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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Recent changes to Examples</title><link>https://sourceforge.net/p/dspchip/wiki/Examples/</link><description>Recent changes to Examples</description><atom:link href="https://sourceforge.net/p/dspchip/wiki/Examples/feed" rel="self"/><language>en</language><lastBuildDate>Thu, 19 Dec 2013 10:10:33 -0000</lastBuildDate><atom:link href="https://sourceforge.net/p/dspchip/wiki/Examples/feed" rel="self" type="application/rss+xml"/><item><title>Examples modified by Anonymous</title><link>https://sourceforge.net/p/dspchip/wiki/Examples/</link><description>&lt;div class="markdown_content"&gt;&lt;h1 id="typical-or-not-usage-scenario"&gt;Typical (or not) usage scenario&lt;/h1&gt;
&lt;p&gt;&lt;code&gt;dspchip&lt;/code&gt; has been implemented to perform common DSP operations on quantitative genomic features. Such data include, but are not limited to, ChIP-seq (or ChIP-chip) data. When we started writing &lt;code&gt;dspchip&lt;/code&gt;, we had to face histone modifications, which spread kilobases and, most important, are poorly detected by available peak-finding software (we really like &lt;a class="" href="http://vancouvershortr.sourceforge.net/"&gt;FindPeaks4&lt;/a&gt;, &lt;a class="" href="http://liulab.dfci.harvard.edu/MACS/" rel="nofollow"&gt;MACS&lt;/a&gt; and &lt;a class="" href="http://home.gwu.edu/~wpeng/Software.htm" rel="nofollow"&gt;SICER&lt;/a&gt;). &lt;/p&gt;
&lt;h2 id="example-1-calculate-log2ratio-between-two-datasets"&gt;Example 1: calculate log2ratio between two datasets&lt;/h2&gt;
&lt;div class="codehilite"&gt;&lt;pre&gt;&lt;span class="err"&gt;$&lt;/span&gt; &lt;span class="n"&gt;dspchip&lt;/span&gt; &lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="n"&gt;i&lt;/span&gt; &lt;span class="n"&gt;chip&lt;/span&gt;&lt;span class="p"&gt;.&lt;/span&gt;&lt;span class="n"&gt;bam&lt;/span&gt; &lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="n"&gt;c&lt;/span&gt; &lt;span class="n"&gt;mock&lt;/span&gt;&lt;span class="p"&gt;.&lt;/span&gt;&lt;span class="n"&gt;bam&lt;/span&gt; &lt;span class="o"&gt;--&lt;/span&gt;&lt;span class="n"&gt;pl&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;NL&lt;/span&gt; &lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="n"&gt;s&lt;/span&gt; &lt;span class="mi"&gt;5000&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;
&lt;p&gt;Will calculate the log2ratio between tags in chip.bam and mock.bam. The results will be written into a bedgraph file, averaging values in 5 kbp windows. &lt;/p&gt;
&lt;h2 id="example-2-chip-seq-analysis"&gt;Example 2: ChIP-seq analysis&lt;/h2&gt;
&lt;div class="codehilite"&gt;&lt;pre&gt;&lt;span class="err"&gt;$&lt;/span&gt; &lt;span class="n"&gt;dspchip&lt;/span&gt; &lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="n"&gt;i&lt;/span&gt; &lt;span class="n"&gt;chip&lt;/span&gt;&lt;span class="p"&gt;.&lt;/span&gt;&lt;span class="n"&gt;bam&lt;/span&gt; &lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="n"&gt;c&lt;/span&gt; &lt;span class="n"&gt;input&lt;/span&gt;&lt;span class="p"&gt;.&lt;/span&gt;&lt;span class="n"&gt;bam&lt;/span&gt; &lt;span class="o"&gt;--&lt;/span&gt;&lt;span class="n"&gt;pl&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;NLFTZ&lt;/span&gt; &lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="n"&gt;e&lt;/span&gt; &lt;span class="mi"&gt;50000&lt;/span&gt; &lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="n"&gt;s&lt;/span&gt; &lt;span class="mi"&gt;5000&lt;/span&gt; &lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="n"&gt;p&lt;/span&gt; &lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="n"&gt;l&lt;/span&gt; &lt;span class="n"&gt;mad&lt;/span&gt; &lt;span class="o"&gt;--&lt;/span&gt;&lt;span class="n"&gt;wf&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;max&lt;/span&gt; &lt;span class="o"&gt;--&lt;/span&gt;&lt;span class="n"&gt;fir&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;hanning&lt;/span&gt; &lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="n"&gt;n&lt;/span&gt; &lt;span class="n"&gt;enriched&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;
&lt;p&gt;Will calculate the log2ratio between chip.bam and input.bam (L) after energy normalization (N). Values will be smoothed using a Hanning low pass filter assuming the expected signal size being 50 kbp (F). Negative values will be removed (Z) after thresholding (T) using a MAD estimation. Data will be processed to find peak boundaries. BigWig file will be stored with a step size of 5 kbp using a "max" windowing function. Files prefix will be "enriched". &lt;/p&gt;
&lt;h2 id="example-3-correlate-features"&gt;Example 3: Correlate features&lt;/h2&gt;
&lt;div class="codehilite"&gt;&lt;pre&gt;&lt;span class="err"&gt;$&lt;/span&gt; &lt;span class="n"&gt;dspchip&lt;/span&gt; &lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="n"&gt;i&lt;/span&gt; &lt;span class="n"&gt;satellite&lt;/span&gt;&lt;span class="p"&gt;.&lt;/span&gt;&lt;span class="n"&gt;bed&lt;/span&gt; &lt;span class="o"&gt;--&lt;/span&gt;&lt;span class="n"&gt;pl&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;FZ&lt;/span&gt; &lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="n"&gt;e&lt;/span&gt; &lt;span class="mi"&gt;50000&lt;/span&gt; &lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="n"&gt;s&lt;/span&gt; &lt;span class="mi"&gt;1000&lt;/span&gt; &lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="n"&gt;n&lt;/span&gt; &lt;span class="n"&gt;satellite&lt;/span&gt; &lt;span class="o"&gt;--&lt;/span&gt;&lt;span class="n"&gt;fa&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;bed&lt;/span&gt; &lt;span class="o"&gt;--&lt;/span&gt;&lt;span class="n"&gt;csize&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;genome&lt;/span&gt;&lt;span class="p"&gt;.&lt;/span&gt;&lt;span class="n"&gt;tab&lt;/span&gt;
&lt;span class="err"&gt;$&lt;/span&gt; &lt;span class="n"&gt;dspchip&lt;/span&gt; &lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="n"&gt;i&lt;/span&gt; &lt;span class="n"&gt;enriched&lt;/span&gt;&lt;span class="p"&gt;.&lt;/span&gt;&lt;span class="n"&gt;bigwig&lt;/span&gt; &lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="n"&gt;c&lt;/span&gt; &lt;span class="n"&gt;satellite&lt;/span&gt;&lt;span class="p"&gt;.&lt;/span&gt;&lt;span class="n"&gt;bigwig&lt;/span&gt; &lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="n"&gt;e&lt;/span&gt; &lt;span class="mi"&gt;50000&lt;/span&gt; &lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="n"&gt;C&lt;/span&gt; &lt;span class="o"&gt;--&lt;/span&gt;&lt;span class="n"&gt;pl&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="err"&gt;''&lt;/span&gt; &lt;span class="o"&gt;--&lt;/span&gt;&lt;span class="n"&gt;fa&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;bw&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;
&lt;p&gt;The first command will get a bed file containing satellite repeats and will smooth it using FFT (assuming a window of 50 kbp). The last command will produce correlation plots (chromosome based) between ChIP-seq analysis and satellite repeats. &lt;/p&gt;&lt;/div&gt;</description><dc:creator xmlns:dc="http://purl.org/dc/elements/1.1/">Anonymous</dc:creator><pubDate>Thu, 19 Dec 2013 10:10:33 -0000</pubDate><guid>https://sourceforge.netf0c2d3734f247d46561986dcd124502f206c75f8</guid></item></channel></rss>