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<p align="center">
<img src="/assets/images/coverage_bam.png" width="500" height="300" alt="calculate the coverage from BAM file" />
</p>

<p>You can use samtools to calculate <a href="sequencing-coverage.html">coverage depth and percent coverage</a> from a BAM file.</p>

<p><code class="language-plaintext highlighter-rouge">samtools depth</code> allows calculation of the coverage depth (how many times the given position is covered by the mapped read ) from the BAM file, whereas
<code class="language-plaintext highlighter-rouge">samtools coverage</code> allows calculation of how much percentages of bases are sequenced from the entire genome and chromosome..</p>

<p>The following examples explain  how to use <code class="language-plaintext highlighter-rouge">samtools depth</code> and <code class="language-plaintext highlighter-rouge">samtools coverage</code> to calculate the coverage from a BAM file.</p>

<p>Before we begin, ensure that samtools is installed on your system. If not, you can <a href="handle-analyze-bam-files">read this article on installing samtools</a>.</p>

<h2 id="samtools-depth">Samtools depth</h2>

<p><code class="language-plaintext highlighter-rouge">samtools depth</code> command can be used for  calculating the coverage depth at each position (per-base coverage) or region of the reference genome.</p>

<p><code class="language-plaintext highlighter-rouge">samtools depth</code> is similar to <code class="language-plaintext highlighter-rouge">samtools mpileup</code> with some <a href="https://www.htslib.org/doc/samtools-depth.html">changes in parameters, output, and offers speed benefit</a>.</p>

<p>Calculate the coverage depth at each position,</p>

<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">samtools</span> <span class="n">depth</span> <span class="nb">input</span><span class="p">.</span><span class="n">bam</span> <span class="o">&gt;</span> <span class="n">coverage</span><span class="p">.</span><span class="n">txt</span>

<span class="c1"># view coverage.txt
</span><span class="n">head</span> <span class="n">coverage</span><span class="p">.</span><span class="n">txt</span>

<span class="n">chr1</span>    <span class="mi">10145</span>   <span class="mi">1</span>
<span class="n">chr1</span>    <span class="mi">10146</span>   <span class="mi">1</span>
<span class="n">chr1</span>    <span class="mi">10147</span>   <span class="mi">1</span>
<span class="n">chr1</span>    <span class="mi">10148</span>   <span class="mi">2</span>
<span class="n">chr1</span>    <span class="mi">10149</span>   <span class="mi">3</span>
</code></pre></div></div>

<p><code class="language-plaintext highlighter-rouge">samtools depth</code> calculates how many times the given position or region is covered by the mapped read.</p>

<p><code class="language-plaintext highlighter-rouge">samtools depth</code> reports three column output containing chromosome, base position, and read depth at each position.</p>

<p>If you want to calculate the average coverage depth of all the reads in a BAM file for all chromosomes, you need to use <code class="language-plaintext highlighter-rouge">awk</code> to calculate the average coverage depth based on the depth values in third column.</p>

<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">samtools</span> <span class="n">depth</span> <span class="nb">input</span><span class="p">.</span><span class="n">bam</span> <span class="o">|</span> <span class="n">awk</span> <span class="s">'{sum+=$3} END {print "Average coverage:", sum/NR}'</span> <span class="n">coverage</span><span class="p">.</span><span class="n">txt</span>

<span class="c1"># output
</span><span class="n">Average</span> <span class="o">=</span>  <span class="mf">1.21185</span>
</code></pre></div></div>

<p>The average coverage depth for <code class="language-plaintext highlighter-rouge">input.bam</code> BAM file is 1.21 i.e. each base is sequenced at 1.21 times on average.</p>

<p>To get the <strong>coverage depth in terms of X</strong>, you need to divide this number (here, 1.21) by the haploid genome size (in bp).</p>

<blockquote>
  <p><code class="language-plaintext highlighter-rouge">samtools depth</code>  does not calculate the coverage depth for positions that are not mapped by the reads. In this case,
you can add <code class="language-plaintext highlighter-rouge">-a</code> parameter to the <code class="language-plaintext highlighter-rouge">samtools depth</code> to output all positions including zero depth .</p>
</blockquote>

<h3 id="samtools-coverage">Samtools coverage</h3>

<p><code class="language-plaintext highlighter-rouge">samtools coverage</code> command can be used for  calculating the percent of coverage i.e. how many percent of bases are covered for each chromosome.
For example. if your chromosome size is 100 and only 10 bases are covered, then your coverage is 10%. Sometimes, this is also referred as
<a href="sequencing-coverage.html">breadth of coverage</a>.</p>

<p><code class="language-plaintext highlighter-rouge">samtools coverage</code> command gives the output in table format for each chromosome.</p>

<p>Calculate the coverage for each chromosome from the BAM file,</p>

<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">samtools</span> <span class="n">coverage</span> <span class="nb">input</span><span class="p">.</span><span class="n">bam</span> 

<span class="c1"># output
#rname  startpos        endpos  numreads        covbases        coverage        meandepth       meanbaseq       meanmapq
</span><span class="n">chr1</span>    <span class="mi">1</span>       <span class="mi">249250621</span>       <span class="mi">444</span>     <span class="mi">13160</span>   <span class="mf">0.00527983</span>      <span class="mf">6.39838e-05</span>     <span class="mf">48.2</span>    <span class="mi">25</span>
<span class="n">chr2</span>    <span class="mi">1</span>       <span class="mi">243199373</span>       <span class="mi">0</span>       <span class="mi">0</span>       <span class="mi">0</span>       <span class="mi">0</span>       <span class="mi">0</span>       <span class="mi">0</span>
<span class="n">chr3</span>    <span class="mi">1</span>       <span class="mi">198022430</span>       <span class="mi">0</span>       <span class="mi">0</span>       <span class="mi">0</span>       <span class="mi">0</span>       <span class="mi">0</span>       <span class="mi">0</span>
<span class="n">chr4</span>    <span class="mi">1</span>       <span class="mi">191154276</span>       <span class="mi">0</span>       <span class="mi">0</span>       <span class="mi">0</span>       <span class="mi">0</span>       <span class="mi">0</span>       <span class="mi">0</span>
<span class="n">chr5</span>    <span class="mi">1</span>       <span class="mi">180915260</span>       <span class="mi">0</span>       <span class="mi">0</span>       <span class="mi">0</span>       <span class="mi">0</span>       <span class="mi">0</span>       <span class="mi">0</span>
</code></pre></div></div>

<p>Based on the output above, the % coverage for chr1 is 0.0052%. This means that 13160 bases are covered for chr1 (out of 249250621).</p>

<p>
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</ul>


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</p>]]></content><author><name>Renesh Bedre</name></author><category term="Bioinformatics" /><summary type="html"><![CDATA[Learn how to use samtools depth and coverage commands to calculate the coverage from BAM file]]></summary></entry><entry><title type="html">Get Non-overlapping Portion Between Two Regions in bedtools</title><link href="https://www.reneshbedre.com/blog/bedtools-subtract.html" rel="alternate" type="text/html" title="Get Non-overlapping Portion Between Two Regions in bedtools" /><published>2024-05-19T00:00:00+00:00</published><updated>2024-05-19T00:00:00+00:00</updated><id>https://www.reneshbedre.com/blog/bedtools-subtract</id><content type="html" xml:base="https://www.reneshbedre.com/blog/bedtools-subtract.html"><![CDATA[<style>
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<p>You can use the <code class="language-plaintext highlighter-rouge">subtract</code> function from the <a href="https://bedtools.readthedocs.io/en/latest/">bedtools</a> to get the non-overlapping portion of genomic intervals between the two regions.</p>

<p>The basic syntax for <code class="language-plaintext highlighter-rouge">bedtools subtract</code> is:</p>

<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">bedtools</span> <span class="n">subtract</span> <span class="o">-</span><span class="n">a</span> <span class="n">region1</span><span class="p">.</span><span class="n">bed</span> <span class="o">-</span><span class="n">b</span> <span class="n">region2</span><span class="p">.</span><span class="n">bed</span> <span class="o">&gt;</span> <span class="n">non_overlapping_regions</span><span class="p">.</span><span class="n">bed</span>
</code></pre></div></div>

<p>The following example explains how to use the <code class="language-plaintext highlighter-rouge">bedtools subtract</code> to get the non-overlapping intervals between two
region files.</p>

<p>Suppose, you have two BED files (region1.bed and region2.bed) containing the genomic intervals as regions and you want to get the non-overlapping portion of intervals from region1.bed that
do not overlap with the regions from region2.bed.</p>

<p>region1.bed</p>

<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">cat</span> <span class="n">region1</span><span class="p">.</span><span class="n">bed</span>

<span class="n">Chr1</span>    <span class="mi">10</span>      <span class="mi">20</span>
<span class="n">Chr1</span>    <span class="mi">40</span>      <span class="mi">70</span>
<span class="n">Chr1</span>    <span class="mi">100</span>     <span class="mi">150</span>
</code></pre></div></div>

<p>region2.bed</p>

<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">cat</span> <span class="n">region2</span><span class="p">.</span><span class="n">bed</span>

<span class="n">Chr1</span>    <span class="mi">15</span>      <span class="mi">20</span>
<span class="n">Chr1</span>    <span class="mi">60</span>      <span class="mi">70</span>
<span class="n">Chr1</span>    <span class="mi">120</span>     <span class="mi">180</span>
</code></pre></div></div>

<p>Get the non-overlapping portion of genomic intervals from region1.bed using <code class="language-plaintext highlighter-rouge">bedtools subtract</code>.</p>

<p>The <code class="language-plaintext highlighter-rouge">bedtools subtract</code> finds the regions from region2.bed that overlap  (by at least 1 bp) with the regions from region1.bed. If there is any
overlapping regions, then the overlapping interval is removed from the region1.bed, and remaining portion of interval is reported.</p>

<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>bedtools subtract -a region1.bed -b region2.bed &gt; non_overlapping_regions.bed

cat non_overlapping_regions.bed

# output
Chr1    10      15
Chr1    40      60
Chr1    100     120
</code></pre></div></div>

<p>You can see that the non-overlapping portion of the genomic intervals are reported from the region1.bed (<code class="language-plaintext highlighter-rouge">-a</code>) file.</p>

<p>By default, <code class="language-plaintext highlighter-rouge">bedtools subtract</code> uses at least 1 bp interval to find the overlapping regions. You can control the amount of overlap (in fractions) from <code class="language-plaintext highlighter-rouge">-a</code> file using the
<code class="language-plaintext highlighter-rouge">-f</code> parameter.</p>

<p>For example, get non-overlapping portion from region1.bed (<code class="language-plaintext highlighter-rouge">-a</code>) file when there is at least 50% overlap between regions from region1.bed and region2.bed files.</p>

<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>bedtools subtract -a region1.bed -b region2.bed -f 0.5 &gt; non_overlapping_regions.bed 

cat non_overlapping_regions.bed

# output
Chr1    10      15
Chr1    40      70
Chr1    100     120
</code></pre></div></div>

<p>You can see that first and third regions overlap by 50% and their non-overlapping portion is reported.</p>

<p>Similarly, You can control the amount of overlap (in fractions) from <code class="language-plaintext highlighter-rouge">-b</code> file using the <code class="language-plaintext highlighter-rouge">-F</code> parameter.</p>

<p>
<h2><span style="color:#33a8ff">Enhance your skills with courses on genomics and bioinformatics</span></h2>
<ul>
  <li><a href="https://coursera.pxf.io/x9j72R" target="_blank">Genomic Data Science Specialization</a></li>
  <li><a href="https://coursera.pxf.io/KevDZy" target="_blank">Biology Meets Programming: Bioinformatics for Beginners</a></li>
  <li><a href="https://imp.i384100.net/DVggGj" target="_blank">Python for Genomic Data Science</a></li>
 <li><a href="https://imp.i384100.net/jW33Le" target="_blank">Bioinformatics Specialization</a></li>
  <li><a href="https://imp.i384100.net/b3okqx" target="_blank">Command Line Tools for Genomic Data Science</a></li>
  <li><a href="https://imp.i384100.net/Y9bk6J" target="_blank">Introduction to Genomic Technologies</a></li>
</ul>


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</p>]]></content><author><name>Renesh Bedre</name></author><category term="Bioinformatics" /><summary type="html"><![CDATA[Learn how to use bedtools subtract to get non-overlapping portion between two regions in BED files]]></summary></entry><entry><title type="html">Find Max and Min Sequence Length in Fasta</title><link href="https://www.reneshbedre.com/blog/max-min-len-fasta.html" rel="alternate" type="text/html" title="Find Max and Min Sequence Length in Fasta" /><published>2024-05-19T00:00:00+00:00</published><updated>2024-05-19T00:00:00+00:00</updated><id>https://www.reneshbedre.com/blog/max-min-len-fasta</id><content type="html" xml:base="https://www.reneshbedre.com/blog/max-min-len-fasta.html"><![CDATA[<style>
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<p>You can use various command-line tools to get the maximum and minimum sequence lengths in a FASTA file.</p>

<p>This article  describes how to find the maximum and minimum sequence lengths in a FASTA file in Python, seqkit, and samtools.</p>

<h2 id="python">Python</h2>

<p>You can use the <code class="language-plaintext highlighter-rouge">max_min_len()</code> function from <a href="https://github.com/reneshbedre/bioinfokit">bioinfokit</a> (v2.1.4) to find the maximum and minimum sequence lengths in a FASTA file.</p>

<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1"># import package
</span><span class="kn">from</span> <span class="nn">bioinfokit.analys</span> <span class="kn">import</span> <span class="n">Fasta</span>

<span class="n">Fasta</span><span class="p">.</span><span class="n">max_min_len</span><span class="p">(</span><span class="s">"file.fasta"</span><span class="p">)</span>

<span class="c1"># output
</span><span class="n">Max</span> <span class="n">Length</span> <span class="n">Seq</span><span class="p">:</span> <span class="n">KU562861</span><span class="p">.</span><span class="mi">1</span> <span class="mi">153</span>
<span class="n">Min</span> <span class="n">Length</span> <span class="n">Seq</span><span class="p">:</span> <span class="n">MH150936</span><span class="p">.</span><span class="mi">1</span> <span class="mi">114</span>
</code></pre></div></div>

<p>In the example file.fasta, the maximum and minimum sequence lengths are 153 bp and 114 bp, respectively.</p>

<h2 id="seqkit">seqkit</h2>

<p>You can use the <code class="language-plaintext highlighter-rouge">fx2tab</code> parameter from <a href="https://bioinf.shenwei.me/seqkit/usage/">seqkit</a> to find the maximum and minimum sequence lengths in a FASTA file.</p>

<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1"># get max length
</span><span class="n">seqkit</span> <span class="n">fx2tab</span> <span class="o">--</span><span class="n">length</span> <span class="o">--</span><span class="n">name</span> <span class="nb">file</span><span class="p">.</span><span class="n">fasta</span> <span class="o">|</span> <span class="n">cut</span> <span class="o">-</span><span class="n">f2</span> <span class="o">|</span> <span class="n">sort</span> <span class="o">-</span><span class="n">n</span> <span class="o">|</span> <span class="n">head</span> <span class="o">-</span><span class="mi">1</span>

<span class="c1"># output
</span><span class="mi">153</span>

<span class="c1"># get min length
</span><span class="n">seqkit</span> <span class="n">fx2tab</span> <span class="o">--</span><span class="n">length</span> <span class="o">--</span><span class="n">name</span> <span class="nb">file</span><span class="p">.</span><span class="n">fasta</span> <span class="o">|</span> <span class="n">cut</span> <span class="o">-</span><span class="n">f2</span> <span class="o">|</span> <span class="n">sort</span> <span class="o">-</span><span class="n">n</span> <span class="o">|</span> <span class="n">tail</span> <span class="o">-</span><span class="mi">1</span>
<span class="c1"># output
</span><span class="mi">114</span>
</code></pre></div></div>

<h2 id="samtools">samtools</h2>

<p>You can also use the <a href="https://www.htslib.org/">samtools</a> indexed fasta file for finding the maximum and minimum sequence lengths.</p>

<p>You first need to create an index of the fasta file.</p>

<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">samtools</span> <span class="n">faidx</span> <span class="nb">file</span><span class="p">.</span><span class="n">fa</span>
</code></pre></div></div>

<p>The first two columns in the index fasta file (<code class="language-plaintext highlighter-rouge">file.fasta.fai</code>) contain the sequence name and their lengths.</p>

<p>You can get the maximum and minimum sequence lengths from <code class="language-plaintext highlighter-rouge">file.fasta.fai</code> like this:</p>

<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1"># get max length
</span><span class="n">cut</span> <span class="o">-</span><span class="n">f2</span> <span class="nb">file</span><span class="p">.</span><span class="n">fasta</span><span class="p">.</span><span class="n">fai</span> <span class="o">|</span> <span class="n">sort</span> <span class="o">-</span><span class="n">n</span> <span class="o">|</span> <span class="n">head</span> <span class="o">-</span><span class="mi">1</span>

<span class="c1"># output
</span><span class="mi">153</span>

<span class="c1"># get min length
</span><span class="n">cut</span> <span class="o">-</span><span class="n">f2</span> <span class="nb">file</span><span class="p">.</span><span class="n">fasta</span><span class="p">.</span><span class="n">fai</span> <span class="o">|</span> <span class="n">sort</span> <span class="o">-</span><span class="n">n</span> <span class="o">|</span> <span class="n">tail</span> <span class="o">-</span><span class="mi">1</span>

<span class="c1"># output
</span><span class="mi">114</span>
</code></pre></div></div>

<p>
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</ul>


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</p>]]></content><author><name>Renesh Bedre</name></author><category term="Machine Learning" /><summary type="html"><![CDATA[Learn how to find the maximum and minimum sequence lengths in a FASTA file using Python, seqkit, and samtools]]></summary></entry><entry><title type="html">Python: Why VIF Return Inf Value?</title><link href="https://www.reneshbedre.com/blog/vif-return-inf.html" rel="alternate" type="text/html" title="Python: Why VIF Return Inf Value?" /><published>2024-05-19T00:00:00+00:00</published><updated>2024-05-19T00:00:00+00:00</updated><id>https://www.reneshbedre.com/blog/vif-return-inf</id><content type="html" xml:base="https://www.reneshbedre.com/blog/vif-return-inf.html"><![CDATA[<style>
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<h2 id="background">Background</h2>

<p><a href="variance-inflation-factor.html">Variance Inflation Factor (VIF)</a> is used for detecting multicollinearity in regression models. It measures how much the 
variance of a regression coefficient is inflated due to multicollinearity with other independent variables in the model.</p>

<p>In Python, VIF can be calculated using the <a href="https://www.statsmodels.org/stable/generated/statsmodels.stats.outliers_influence.variance_inflation_factor.html"><code class="language-plaintext highlighter-rouge">variance_inflation_factor()</code></a> function from the statsmodels package. However, 
you may encounter a situation where you get <code class="language-plaintext highlighter-rouge">Inf</code> (infinity) values as the VIF for some of the independent variables.</p>

<p>Identifying and removing the multicollinearity issues is essential for robust predictive modeling in machine learning.</p>

<p>This article explains the reasons behind <code class="language-plaintext highlighter-rouge">Inf</code> values  for the VIF with an example analysis.</p>

<h2 id="why-inf-values-for-vif">Why <code class="language-plaintext highlighter-rouge">inf</code> values for VIF?</h2>

<p>You can get <code class="language-plaintext highlighter-rouge">inf</code> values for VIF due to the <strong>perfect multicollinearity</strong>. This happens when two or more independent variables
in a model are perfectly linearly dependent. That is, one independent variable in the model can be entirely predicted by 
another independent variable.</p>

<p>If you have multiple identical columns in the input dataset, there will be perfect multicollinearity.</p>

<p>In addition, high correlation (correlation coefficients close to 1 or -1) between the independent variables can also 
give very high VIF values that could lead to <code class="language-plaintext highlighter-rouge">inf</code> values.</p>

<h2 id="vif-calculation-example">VIF calculation Example</h2>

<p>The following example explains  how you get the <code class="language-plaintext highlighter-rouge">inf</code> values for the VIF.</p>

<p>Create an example dataset,</p>

<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1"># import package
</span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="n">pd</span>

<span class="c1"># load example dataset
</span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="p">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s">"https://reneshbedre.github.io/assets/posts/reg/bp.csv"</span><span class="p">)</span>

<span class="c1"># view
</span><span class="n">df</span><span class="p">.</span><span class="n">head</span><span class="p">()</span>
    <span class="n">BP</span>  <span class="n">Age</span>  <span class="n">Weight</span>   <span class="n">BSA</span>  <span class="n">Dur</span>  <span class="n">Pulse</span>  <span class="n">Stress</span>
<span class="mi">0</span>  <span class="mi">105</span>   <span class="mi">47</span>    <span class="mf">85.4</span>  <span class="mf">1.75</span>  <span class="mf">5.1</span>     <span class="mi">63</span>      <span class="mi">33</span>
<span class="mi">1</span>  <span class="mi">115</span>   <span class="mi">49</span>    <span class="mf">94.2</span>  <span class="mf">2.10</span>  <span class="mf">3.8</span>     <span class="mi">70</span>      <span class="mi">14</span>
<span class="mi">2</span>  <span class="mi">116</span>   <span class="mi">49</span>    <span class="mf">95.3</span>  <span class="mf">1.98</span>  <span class="mf">8.2</span>     <span class="mi">72</span>      <span class="mi">10</span>
<span class="mi">3</span>  <span class="mi">117</span>   <span class="mi">50</span>    <span class="mf">94.7</span>  <span class="mf">2.01</span>  <span class="mf">5.8</span>     <span class="mi">73</span>      <span class="mi">99</span>
<span class="mi">4</span>  <span class="mi">112</span>   <span class="mi">51</span>    <span class="mf">89.4</span>  <span class="mf">1.89</span>  <span class="mf">7.0</span>     <span class="mi">72</span>      <span class="mi">95</span>
</code></pre></div></div>

<p>Create separate datasets for independent variables (Age, Weight, BSA, Dur, Pulse, Stress) and dependent variables (BP),</p>

<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1"># independent variables
</span><span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="p">[[</span><span class="s">'Age'</span><span class="p">,</span> <span class="s">'Weight'</span><span class="p">,</span> <span class="s">'BSA'</span><span class="p">,</span> <span class="s">'Dur'</span><span class="p">,</span> <span class="s">'Pulse'</span><span class="p">,</span> <span class="s">'Stress'</span><span class="p">]]</span>   

 <span class="c1"># dependent variables
</span><span class="n">y</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s">'BP'</span><span class="p">]</span>   
</code></pre></div></div>

<p>Add duplicate independent variable to have perfect multicollinearity,</p>

<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1"># independent variables
</span><span class="n">X</span><span class="p">[</span><span class="s">'Age_dup'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s">'Age'</span><span class="p">]</span>   

<span class="c1"># view X
</span><span class="n">X</span><span class="p">.</span><span class="n">head</span><span class="p">()</span>

  <span class="n">Age</span>  <span class="n">Weight</span>   <span class="n">BSA</span>  <span class="n">Dur</span>  <span class="n">Pulse</span>  <span class="n">Stress</span>  <span class="n">Age_dup</span>
<span class="mi">0</span>   <span class="mi">47</span>    <span class="mf">85.4</span>  <span class="mf">1.75</span>  <span class="mf">5.1</span>     <span class="mi">63</span>      <span class="mi">33</span>       <span class="mi">47</span>
<span class="mi">1</span>   <span class="mi">49</span>    <span class="mf">94.2</span>  <span class="mf">2.10</span>  <span class="mf">3.8</span>     <span class="mi">70</span>      <span class="mi">14</span>       <span class="mi">49</span>
<span class="mi">2</span>   <span class="mi">49</span>    <span class="mf">95.3</span>  <span class="mf">1.98</span>  <span class="mf">8.2</span>     <span class="mi">72</span>      <span class="mi">10</span>       <span class="mi">49</span>
<span class="mi">3</span>   <span class="mi">50</span>    <span class="mf">94.7</span>  <span class="mf">2.01</span>  <span class="mf">5.8</span>     <span class="mi">73</span>      <span class="mi">99</span>       <span class="mi">50</span>
<span class="mi">4</span>   <span class="mi">51</span>    <span class="mf">89.4</span>  <span class="mf">1.89</span>  <span class="mf">7.0</span>     <span class="mi">72</span>      <span class="mi">95</span>       <span class="mi">51</span>
</code></pre></div></div>

<p>Calculate multicollinearity using the <code class="language-plaintext highlighter-rouge">variance_inflation_factor()</code> function from the statsmodels package,</p>

<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">import</span> <span class="nn">statsmodels.api</span> <span class="k">as</span> <span class="n">sm</span>
<span class="kn">from</span> <span class="nn">statsmodels.stats.outliers_influence</span> <span class="kn">import</span> <span class="n">variance_inflation_factor</span>

<span class="n">X</span> <span class="o">=</span> <span class="n">sm</span><span class="p">.</span><span class="n">add_constant</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="c1"># fit the regression model
</span><span class="n">reg</span> <span class="o">=</span> <span class="n">sm</span><span class="p">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">X</span><span class="p">).</span><span class="n">fit</span><span class="p">()</span>
<span class="c1"># get Variance Inflation Factor (VIF) 
</span><span class="n">pd</span><span class="p">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s">'variables'</span><span class="p">:</span><span class="n">X</span><span class="p">.</span><span class="n">columns</span><span class="p">[</span><span class="mi">1</span><span class="p">:],</span> <span class="s">'VIF'</span><span class="p">:[</span><span class="n">variance_inflation_factor</span><span class="p">(</span><span class="n">X</span><span class="p">.</span><span class="n">values</span><span class="p">,</span> <span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">.</span><span class="n">columns</span><span class="p">[</span><span class="mi">1</span><span class="p">:]))]})</span>
  <span class="n">variables</span>       <span class="n">VIF</span>
<span class="mi">0</span>       <span class="n">Age</span>       <span class="n">inf</span>
<span class="mi">1</span>    <span class="n">Weight</span>  <span class="mf">8.417035</span>
<span class="mi">2</span>       <span class="n">BSA</span>  <span class="mf">5.328751</span>
<span class="mi">3</span>       <span class="n">Dur</span>  <span class="mf">1.237309</span>
<span class="mi">4</span>     <span class="n">Pulse</span>  <span class="mf">4.413575</span>
<span class="mi">5</span>    <span class="n">Stress</span>  <span class="mf">1.834845</span>
<span class="mi">6</span>   <span class="n">Age_dup</span>       <span class="n">inf</span>
</code></pre></div></div>

<p>You can see that the VIF values for the Age and Agw_dups variables are <code class="language-plaintext highlighter-rouge">inf</code>. This is because both Age and Age_dups are perfectly linearly dependent (due to exact values in between those variables).
It means that <code class="language-plaintext highlighter-rouge">Age</code> and <code class="language-plaintext highlighter-rouge">Age_dup</code> have perfect multicollinearity.</p>

<p>The multicollinearity issue in regression models can be identified  by checking for duplicate columns and performing the pairwise correlation. The multicollinearity issue can be resolved by removing one of the variables causing
the multicollinearity.</p>

<p>
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</p>]]></content><author><name>Renesh Bedre</name></author><category term="Machine Learning" /><summary type="html"><![CDATA[Learn resons behind the Inf Value in VIF analysis in Python with example]]></summary></entry><entry><title type="html">What is Log2 Fold Change in Bioinformatics</title><link href="https://www.reneshbedre.com/blog/log-fold-change.html" rel="alternate" type="text/html" title="What is Log2 Fold Change in Bioinformatics" /><published>2024-04-28T00:00:00+00:00</published><updated>2024-05-19T00:00:00+00:00</updated><id>https://www.reneshbedre.com/blog/log-fold-change</id><content type="html" xml:base="https://www.reneshbedre.com/blog/log-fold-change.html"><![CDATA[<script type="text/javascript" async="" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-MML-AM_CHTML"></script>

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<p align="center">
<img src="/assets/images/log2fc.png" width="500" height="300" alt="log2 fold change" />
</p>

<h2 id="what-is-log2-fold-change">What is Log2 Fold Change?</h2>

<p>The log2 fold change (log2FC) term is commonly used in bioinformatics to measure the changes in gene expression between two 
conditions (e.g. control vs. treated).</p>

<p>log2 fold change is the ratio of expression levels between two conditions and it is calculated by taking log2 of the ratio of
expression levels of two conditions.</p>

<p>For example, you have two conditions such as control and treated, and you measured the expression changes (read counts) between 
two conditions across several genes. The log2 fold change for a particular gene can be calculated  as:</p>

<p align="center">
<img src="/assets/images/CodeCogsEqn.png" width="146" height="38" alt="log2 fold change formula" />
</p>

<p>Where, <em>T</em> is the gene expression count in the treated sample and <em>C</em> is the gene expression count in the control sample.</p>

<h2 id="why-to-use-log2-fold-change">Why to use Log2 Fold Change?</h2>

<h3 id="easy-to-understand-changes-in--gene-expression">Easy to understand changes in  gene expression</h3>

<p>Log2 fold change helps to measure the changes in gene expression in different directions equally from zero.</p>

<p>For example, a log2 fold change of 1 (positive fold change), indicates that the gene is upregulated by a factor of 2. Similarly, a log2 fold change of -1 (negative fold change)
indicates that the gene is downregulated by a factor of 2.</p>

<p>This symmetry in both directions helps easy to understand the changes in gene expression.</p>

<p>It also helps in easy comparison allowing visualization and comparison in plots like <a href="volcano.html">volcano plots</a>,
<a href="heatmap-python.html">heatmaps</a>, <a href="ma.html">MA plot</a>, etc.</p>

<h3 id="biological-relevance">Biological relevance</h3>

<p>Gene expression changes could be significantly  different with very high numbers (sometimes exponential) under varying biological conditions.</p>

<p>Log transformation simplifies these changes in gene expression and makes the results more interpretable in a biological context.</p>

<p>For instance, a log2 fold change of 1 indicates a change in gene expression by a factor of 2 (i.e. doubling of expression).</p>

<h2 id="log2-fold-change-calculation">Log2 Fold Change calculation</h2>

<p>Consider a gene with an expression count of 500 in the control sample and 2000 in the treated sample (i.e. gene is upregulated in the treated sample by 4 fold)</p>

<p align="center">
<img src="/assets/images/log2fc_1.png" width="201" height="37" alt="log2 fold change calculation" />
</p>

<p>Conversely, if the expression counts are 2000 in the control sample and 500 in the treated sample (i.e. gene is downregulated in the treated sample by 4 fold):</p>

<p align="center">
<img src="/assets/images/log2fc_2.png" width="201" height="37" alt="log2 fold change calculation" />
</p>

<p>You can see that log2 transformation helped easy comparison in gene expression changes in the control and treated samples. Without log2 transformation, we will
get the values of 4 for upregulation and 0.25 for downregulation.</p>

<p>Hence, log2 fold change is a straightforward and meaningful way to describe changes in gene expression.</p>

<p>In Bioinformatics, the log2 fold change is reported by various tools such as <a href="deseq2.html">DESeq2</a>, <a href="edger-tutorial.html">EdgeR</a>, etc.</p>

<p>
<h2><span style="color:#33a8ff">Enhance your skills with courses on genomics and bioinformatics</span></h2>
<ul>
  <li><a href="https://coursera.pxf.io/x9j72R" target="_blank">Genomic Data Science Specialization</a></li>
  <li><a href="https://coursera.pxf.io/KevDZy" target="_blank">Biology Meets Programming: Bioinformatics for Beginners</a></li>
  <li><a href="https://imp.i384100.net/DVggGj" target="_blank">Python for Genomic Data Science</a></li>
 <li><a href="https://imp.i384100.net/jW33Le" target="_blank">Bioinformatics Specialization</a></li>
  <li><a href="https://imp.i384100.net/b3okqx" target="_blank">Command Line Tools for Genomic Data Science</a></li>
  <li><a href="https://imp.i384100.net/Y9bk6J" target="_blank">Introduction to Genomic Technologies</a></li>
</ul>


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</p>]]></content><author><name>Renesh Bedre</name></author><category term="Bioinformatics" /><category term="Molecular Biology" /><summary type="html"><![CDATA[Learn what is log2 fold change, how to calculate log2 fold change, and why log2 transformation used for measuring the fold change between samples in bioinformatics]]></summary></entry><entry><title type="html">Visualizing the 3D Genome with Python</title><link href="https://www.reneshbedre.com/blog/visualize-3d-genomics.html" rel="alternate" type="text/html" title="Visualizing the 3D Genome with Python" /><published>2024-04-21T00:00:00+00:00</published><updated>2024-04-20T00:00:00+00:00</updated><id>https://www.reneshbedre.com/blog/visualize-3d-genomics</id><content type="html" xml:base="https://www.reneshbedre.com/blog/visualize-3d-genomics.html"><![CDATA[<script type="text/javascript" async="" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-MML-AM_CHTML"></script>

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<p>The genome isn’t just a long string of letters, it folds and loops in intricate ways inside the
nucleus. This 3D structure plays a critical role in regulating gene expression, cellular function,
and genome stability. With the rise of 3D genomics, scientists are using experimental and
computational tools to uncover how DNA’s shape influences its function.</p>

<p>In this article, we will briefly explore 3D genomics and walk through a simple Python script to
visualize simulated chromatin interaction data, similar to what you might get from Hi-C
experiments.</p>

<h2 id="why-3d-matters-in-genomics">Why 3D Matters in Genomics?</h2>

<p>Traditional genomics looks at the genome as a 1D sequence, but in the cell nucleus, this
sequence folds to bring distant genomic regions close together.</p>

<ul>
  <li>Enhancers to activate distant genes</li>
  <li>Structural features like TADs (topologically associating domains) to compartmentalize
regulatory activity</li>
  <li>The formation of chromatin loops that are critical for gene expression</li>
</ul>

<h2 id="visualizing-simulated-hi-c-data-in-python">Visualizing Simulated Hi-C Data in Python</h2>

<p>Let’s simulate and visualize a simple Hi-C contact matrix using Python. A contact matrix shows
the frequency of physical interactions between different genomic regions.</p>

<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="n">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="n">plt</span>
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="n">sns</span>

<span class="c1"># create simulated Hi-C contact matrix
</span><span class="n">np</span><span class="p">.</span><span class="n">random</span><span class="p">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
<span class="n">size</span> <span class="o">=</span> <span class="mi">30</span> <span class="c1"># Number of genomic bins
</span><span class="n">matrix</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">random</span><span class="p">.</span><span class="n">poisson</span><span class="p">(</span><span class="n">lam</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="n">size</span><span class="p">))</span>

<span class="c1"># Add higher interaction within certain domains (TADs)
</span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="mi">10</span><span class="p">):</span>
    <span class="n">matrix</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="mi">5</span><span class="p">,</span> <span class="n">i</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="mi">5</span><span class="p">]</span> <span class="o">+=</span> <span class="n">np</span><span class="p">.</span><span class="n">random</span><span class="p">.</span><span class="n">poisson</span><span class="p">(</span><span class="n">lam</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>

<span class="c1"># Make the matrix symmetric (Hi-C contact maps are symmetrical)
</span><span class="n">hic_matrix</span> <span class="o">=</span> <span class="p">(</span><span class="n">matrix</span> <span class="o">+</span> <span class="n">matrix</span><span class="p">.</span><span class="n">T</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span>
<span class="c1"># Plotting the Hi-C-like matrix
</span><span class="n">plt</span><span class="p">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">sns</span><span class="p">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">hic_matrix</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=&amp;</span><span class="c1">#39;coolwarm&amp;#39;, square=True, cbar_kws={&amp;#39;label&amp;#39;: &amp;#39;Interaction
</span><span class="n">Frequency</span><span class="o">&amp;</span><span class="c1">#39;})
</span><span class="n">plt</span><span class="p">.</span><span class="n">title</span><span class="p">(</span><span class="o">&amp;</span><span class="c1">#39;Simulated Hi-C Contact Map&amp;#39;)
</span><span class="n">plt</span><span class="p">.</span><span class="n">xlabel</span><span class="p">(</span><span class="o">&amp;</span><span class="c1">#39;Genomic Bins&amp;#39;)
</span><span class="n">plt</span><span class="p">.</span><span class="n">ylabel</span><span class="p">(</span><span class="o">&amp;</span><span class="c1">#39;Genomic Bins&amp;#39;)
</span><span class="n">plt</span><span class="p">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="p">.</span><span class="n">show</span><span class="p">()</span>
</code></pre></div></div>

<h2 id="interpretation">Interpretation</h2>

<ul>
  <li>High values near the diagonal show local interactions between adjacent regions.</li>
  <li>Clusters of higher intensity off the diagonal can represent loops or TADs.</li>
  <li>These visual cues give researchers insight into how chromatin folds and which parts of
the genome physically interact.</li>
</ul>

<p>
<h2><span style="color:#33a8ff">Enhance your skills with courses on genomics and bioinformatics</span></h2>
<ul>
  <li><a href="https://coursera.pxf.io/x9j72R" target="_blank">Genomic Data Science Specialization</a></li>
  <li><a href="https://coursera.pxf.io/KevDZy" target="_blank">Biology Meets Programming: Bioinformatics for Beginners</a></li>
  <li><a href="https://imp.i384100.net/DVggGj" target="_blank">Python for Genomic Data Science</a></li>
 <li><a href="https://imp.i384100.net/jW33Le" target="_blank">Bioinformatics Specialization</a></li>
  <li><a href="https://imp.i384100.net/b3okqx" target="_blank">Command Line Tools for Genomic Data Science</a></li>
  <li><a href="https://imp.i384100.net/Y9bk6J" target="_blank">Introduction to Genomic Technologies</a></li>
</ul>


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</p>]]></content><author><name>Renesh Bedre</name></author><category term="Bioinformatics" /><summary type="html"><![CDATA[Introduction to 3D Genomics]]></summary></entry><entry><title type="html">Unlocking the Genome’s Architecture - An Introduction to 3D Genomics</title><link href="https://www.reneshbedre.com/blog/hic.html" rel="alternate" type="text/html" title="Unlocking the Genome’s Architecture - An Introduction to 3D Genomics" /><published>2024-04-20T00:00:00+00:00</published><updated>2024-04-20T00:00:00+00:00</updated><id>https://www.reneshbedre.com/blog/hic</id><content type="html" xml:base="https://www.reneshbedre.com/blog/hic.html"><![CDATA[<script type="text/javascript" async="" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-MML-AM_CHTML"></script>

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<p>While traditional genomics has provided a linear sequence of DNA, it’s now evident that the 
spatial arrangement of the genome plays a crucial role in regulating gene expression and cellular behavior. This 
realization has given rise to the field of 3D genomics, which explores how the genome’s physical structure influences 
its function.</p>

<h2 id="what-is-3d-genomics">What Is 3D Genomics?</h2>

<p>3D genomics investigate the three-dimensional configuration of the genome within the nucleus. Rather than existing as 
a simple linear sequence, DNA folds and loops in complex ways, bringing distant genomic regions into close proximity. 
These interactions can affect gene expression, as regulatory elements like enhancers and silencers may physically contact 
genes they regulate, despite being far apart in the linear sequence.</p>

<h2 id="key-technologies-in-3d-genomics">Key Technologies in 3D Genomics</h2>

<p>Several advanced techniques have been developed to study the genome’s 3D structure:</p>
<ul>
  <li>Hi-C: A genome-wide method that captures chromatin interactions by crosslinking DNA, cutting it with restriction enzymes, 
and re-ligating the fragments. Sequencing the junctions reveals which regions are physically close in the nucleus.</li>
  <li>Chromatin Conformation Capture (3C) and its derivatives (4C, 5C): These techniques analyze specific chromatin interactions, 
providing detailed insights into the spatial organization of particular genomic loci.</li>
  <li>Genome Architecture Mapping (GAM): A method that involves slicing the nucleus and sequencing the DNA in each slice to infer 
spatial proximity between genomic regions.</li>
  <li>Single-cell Hi-C: An adaptation of Hi-C that allows the study of chromatin interactions at the single-cell level, 
revealing cell-to-cell variability in genome organization.</li>
</ul>

<h2 id="biological-significance-of-3d-genome-organization">Biological Significance of 3D Genome Organization</h2>

<p>The three-dimensional arrangement of the genome is not random; it’s intricately organized into structures like loops, domains, and compartments:</p>
<ul>
  <li>Topologically Associating Domains (TADs): These are regions where DNA sequences interact more frequently with each other 
than with sequences outside the domain. TADs play a role in regulating gene expression by limiting enhancer-promoter interactions to within the domain.</li>
  <li>Chromatin Loops: Loops bring enhancers into contact with their target promoters, facilitating or repressing transcription.</li>
  <li>Compartments (A/B compartments): The genome segregates into active (A) and inactive (B) compartments, reflecting regions of open and closed chromatin, respectively.</li>
</ul>

<p>Disruptions in these structures can lead to misregulation of gene expression and are implicated in various diseases, including cancers and developmental disorders.</p>

<h2 id="applications-and-future-directions">Applications and Future Directions</h2>

<p>Understanding the 3D genome has profound implications:</p>
<ul>
  <li>Disease Research: Identifying structural variations that disrupt normal chromatin architecture can elucidate mechanisms of disease.</li>
  <li>Gene Therapy: Targeting specific chromatin interactions may offer novel therapeutic strategies.</li>
  <li>Developmental Biology: Studying how genome organization changes during development can provide insights into differentiation and organogenesis.</li>
</ul>

<p>As technologies advance, integrating 3D genomics with other omics data will enhance our understanding of gene regulation and cellular function.</p>

<p>In summary, 3D genomics offers a deeper layer of understanding beyond the linear DNA sequence, revealing how the spatial 
organization of the genome influences its function and regulation. This field holds promise for advancing our knowledge of 
biology and improving disease diagnosis and treatment.</p>

<p>
<h2><span style="color:#33a8ff">Enhance your skills with courses on genomics and bioinformatics</span></h2>
<ul>
  <li><a href="https://coursera.pxf.io/x9j72R" target="_blank">Genomic Data Science Specialization</a></li>
  <li><a href="https://coursera.pxf.io/KevDZy" target="_blank">Biology Meets Programming: Bioinformatics for Beginners</a></li>
  <li><a href="https://imp.i384100.net/DVggGj" target="_blank">Python for Genomic Data Science</a></li>
 <li><a href="https://imp.i384100.net/jW33Le" target="_blank">Bioinformatics Specialization</a></li>
  <li><a href="https://imp.i384100.net/b3okqx" target="_blank">Command Line Tools for Genomic Data Science</a></li>
  <li><a href="https://imp.i384100.net/Y9bk6J" target="_blank">Introduction to Genomic Technologies</a></li>
</ul>


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</p>]]></content><author><name>Renesh Bedre</name></author><category term="Bioinformatics" /><summary type="html"><![CDATA[Introduction to 3D Genomics]]></summary></entry><entry><title type="html">Samtools: Extract Reads from Specific Genomic Regions</title><link href="https://www.reneshbedre.com/blog/extract-reads-within-region-samtools.html" rel="alternate" type="text/html" title="Samtools: Extract Reads from Specific Genomic Regions" /><published>2024-04-03T00:00:00+00:00</published><updated>2024-04-04T00:00:00+00:00</updated><id>https://www.reneshbedre.com/blog/extract-reads-within-region-samtools</id><content type="html" xml:base="https://www.reneshbedre.com/blog/extract-reads-within-region-samtools.html"><![CDATA[<style>
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<p>In genomics and bioinformatics, <a href="https://www.htslib.org/">samtools</a> is widely used for extracting sequence reads from BAM file that fall within specific genomic regions.</p>

<p><code class="language-plaintext highlighter-rouge">samtools view</code> command can be used as shown below to extract reads from single or multiple regions from the BAM file.</p>

<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1"># extract reads from single region
</span><span class="n">samtools</span> <span class="n">view</span> <span class="o">-</span><span class="n">b</span> <span class="nb">input</span><span class="p">.</span><span class="n">bam</span> <span class="s">"chr:start-end"</span>  <span class="o">&gt;</span> <span class="n">regions</span><span class="p">.</span><span class="n">bam</span>

<span class="c1"># extract reads from multiple regions
</span><span class="n">samtools</span> <span class="n">view</span> <span class="o">-</span><span class="n">b</span> <span class="o">-</span><span class="n">L</span> <span class="n">region</span><span class="p">.</span><span class="n">bed</span> <span class="nb">input</span><span class="p">.</span><span class="n">bam</span> <span class="o">&gt;</span> <span class="n">regions</span><span class="p">.</span><span class="n">sam</span>
</code></pre></div></div>

<blockquote>
  <p>To extract reads from a BAM file using samtools, you need to first create an index file (<code class="language-plaintext highlighter-rouge">.bai</code>) for the BAM file using
<code class="language-plaintext highlighter-rouge">samtools index input.bam</code>. This index file is necessary for extracting reads within specific genomic regions.</p>
</blockquote>

<p>Before we begin, ensure that samtools is installed on your system. If not, you can
<a href="handle-analyze-bam-files.html">read this article on installing samtools</a>.</p>

<p>The following examples demonstrate how to extract reads within a specified region from the BAM file using
samtools.</p>

<h2 id="extract-reads-from-single-region">Extract reads from single region</h2>

<p>If you want to extract the reads from chromosome 1 spanning positions 3000 to 5000, you can use the samtools as:</p>

<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">samtools</span> <span class="n">view</span> <span class="o">-</span><span class="n">b</span>  <span class="nb">input</span><span class="p">.</span><span class="n">bam</span> <span class="s">"chr1:3000-5000"</span> <span class="o">&gt;</span> <span class="n">regions</span><span class="p">.</span><span class="n">bam</span>
</code></pre></div></div>

<p>Where, <code class="language-plaintext highlighter-rouge">-b</code> parameter specify the output should be in BAM format</p>

<p>The above command will create a new BAM file <code class="language-plaintext highlighter-rouge">regions.bam</code> which will contain the sequence reads that overlap the
given region (chr1:3000-5000).</p>

<p>If you want to include header in the output, you can add <code class="language-plaintext highlighter-rouge">-h</code> parameter to the above command.</p>

<p>If you want to create an output file in SAM format, you can use the following command.</p>

<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">samtools</span> <span class="n">view</span> <span class="nb">input</span><span class="p">.</span><span class="n">bam</span> <span class="s">"chr1:3000-5000"</span> <span class="o">&gt;</span> <span class="n">regions</span><span class="p">.</span><span class="n">sam</span>
</code></pre></div></div>

<h2 id="extract-reads-from-multiple-regions">Extract reads from multiple regions</h2>

<p>You can extract the sequence reads from the multiple regions using the samtools. You need to provide the multiple
region coordinates as a <a href="bedtools-genomics.html">BED file</a>.</p>

<p>Suppose you have multiple region coordinates given in the BED file as below,</p>

<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1"># region.bed
</span><span class="n">chr1</span>    <span class="mi">3000</span>    <span class="mi">5000</span>
<span class="n">chr10</span>   <span class="mi">5000</span>    <span class="mi">9000</span>
</code></pre></div></div>

<p>You can use this BED file to extract the sequences reads that overlaps genomic regions in BED file.</p>

<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">samtools</span> <span class="n">view</span> <span class="o">-</span><span class="n">b</span> <span class="o">-</span><span class="n">L</span> <span class="n">region</span><span class="p">.</span><span class="n">bed</span> <span class="nb">input</span><span class="p">.</span><span class="n">bam</span> <span class="o">&gt;</span> <span class="n">regions</span><span class="p">.</span><span class="n">bam</span>
</code></pre></div></div>

<p>Where, <code class="language-plaintext highlighter-rouge">-b</code> parameter specifies the output should be in BAM format, <code class="language-plaintext highlighter-rouge">-L</code> parameter specifies to extract the reads 
overlapping genomic region given in BED file.</p>

<p>The above command will create a new file <code class="language-plaintext highlighter-rouge">regions.bam</code> which will contain reads overlapping the multiple regions given
in the BED file.</p>

<p>If you want to create an output file in SAM format, you can use the following command.</p>

<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">samtools</span> <span class="n">view</span> <span class="o">-</span><span class="n">L</span> <span class="n">region</span><span class="p">.</span><span class="n">bed</span> <span class="nb">input</span><span class="p">.</span><span class="n">bam</span> <span class="o">&gt;</span> <span class="n">regions</span><span class="p">.</span><span class="n">sam</span>
</code></pre></div></div>

<p>
<h2><span style="color:#33a8ff">Enhance your skills with courses on genomics and bioinformatics</span></h2>
<ul>
  <li><a href="https://coursera.pxf.io/x9j72R" target="_blank">Genomic Data Science Specialization</a></li>
  <li><a href="https://coursera.pxf.io/KevDZy" target="_blank">Biology Meets Programming: Bioinformatics for Beginners</a></li>
  <li><a href="https://imp.i384100.net/DVggGj" target="_blank">Python for Genomic Data Science</a></li>
 <li><a href="https://imp.i384100.net/jW33Le" target="_blank">Bioinformatics Specialization</a></li>
  <li><a href="https://imp.i384100.net/b3okqx" target="_blank">Command Line Tools for Genomic Data Science</a></li>
  <li><a href="https://imp.i384100.net/Y9bk6J" target="_blank">Introduction to Genomic Technologies</a></li>
</ul>


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</p>]]></content><author><name>Renesh Bedre</name></author><category term="Bioinformatics" /><summary type="html"><![CDATA[Learn how to extract reads from BAM files that fall within a specified region with samtools]]></summary></entry><entry><title type="html">Samtools: Extract Mapped and Unmapped Paired-end Reads</title><link href="https://www.reneshbedre.com/blog/filter-paired-reads-samtools.html" rel="alternate" type="text/html" title="Samtools: Extract Mapped and Unmapped Paired-end Reads" /><published>2024-03-29T00:00:00+00:00</published><updated>2024-03-25T00:00:00+00:00</updated><id>https://www.reneshbedre.com/blog/filter-paired-reads-samtools</id><content type="html" xml:base="https://www.reneshbedre.com/blog/filter-paired-reads-samtools.html"><![CDATA[<style>
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<p><a href="https://www.htslib.org/">Samtools</a> can be used for extracting mapped and unmapped paired-end reads from SAM/BAM files.</p>

<p>Unlike single-end read filtering, you need to consider whether the paired-end reads are properly paired and both reads 
of the pairs are mapped while extracting mapped and unmapped paired-end reads.</p>

<blockquote>
  <p>The paired-end reads are <strong>properly paired (concordant alignments)</strong> when both of the reads are mapped to the reference genome in the correct orientation
as per library preparation protocol  (e.g.first read on the forward strand and second read on the reverse strand).
In addition, the properly paired reads will have the expected insert size (distance between the mapped positions of the read pair).</p>
</blockquote>

<p>You can use the <code class="language-plaintext highlighter-rouge">samtools view</code> command with <code class="language-plaintext highlighter-rouge">-F</code> or <code class="language-plaintext highlighter-rouge">-f</code> parameter and associated flag values for
extracting mapped and unmapped paired-end reads from SAM/BAM files.</p>

<p>If you have not installed samtools,<a href="handle-analyze-bam-files.html">read this article on installing samtools</a>.</p>

<p>The following examples demonstrate how to extract mapped and unmapped paired-end reads from the BAM file using
samtools.</p>

<h2 id="extract-paired-reads-mapped-in-the-proper-pair">Extract paired reads mapped in the proper pair</h2>

<p>You can use the following commands to extract the reads mapped in the proper pair.</p>

<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">samtools</span> <span class="n">view</span> <span class="o">-</span><span class="n">b</span> <span class="o">-</span><span class="n">f</span> <span class="mi">2</span> <span class="nb">input</span><span class="p">.</span><span class="n">bam</span> <span class="o">&gt;</span> <span class="n">mapped</span><span class="p">.</span><span class="n">bam</span>
</code></pre></div></div>

<p>Where, <code class="language-plaintext highlighter-rouge">-b</code> parameter specifies  the output should be in BAM format, <code class="language-plaintext highlighter-rouge">-f 2</code> parameter specifies  to <strong>extract paired-end reads 
mapped in proper pair</strong>.</p>

<p>The above command will create a new BAM file <code class="language-plaintext highlighter-rouge">mapped.bam</code> which will contain paired-end reads 
mapped in proper pair.</p>

<p>If you want to create an output file in SAM format, you can use the following command.</p>

<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">samtools</span> <span class="n">view</span> <span class="o">-</span><span class="n">f</span> <span class="mi">2</span> <span class="nb">input</span><span class="p">.</span><span class="n">bam</span> <span class="o">&gt;</span> <span class="n">mapped</span><span class="p">.</span><span class="n">sam</span>
</code></pre></div></div>

<h2 id="extract-paired-reads-where-one-read-is-mapped-and-the-other-is-unmapped">Extract paired reads where one read is mapped and the other is unmapped</h2>

<p>You can use the following commands to extract the paired-end reads where one read is mapped and the other read is unmapped.</p>

<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">samtools</span> <span class="n">view</span> <span class="o">-</span><span class="n">b</span> <span class="o">-</span><span class="n">F</span> <span class="mi">4</span> <span class="o">-</span><span class="n">f</span> <span class="mi">8</span> <span class="nb">input</span><span class="p">.</span><span class="n">bam</span> <span class="o">&gt;</span> <span class="n">mapped</span><span class="p">.</span><span class="n">bam</span>
</code></pre></div></div>

<p>Where, <code class="language-plaintext highlighter-rouge">-b</code> parameter specifies the output should be in BAM format, <code class="language-plaintext highlighter-rouge">-F 4</code> parameter specifies to <strong>extract paired-end reads that 
are mapped</strong>, and <code class="language-plaintext highlighter-rouge">-f 8</code> parameter specifies to <strong>extract paired-end reads where one of the reads in the pair is unmapped</strong>.</p>

<p>The above command will create a new file <code class="language-plaintext highlighter-rouge">mapped.bam</code> which will contain mapped paired-end reads where one of the reads in the pair
is unmapped i.e. extract paired reads where one of the read is mapped and other is unmapped.</p>

<p>If you want to create an output file in SAM format, you can use the following command.</p>

<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">samtools</span> <span class="n">view</span> <span class="o">-</span><span class="n">F</span> <span class="mi">4</span> <span class="o">-</span><span class="n">f</span> <span class="mi">8</span> <span class="nb">input</span><span class="p">.</span><span class="n">bam</span> <span class="o">&gt;</span> <span class="n">mapped</span><span class="p">.</span><span class="n">sam</span>
</code></pre></div></div>

<h2 id="extract-paired-end-reads-where-both-reads-are-not-mapped">Extract paired-end reads where both reads are not mapped</h2>

<p>You can use the following commands to extract the paired-end reads where both reads are not mapped to the reference genome.</p>

<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">samtools</span> <span class="n">view</span> <span class="o">-</span><span class="n">b</span> <span class="o">-</span><span class="n">f</span> <span class="mi">12</span> <span class="nb">input</span><span class="p">.</span><span class="n">bam</span> <span class="o">&gt;</span> <span class="n">unmapped</span><span class="p">.</span><span class="n">bam</span>
</code></pre></div></div>

<p>Where, <code class="language-plaintext highlighter-rouge">-b</code> parameter specifies the output should be in BAM format, <code class="language-plaintext highlighter-rouge">-f 12</code> parameter specifies to <strong>extract the paired-end 
reads where both reads of the pairs are not mapped</strong>.</p>

<p>The above command will create a new file <code class="language-plaintext highlighter-rouge">unmapped.bam</code> which will contain paired-end 
reads where both reads of the pairs are not mapped.</p>

<p>Please <a href="filter-paired-reads-samtools.html">read my article on extracting single-end reads from SAM/BAM file</a>.</p>

<p>
<h2><span style="color:#33a8ff">Enhance your skills with courses on genomics and bioinformatics</span></h2>
<ul>
  <li><a href="https://coursera.pxf.io/x9j72R" target="_blank">Genomic Data Science Specialization</a></li>
  <li><a href="https://coursera.pxf.io/KevDZy" target="_blank">Biology Meets Programming: Bioinformatics for Beginners</a></li>
  <li><a href="https://imp.i384100.net/DVggGj" target="_blank">Python for Genomic Data Science</a></li>
 <li><a href="https://imp.i384100.net/jW33Le" target="_blank">Bioinformatics Specialization</a></li>
  <li><a href="https://imp.i384100.net/b3okqx" target="_blank">Command Line Tools for Genomic Data Science</a></li>
  <li><a href="https://imp.i384100.net/Y9bk6J" target="_blank">Introduction to Genomic Technologies</a></li>
</ul>


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</p>]]></content><author><name>Renesh Bedre</name></author><category term="Bioinformatics" /><summary type="html"><![CDATA[Learn how to filter mapped and unmapped paired-end reads with Samtools]]></summary></entry><entry><title type="html">Samtools: How to Filter Mapped and Unmapped Reads</title><link href="https://www.reneshbedre.com/blog/filter-reads-samtools.html" rel="alternate" type="text/html" title="Samtools: How to Filter Mapped and Unmapped Reads" /><published>2024-03-25T00:00:00+00:00</published><updated>2024-03-25T00:00:00+00:00</updated><id>https://www.reneshbedre.com/blog/filter-reads-samtools</id><content type="html" xml:base="https://www.reneshbedre.com/blog/filter-reads-samtools.html"><![CDATA[<style>
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<p><a href="https://www.htslib.org/">Samtools</a> is a suite of utilities commonly used in analyzing the aligned sequence data in the SAM (Sequence Alignment/Map) 
and BAM (Binary Alignment/Map) formats in bioinformatics and genomics analysis.</p>

<p><code class="language-plaintext highlighter-rouge">samtools view</code> command with <code class="language-plaintext highlighter-rouge">-F</code> or <code class="language-plaintext highlighter-rouge">-f</code> parameter and a flag value is typically used in the filtering mapped and unmapped sequence 
reads from SAM/BAM files.</p>

<p>The flag value is a numerical value that encodes various properties of each read alignment. For example,
the flag value of 4 (0x4) indicates that the sequence read does not have a valid alignment to the reference genome (unmapped sequence
reads).</p>

<p>If you have not installed samtools,<a href="handle-analyze-bam-files.html">read this article on installing samtools</a>.</p>

<p>The following examples demonstrate how to filter mapped and unmapped sequence reads from the BAM file using
samtools.</p>

<h2 id="filter-unmapped-sequence-reads">Filter unmapped sequence reads</h2>

<p>You can use the following commands to filter the unmapped sequence  reads from the BAM file using Samtools.</p>

<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">samtools</span> <span class="n">view</span> <span class="o">-</span><span class="n">b</span> <span class="o">-</span><span class="n">f</span> <span class="mi">4</span> <span class="nb">input</span><span class="p">.</span><span class="n">bam</span> <span class="o">&gt;</span> <span class="n">unmapped</span><span class="p">.</span><span class="n">bam</span>
</code></pre></div></div>

<p>Where, <code class="language-plaintext highlighter-rouge">-b</code> parameter specify the output should be in BAM format, <code class="language-plaintext highlighter-rouge">-f 4</code> parameter specifies  to <strong>filter the unmapped
sequence reads</strong> (retain only unmapped sequence reads in <code class="language-plaintext highlighter-rouge">unmapped.bam</code>).</p>

<p>The above command will create a new BAM file <code class="language-plaintext highlighter-rouge">unmapped.bam</code> which will contain only unmapped reads from the input BAM file.</p>

<p>If you want to create an output file in SAM format, you can use the following command.</p>

<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">samtools</span> <span class="n">view</span> <span class="o">-</span><span class="n">f</span> <span class="mi">4</span> <span class="nb">input</span><span class="p">.</span><span class="n">bam</span> <span class="o">&gt;</span> <span class="n">unmapped</span><span class="p">.</span><span class="n">sam</span>
</code></pre></div></div>

<p>The above command will create a new SAM file <code class="language-plaintext highlighter-rouge">unmapped.sam</code> which will contain only unmapped reads from the input BAM file.</p>

<p>These commands will work for the single-end reads. While filtering mapped and unmapped sequence reads for paired-end data, 
it is also important to consider whether the paired-end reads are properly paired. Please <a href="filter-paired-reads-samtools.html">read my article on extracting
paired-end reads from SAM/BAM file</a>.</p>

<h2 id="filter-mapped-sequence-reads">Filter mapped sequence reads</h2>

<p>You can use the following commands to filter the mapped sequence reads from the BAM file using Samtools.</p>

<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">samtools</span> <span class="n">view</span> <span class="o">-</span><span class="n">b</span> <span class="o">-</span><span class="n">F</span> <span class="mi">4</span> <span class="nb">input</span><span class="p">.</span><span class="n">bam</span> <span class="o">&gt;</span> <span class="n">mapped</span><span class="p">.</span><span class="n">bam</span>
</code></pre></div></div>

<p>Where, <code class="language-plaintext highlighter-rouge">-b</code> parameter specifies the output should be in BAM format, <code class="language-plaintext highlighter-rouge">-F 4</code> parameter specifies to <strong>filter out the unmapped
sequence reads</strong> (retain only mapped sequence reads in <code class="language-plaintext highlighter-rouge">mapped.bam</code>).</p>

<p>The above command will create a new file <code class="language-plaintext highlighter-rouge">mapped.bam</code> which will contain only mapped reads from the input BAM file.</p>

<p>If you want to create an output file in SAM format, you can use the following command.</p>

<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">samtools</span> <span class="n">view</span> <span class="o">-</span><span class="n">F</span> <span class="mi">4</span> <span class="nb">input</span><span class="p">.</span><span class="n">bam</span> <span class="o">&gt;</span> <span class="n">mapped</span><span class="p">.</span><span class="n">sam</span>
</code></pre></div></div>

<p>The above command will create a new SAM file <code class="language-plaintext highlighter-rouge">mapped.sam</code> which will contain only mapped reads from the input BAM file.</p>

<p>These commands will work for the single-end reads. While filtering mapped and unmapped sequence reads for paired-end data, 
it is also important to consider whether the paired-end reads are properly paired. Please <a href="filter-paired-reads-samtools.html">read my article on extracting
paired-end reads from SAM/BAM file</a>.</p>

<p>
<h2><span style="color:#33a8ff">Enhance your skills with courses on genomics and bioinformatics</span></h2>
<ul>
  <li><a href="https://coursera.pxf.io/x9j72R" target="_blank">Genomic Data Science Specialization</a></li>
  <li><a href="https://coursera.pxf.io/KevDZy" target="_blank">Biology Meets Programming: Bioinformatics for Beginners</a></li>
  <li><a href="https://imp.i384100.net/DVggGj" target="_blank">Python for Genomic Data Science</a></li>
 <li><a href="https://imp.i384100.net/jW33Le" target="_blank">Bioinformatics Specialization</a></li>
  <li><a href="https://imp.i384100.net/b3okqx" target="_blank">Command Line Tools for Genomic Data Science</a></li>
  <li><a href="https://imp.i384100.net/Y9bk6J" target="_blank">Introduction to Genomic Technologies</a></li>
</ul>


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</p>]]></content><author><name>Renesh Bedre</name></author><category term="Bioinformatics" /><summary type="html"><![CDATA[Learn how to filter mapped and unmapped sequence reads with Samtools]]></summary></entry></feed>