flankOrder</font></td>
<td>The Markov order of the model for the flanking sequence and the background sequence, valid range = [0, 5], default = 0</td>
<td>Integer</td>
</tr>
<tr>
<td><font color="green">motifOrder</font></td>
<td>The Markov order of the motif model, valid range = [0, 3], default = 0</td>
<td>Integer</td>
</tr>
<tr>
<td><font color="green">bothStrands</font></td>
<td>a switch whether to use both strands or not, default = true</td>
<td>Boolean</td>
</tr>
<tr>
<td><font color="green">init</font></td>
<td>the method that is used for initialization, one of 'best-random=<number>', 'enum=<length>', and 'specific=<sequence or file of sequence>'</td>
<td>String=[Integer | String]</td>
</tr>
<tr>
<td><font color="green">xml</font></td>
<td>the file name of the xml file the classifier is written to, default = ./classifier.xml</td>
<td>String</td>
</tr>
<tr>
<td><font color="green">adjust</font></td>
<td>a switch whether to adjust the motif length, i.e., either to shrink or expand, default = true</td>
<td>Boolean</td>
</tr>
<tr>
<td><font color="green">p-val</font></td>
<td>a p-value for predicting binding sites, valid range = [0.0, 1.0], OPTIONAL</td>
<td>Double</td>
</tr>
</table>

Revision as of 18:25, 19 November 2009

This site contains projects that use Jstacs.