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<title>ComSeq - Broad</title>
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<a href="/index.html">Home</a> &gt; ComSeq
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<h1>ComSeq </h1>
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<H2>Overview </H2>
<p>
ComSeq is a set of utilities used for simulating and analyzing
compressed-sensing pooling design experiments for
next-generation sequencing projects. The aim is to identify
novel rare alleles, or detect individuals who are carriers of
known SNPs in a large population. The package enables an
in-silico simulation of a sequencing pooling experiments,
including modeling of sequencing error parameters. The package
also enables reconstructing genotypes of individuals profiled
in real experiments. Detailed instructions and usage examples
are given in the <a href="README.txt"> README</a> file.
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<P align="center"><IMG src="comseq.jpg" alt="SiPhy Data" width="400" height="400" border="0"></P>
<H2>About </H2>
<p>
ComSeq is a collaboration between the Broad Institute, the <A &nbsp; href="http://www.openu.ac.il/home/shental/">
Open University of Israel </A>, and the Weizmann Institute of Science.
If you use the package please cite the following paper:
<i> </i> <br>
<A &nbsp; href="http://nar.oxfordjournals.org/cgi/content/abstract/gkq675"> Identification of Rare-Alleles And Their Carriers Using Compressed Se(que)nsing </A> <br>
<b> N. Shental, A. Amir and O. Zuk, <i> Nucleic Acid Research (2010)
</i> </b>
</p>
<H2>ComSeq Installation </H2>
<p>
ComSeq includes a set of Matlab functions which can be individually
used. You can download the whole package, or individual
functions below. To install the package simply download the
file comseq.tgz to a directory of your choice, extract it, and
add to your Matlab path. You can also obtain a stand-alone
executible for various platforms upon request.
The package uses the GPSR algorithm. We thank <A &nbsp;
href="http://www.lx.it.pt/~mtf/"> Mário Figueiredo </A> for allowing us to include it in this package.
</p>
<H2>Contact </H2>
<p>
For any questions or comments, please contact <A &nbsp; href="mailto:shental@openu.ac.il">
Noam Shental </A> or <A &nbsp; href="mailto:orzuk@broadinstitute.org">
Or Zuk </A>
</p>
<TABLE summary="M-file index" border="0" cellpadding="0" cellspacing="0">
<TBODY><TR>
<tr>
<td><a href="applyGPSR.m" name="applyGPSR" id="applyGPSR">applyGPSR.m</a></td>
<td>&nbsp;-&nbsp;</td>
<td>Runs the GPSR algorithm - using the default parameters.</td>
</tr>
<tr>
<td><a href="displaySimulationParameters.m" name="displaySimulationParameters" id="displaySimulationParameters">displaySimulationParameters.m</a></td>
<td>&nbsp;-&nbsp;</td>
<td>Prints the parameters used for simulations</td>
</tr>
<td><a href="GPSR_BB.m" name="GPSR_BB" id="GPSR_BB">GPSR_BB.m</a></td>
<td>&nbsp;-&nbsp;</td>
<td>The GPSR solver.</td>
</tr>
<tr>
<td><a href="modifySolution.m" name="modifySolution" id="modifySolution">modifySolution.m</a></td>
<td>&nbsp;-&nbsp;</td>
<td>Discretize solution: find rounded solution with minimal squared error with respect to measurements</td>
</tr>
<tr>
<td><a href="normalizeRows.m" name="normalizeRows" id="normalizeRows">normalizeRows.m</a></td>
<td>&nbsp;-&nbsp;</td>
<td>Normalize matrix rows </td>
</tr>
<tr>
<td><a href="optimize_experimental_setting.m" name="optimize_experimental_setting" id="optimize_experimental_setting">optimize_experimental_setting.m</a></td>
<td>&nbsp;-&nbsp;</td>
<td> Computes the maximal sample size we can reconstruct for a given number of lanes, or the minimal number of lanes sufficient to reconstruct a certain number of individuals. see optimize_experiment_settings_example.m for an example</td>
</tr>
<tr>
<td><a href="optimize_experiment_settings_example.m" name="optimize_experiment_settings_example" id="optimize_experiment_settings_example">optimize_experiment_settings_example.m</a></td>
<td>&nbsp;-&nbsp;</td>
<td> Example of using the function optimize_experiment_setting.m for computing the maximal sample size for a given number of lanes.</td>
</tr>
<tr>
<td><a href="save_experiment_settings.m" name="save_experiment_settings" id="save_experiment_settings">save_experiment_settings.m</a></td>
<td>&nbsp;-&nbsp;</td>
<td> Save experimental parameters in a text file (including the pooling matrix) </td>
</tr>
<tr>
<td><a href="pooling_example.m" name="pooling_example" id="pooling_example">pooling_example.m</a></td>
<td>&nbsp;-&nbsp;</td>
<td>Simulate a single case of carriers reconstruction</td>
</tr>
<tr>
<td><a href="simulateCSseq.m" name="simulateCSseq" id="simulateCSseq">simulateCSseq.m</a></td>
<td>&nbsp;-&nbsp;</td>
<td>Simulate and reconstruct a genotype vector in a specific setting.</td>
</tr>
<td><a href="reconstruct_experiment_example.m" name="reconstruct_experiment_example" id="reconstruct_experiment_example">reconstruct_experiment_example.m</a></td>
<td>&nbsp;-&nbsp;</td>
<td>Reconstruct a genotype vector based on a sequencer's reads. </td>
</tr>
<tr>
<td><a href="README.txt" name="README.txt" id="README.txt"><font color="003300">README.txt</a></td>
<td>&nbsp;-&nbsp;</td>
<td> Documentation on the ComSeq package </td>
</tr>
<tr>
<td><a href="comseq.tgz" name="comseq" id="comseq"><STRONG><i>comseq.tgz</i></STRONG></a></td>
<td>&nbsp;-&nbsp;</td>
<td><b><i>Download the whole package. </i></b></td>
</tr>
<TR>
</TR></TBODY></TABLE><!-- End m-file index -->
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