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<h1 id="How-to-approach-a-real-world-problem-from-the-beginning">How to approach a real world problem from the beginning<a class="anchor-link" href="#How-to-approach-a-real-world-problem-from-the-beginning">&#182;</a></h1>
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<p>Things to mention about this talk:</p>
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<li>We are going to integrate a lot of the pieces we've been talking about.</li>
<li>Input problem, output functional script</li>
<li>The script is going to be written in Python, but don't worry about syntax. It doesn't matter.</li>
<li>Don't bother taking notes. Just think.</li>
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<h1 id="The-Problem">The Problem<a class="anchor-link" href="#The-Problem">&#182;</a></h1>
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<p>Here's what your boss tells you:</p>
<p>"I need to know the genomic spans of all the transcripts in this file. More specifically, I want only canonical transcripts, which are designated in the transcript name by ending in 01. Also, the graph the spans sorted by length.</p>
<p>The file is attached."</p>
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<p>It sounds like a reasonable and fairly straightforward task, but how do you actually go about tackling the problem?</p>
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<p>Opening the file's a good start:</p>
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##gff-version 3
#description: evidence-based annotation of the mouse genome (GRCm38), version 5 (Ensembl 80) - long non-coding RNAs
#provider: GENCODE
#contact: [email protected]
#format: gff3
#date: 2015-05-13
##sequence-region chr1 1 195471971
chr1 HAVANA gene 3073253 3074322 . + . ID=ENSMUSG00000102693.1;gene_id=ENSMUSG00000102693.1;gene_type=TEC;gene_status=KNOWN;gene_name=4933401J01Rik;level=2;havana_gene=OTTMUSG00000049935.1
chr1 HAVANA transcript 3073253 3074322 . + . ID=ENSMUST00000193812.1;Parent=ENSMUSG00000102693.1;gene_id=ENSMUSG00000102693.1;transcript_id=ENSMUST00000193812.1;gene_type=TEC;gene_status=KNOWN;gene_name=4933401J01Rik;transcript_type=TEC;transcript_status=KNOWN;transcript_name=4933401J01Rik-001;level=2;tag=basic;havana_gene=OTTMUSG00000049935.1;havana_transcript=OTTMUST00000127109.1
chr1 HAVANA exon 3073253 3074322 . + . ID=exon:ENSMUST00000193812.1:1;Parent=ENSMUST00000193812.1;gene_id=ENSMUSG00000102693.1;transcript_id=ENSMUST00000193812.1;gene_type=TEC;gene_status=KNOWN;gene_name=4933401J01Rik;transcript_type=TEC;transcript_status=KNOWN;transcript_name=4933401J01Rik-001;exon_number=1;exon_id=ENSMUSE00001343744.1;level=2;havana_gene=OTTMUSG00000049935.1;havana_transcript=OTTMUST00000127109.1;tag=basic
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<p>It's pretty ugly, right? It's a bit nicer if you open is in something like excel, but there are still 126,218 lines to go through.</p>
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<p>The first place to start is by breaking up the task into bite size chucks, and turning those chunks into pseduocode.</p>
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<p>What specific steps, and in what order, are we going to need to complete for this task?</p>
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<ol>
<li>Read this file to get information about the transcripts</li>
<li>Filter transcripts for 01 endings</li>
<li>Find the span of each transcript</li>
<li>Plot the spans by length</li>
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<p>Warning: Moving forward, we aren't focusing on optimization or doing things the way a professional developer would. The point of this exercise is to get the job done with the tools we have, and do it in a timely fashion (i.e. don't spend forever researching how to do each step).</p>
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<h1 id="Read-this-file-to-get-information-about-the-transcripts">Read this file to get information about the transcripts<a class="anchor-link" href="#Read-this-file-to-get-information-about-the-transcripts">&#182;</a></h1>
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<p>Each of these high level ideas should be further broken down into low level pseudocode. Write these ideas out before you touch the keyboard, and then again as comments as you're going through the script.</p>
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<li>Open the file</li>
<li>Ignore the header lines</li>
<li>Loop through the rest of the lines</li>
<li>Check to see if the 3rd column is a transcript</li>
<li>If it is, save that line</li>
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<p>Where are we going to save that line?</p>
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<p>If you wanted, you could write it to a new file, and check to make sure everything looks good there. We want to keep the program running though, so we need to have a place to store the lines we want to keep ready to go. Let's add that to our to-do list.</p>
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<li>Open the file</li>
<li>Ignore the header lines</li>
<li>Place to store transcript lines</li>
<li>Loop through the rest of the lines</li>
<li>Check to see if the 3rd column is a transcript</li>
<li>If it is, save that line</li>
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<div class=" highlight hl-ipython2"><pre><span class="c">#Open the file</span>
<span class="n">folder</span> <span class="o">=</span> <span class="s">&quot;C:/Users/Jessime/Research/&quot;</span>
<span class="n">file_path</span> <span class="o">=</span> <span class="n">folder</span><span class="o">+</span><span class="s">&quot;gencode.v22.long_noncoding_RNAs.gff3&quot;</span>
<span class="n">gff3</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span>
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<p>That's pretty straightforward, just open the file designated by the path. Don't get tripped up by the string concatenation. That's just a preference of mine to make things more readable.</p>
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<div class=" highlight hl-ipython2"><pre><span class="c">#Ignore the header lines</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">7</span><span class="p">):</span>
<span class="k">print</span> <span class="n">gff3</span><span class="o">.</span><span class="n">readline</span><span class="p">()</span>
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<pre>##gff-version 3
#description: evidence-based annotation of the human genome (GRCh38), version 22 (Ensembl 79) - long non-coding RNAs
#provider: GENCODE
#contact: [email protected]
#format: gff3
#date: 2015-03-06
##sequence-region chr1 1 248956422
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<p>You don't have to print them, but it lets you know that you've moved past all of the header lines and you're where you want to be.</p>
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<div class=" highlight hl-ipython2"><pre><span class="c">#Place to store transcript lines</span>
<span class="n">transcripts</span> <span class="o">=</span> <span class="p">[]</span>
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<p>A simple list of the lines we want to keep will do the trick</p>
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<p>Here's where things start to become a little more complicated. The next 3 comments are going to be executed in a for loop.</p>
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<div class=" highlight hl-ipython2"><pre><span class="c">#Loop through the rest of the lines</span>
<span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">gff3</span><span class="p">:</span>
<span class="n">columns</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">()</span> <span class="c">#Google &#39;get column of text python&#39;</span>
<span class="n">line_type</span> <span class="o">=</span> <span class="n">columns</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
<span class="c">#Check to see if the 3rd column is a transcript</span>
<span class="k">if</span> <span class="n">line_type</span> <span class="o">==</span> <span class="s">&quot;transcript&quot;</span><span class="p">:</span>
<span class="c">#If it is, save that line</span>
<span class="n">transcripts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">line</span><span class="p">)</span>
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<p>Let's make sure our list has been populated.</p>
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<div class=" highlight hl-ipython2"><pre><span class="nb">len</span><span class="p">(</span><span class="n">transcripts</span><span class="p">)</span>
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<pre>27670</pre>
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<p>Great, now we have our transcripts. What was the next step again?</p>
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<h1 id="Filter-transcripts-for-01-endings">Filter transcripts for 01 endings<a class="anchor-link" href="#Filter-transcripts-for-01-endings">&#182;</a></h1>
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<p>Again, we need to break this down into simpler steps. My initial thought is, "This shouldn't be too hard, just:"</p>
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<li>Loop through each transcript</li>
<li>Check if the name ends in 01</li>
<li>If it does, save that line</li>
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<p>This structure looks really familiar. Let's go ahead and just add in our container.</p>
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<li>Place to store 01 transcript lines</li>
<li>Loop through each transcript</li>
<li>Check if the name ends in 01</li>
<li>If it does, save that line</li>
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<p>But wait, how do you go about checking if the name ends in 01? Let's print a couple lines to check what they look like again.</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">print</span> <span class="n">transcripts</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">print</span> <span class="n">transcripts</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
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<pre>chr1 HAVANA transcript 29554 31097 . + . ID=ENST00000473358.1;Parent=ENSG00000243485.3;gene_id=ENSG00000243485.3;transcript_id=ENST00000473358.1;gene_type=lincRNA;gene_status=NOVEL;gene_name=RP11-34P13.3;transcript_type=lincRNA;transcript_status=KNOWN;transcript_name=RP11-34P13.3-001;level=2;transcript_support_level=5;tag=not_best_in_genome_evidence,basic;havana_gene=OTTHUMG00000000959.2;havana_transcript=OTTHUMT00000002840.1
chr1 HAVANA transcript 30267 31109 . + . ID=ENST00000469289.1;Parent=ENSG00000243485.3;gene_id=ENSG00000243485.3;transcript_id=ENST00000469289.1;gene_type=lincRNA;gene_status=NOVEL;gene_name=RP11-34P13.3;transcript_type=lincRNA;transcript_status=KNOWN;transcript_name=RP11-34P13.3-002;level=2;transcript_support_level=5;tag=not_best_in_genome_evidence;havana_gene=OTTHUMG00000000959.2;havana_transcript=OTTHUMT00000002841.2
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<p>It looks like transcript_name is the section we're after, but how do we "get to it"?</p>
<p>WARNING: I probably don't have to say this, but I'm going to anyway: in real life, don't just guess about what you're processing. You'll eventually get it wrong and potentially waste a lot of time. Save yourself the trouble and ask someone if "transcript_name" is the field you need to process.</p>
<p>Grabbing the index of those values isn't going to work because you have no way of knowing that they're going to stay the same for other lines. The best way to go about this is whittle away pieces until you have only the part you want. Let's give it a shot:</p>
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<div class=" highlight hl-ipython2"><pre><span class="n">practice_line</span> <span class="o">=</span> <span class="n">transcripts</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">print</span> <span class="n">practice_line</span>
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<pre>chr1 HAVANA transcript 29554 31097 . + . ID=ENST00000473358.1;Parent=ENSG00000243485.3;gene_id=ENSG00000243485.3;transcript_id=ENST00000473358.1;gene_type=lincRNA;gene_status=NOVEL;gene_name=RP11-34P13.3;transcript_type=lincRNA;transcript_status=KNOWN;transcript_name=RP11-34P13.3-001;level=2;transcript_support_level=5;tag=not_best_in_genome_evidence,basic;havana_gene=OTTHUMG00000000959.2;havana_transcript=OTTHUMT00000002840.1
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<div class=" highlight hl-ipython2"><pre><span class="n">p_columns</span> <span class="o">=</span> <span class="n">practice_line</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
<span class="k">print</span> <span class="n">p_columns</span>
<span class="k">print</span> <span class="s">&quot;&quot;</span>
<span class="k">print</span> <span class="nb">len</span><span class="p">(</span><span class="n">p_columns</span><span class="p">)</span>
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<pre>[&apos;chr1&apos;, &apos;HAVANA&apos;, &apos;transcript&apos;, &apos;29554&apos;, &apos;31097&apos;, &apos;.&apos;, &apos;+&apos;, &apos;.&apos;, &apos;ID=ENST00000473358.1;Parent=ENSG00000243485.3;gene_id=ENSG00000243485.3;transcript_id=ENST00000473358.1;gene_type=lincRNA;gene_status=NOVEL;gene_name=RP11-34P13.3;transcript_type=lincRNA;transcript_status=KNOWN;transcript_name=RP11-34P13.3-001;level=2;transcript_support_level=5;tag=not_best_in_genome_evidence,basic;havana_gene=OTTHUMG00000000959.2;havana_transcript=OTTHUMT00000002840.1&apos;]
9
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<p>For whatever reason, this last pieces of information is seperated by semi-colons, and didn't get split up properly. We can grab that for further processing.</p>
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<div class=" highlight hl-ipython2"><pre><span class="n">info_column</span> <span class="o">=</span> <span class="n">p_columns</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="k">print</span> <span class="n">info_column</span>
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<pre>ID=ENST00000473358.1;Parent=ENSG00000243485.3;gene_id=ENSG00000243485.3;transcript_id=ENST00000473358.1;gene_type=lincRNA;gene_status=NOVEL;gene_name=RP11-34P13.3;transcript_type=lincRNA;transcript_status=KNOWN;transcript_name=RP11-34P13.3-001;level=2;transcript_support_level=5;tag=not_best_in_genome_evidence,basic;havana_gene=OTTHUMG00000000959.2;havana_transcript=OTTHUMT00000002840.1
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<div class=" highlight hl-ipython2"><pre><span class="n">semi_columns</span> <span class="o">=</span> <span class="n">info_column</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">&quot;;&quot;</span><span class="p">)</span>
<span class="k">print</span> <span class="n">semi_columns</span>
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<pre>[&apos;ID=ENST00000473358.1&apos;, &apos;Parent=ENSG00000243485.3&apos;, &apos;gene_id=ENSG00000243485.3&apos;, &apos;transcript_id=ENST00000473358.1&apos;, &apos;gene_type=lincRNA&apos;, &apos;gene_status=NOVEL&apos;, &apos;gene_name=RP11-34P13.3&apos;, &apos;transcript_type=lincRNA&apos;, &apos;transcript_status=KNOWN&apos;, &apos;transcript_name=RP11-34P13.3-001&apos;, &apos;level=2&apos;, &apos;transcript_support_level=5&apos;, &apos;tag=not_best_in_genome_evidence,basic&apos;, &apos;havana_gene=OTTHUMG00000000959.2&apos;, &apos;havana_transcript=OTTHUMT00000002840.1&apos;]
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<p>We're making progress...</p>
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<div class=" highlight hl-ipython2"><pre><span class="n">transcript_name_column</span> <span class="o">=</span> <span class="n">semi_columns</span><span class="p">[</span><span class="mi">9</span><span class="p">]</span>
<span class="k">print</span> <span class="n">transcript_name_column</span>
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<pre>transcript_name=RP11-34P13.3-001
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<p>We're almost done now...</p>
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<div class=" highlight hl-ipython2"><pre><span class="n">name_split</span> <span class="o">=</span> <span class="n">transcript_name_column</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">&quot;=&quot;</span><span class="p">)</span>
<span class="k">print</span> <span class="n">name_split</span>
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<pre>[&apos;transcript_name&apos;, &apos;RP11-34P13.3-001&apos;]
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<span class="k">print</span> <span class="n">name</span>
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<pre>RP11-34P13.3-001
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<p>So what does all of that look like together?</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">def</span> <span class="nf">get_name</span><span class="p">(</span><span class="n">line</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;returns the transcript name of an RNA from a line of a GFF3 file&quot;&quot;&quot;</span>
<span class="n">columns</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
<span class="n">info_column</span> <span class="o">=</span> <span class="n">columns</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="n">semi_columns</span> <span class="o">=</span> <span class="n">info_column</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">&quot;;&quot;</span><span class="p">)</span>
<span class="n">transcript_name_column</span> <span class="o">=</span> <span class="n">semi_columns</span><span class="p">[</span><span class="mi">9</span><span class="p">]</span>
<span class="n">name_split</span> <span class="o">=</span> <span class="n">transcript_name_column</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">&quot;=&quot;</span><span class="p">)</span>
<span class="n">name</span> <span class="o">=</span> <span class="n">name_split</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="k">return</span> <span class="n">name</span>
<span class="k">print</span> <span class="n">practice_line</span>
<span class="n">name</span> <span class="o">=</span> <span class="n">get_name</span><span class="p">(</span><span class="n">practice_line</span><span class="p">)</span>
<span class="k">print</span> <span class="n">name</span>
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<pre>chr1 HAVANA transcript 29554 31097 . + . ID=ENST00000473358.1;Parent=ENSG00000243485.3;gene_id=ENSG00000243485.3;transcript_id=ENST00000473358.1;gene_type=lincRNA;gene_status=NOVEL;gene_name=RP11-34P13.3;transcript_type=lincRNA;transcript_status=KNOWN;transcript_name=RP11-34P13.3-001;level=2;transcript_support_level=5;tag=not_best_in_genome_evidence,basic;havana_gene=OTTHUMG00000000959.2;havana_transcript=OTTHUMT00000002840.1
RP11-34P13.3-001
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<p>We finally have the name, what do we want to do with it again? We can integrate these steps into our original pseudocode.</p>
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<li>Place to store 01 transcript lines</li>
<li>Loop through each transcript</li>
<li>Extract the name from the line<ol>
<li>Split the line into a list</li>
<li>Keep the last element/column</li>
<li>Split that column up by semi-colons</li>
<li>Keep the nineth element/column, that has the transcript name</li>
<li>Split that column by the equal sign</li>
<li>Keep the name on the right side of the equal sign</li>
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<li>Check if the name ends in 01</li>
<li>If it does, save that line</li>
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<div class=" highlight hl-ipython2"><pre><span class="c">#Place to store 01 transcript lines</span>
<span class="n">transcripts01</span> <span class="o">=</span> <span class="p">[]</span>
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<div class=" highlight hl-ipython2"><pre><span class="c">#Loop through each transcript</span>
<span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">transcripts</span><span class="p">:</span>
<span class="c">#Extract the name from the line</span>
<span class="n">columns</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
<span class="n">info_column</span> <span class="o">=</span> <span class="n">columns</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="n">semi_columns</span> <span class="o">=</span> <span class="n">info_column</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">&quot;;&quot;</span><span class="p">)</span>
<span class="n">transcript_name_column</span> <span class="o">=</span> <span class="n">semi_columns</span><span class="p">[</span><span class="mi">9</span><span class="p">]</span>
<span class="n">name_split</span> <span class="o">=</span> <span class="n">transcript_name_column</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">&quot;=&quot;</span><span class="p">)</span>
<span class="n">name</span> <span class="o">=</span> <span class="n">name_split</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="c">#Check if name ends in 01</span>
<span class="k">if</span> <span class="n">name</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">:]</span> <span class="o">==</span> <span class="s">&quot;01&quot;</span><span class="p">:</span>
<span class="c">#If it does, save that line</span>
<span class="n">transcripts01</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">line</span><span class="p">)</span>
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<div class=" highlight hl-ipython2"><pre><span class="nb">len</span><span class="p">(</span><span class="n">transcripts01</span><span class="p">)</span>
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<pre>15954</pre>
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<p>We're making some significant progress, since we now have only the lines we care about. Now let's get those spans.</p>
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<h1 id="Find-the-span-of-each-transcript">Find the span of each transcript<a class="anchor-link" href="#Find-the-span-of-each-transcript">&#182;</a></h1>
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<p>Knowing or getting the span of each transcript is a little vaugue. To clarify further, 'span' is just going to mean the end-start chromosome positions which are found in the 4th and 5th columns. We're going to want the information in two forms. First, it would be nice to be able to type the name of a given transcript and immediately know it's span. Next, we're going to need a sorted list of the spans that can be used for our plot. Given these clearer instructions, we can make some psuedocode.</p>
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<li>Make containers for the spans</li>
<li>Loop through all of the 01 transcripts</li>
<li>Find the span</li>
<li>Put the name&amp;span in one container and just the span in the other</li>
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<div class=" highlight hl-ipython2"><pre><span class="c">#Make containers for the spans</span>
<span class="n">spans</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">span_lookup</span> <span class="o">=</span> <span class="p">{}</span> <span class="c">#A dictionary will be perfect for our purposes here</span>
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<div class=" highlight hl-ipython2"><pre><span class="c">#Loop through all of the 01 transcripts</span>
<span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">transcripts01</span><span class="p">:</span>
<span class="c">#Find the span</span>
<span class="n">columns</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
<span class="n">start</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">columns</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
<span class="n">end</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">columns</span><span class="p">[</span><span class="mi">4</span><span class="p">])</span>
<span class="n">span</span> <span class="o">=</span> <span class="n">end</span> <span class="o">-</span> <span class="n">start</span>
<span class="c">#Populate the dictionary</span>
<span class="n">name</span> <span class="o">=</span> <span class="n">get_name</span><span class="p">(</span><span class="n">line</span><span class="p">)</span>
<span class="n">span_lookup</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">span</span>
<span class="c">#Populate the list</span>
<span class="n">spans</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">span</span><span class="p">)</span>
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<p>Let's double check to make sure our lookup table is working by inputting some of the transcript names</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">print</span> <span class="n">span_lookup</span><span class="p">[</span><span class="s">&quot;RP11-72L22.1-001&quot;</span><span class="p">]</span>
<span class="k">print</span> <span class="n">span_lookup</span><span class="p">[</span><span class="s">&quot;XIST-001&quot;</span><span class="p">]</span>
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<pre>170391
32097
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<p>We've just completed one of the two part's of the job. All that's left now to do is plot the spans.</p>
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<h1 id="Plot-the-spans-by-length">Plot the spans by length<a class="anchor-link" href="#Plot-the-spans-by-length">&#182;</a></h1>
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<p>Python is a general purpose language used for everything from controlling microdevices to web development. A vast majority of it's users are never going to have to plot anything, ever. That's why libraries (or modules) exist. At this point, you're probably going to have to do a bit of Googling to see what plotting libraries Python as, and what fits your needs best</p>
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<p>Simply searching "scatter plot python" will give you:</p>
<p><a href="http://matplotlib.org/examples/shapes_and_collections/scatter_demo.html">http://matplotlib.org/examples/shapes_and_collections/scatter_demo.html</a></p>
<p>as the first link. This resource will tell you what you need to make a scatter plot.</p>
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<p>The first thing we have to do is import the library.</p>
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<div class=" highlight hl-ipython2"><pre><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
<span class="c">#Ignore this line, it&#39;s just for the presentation</span>
<span class="o">%</span><span class="k">matplotlib</span> inline
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<p>If we look at the link I mentioned above, we'll see that we need a list of x coordinates and another list of y coordinates. Which makes sense for a scatter plot, right?</p>
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<div class=" highlight hl-ipython2"><pre><span class="c">#Coordinate lists for the scatter plot</span>
<span class="n">x</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">spans</span><span class="p">))</span>
<span class="n">y</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">spans</span><span class="p">)</span>
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<div class=" highlight hl-ipython2"><pre><span class="c">#Generate scatter plot</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<h1 id="Putting-all-the-pieces-together">Putting all the pieces together<a class="anchor-link" href="#Putting-all-the-pieces-together">&#182;</a></h1>
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<div class="prompt input_prompt">In&nbsp;[32]:</div>
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<div class=" highlight hl-ipython2"><pre><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
<span class="c">#Ignore this line, it&#39;s just for the presentation</span>
<span class="o">%</span><span class="k">matplotlib</span> inline
<span class="k">def</span> <span class="nf">get_name</span><span class="p">(</span><span class="n">line</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;returns the transcript name of an RNA from a line of a GFF3 file&quot;&quot;&quot;</span>
<span class="n">columns</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
<span class="n">info_column</span> <span class="o">=</span> <span class="n">columns</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="n">semi_columns</span> <span class="o">=</span> <span class="n">info_column</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">&quot;;&quot;</span><span class="p">)</span>
<span class="n">transcript_name_column</span> <span class="o">=</span> <span class="n">semi_columns</span><span class="p">[</span><span class="mi">9</span><span class="p">]</span>
<span class="n">name_split</span> <span class="o">=</span> <span class="n">transcript_name_column</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">&quot;=&quot;</span><span class="p">)</span>
<span class="n">name</span> <span class="o">=</span> <span class="n">name_split</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="k">return</span> <span class="n">name</span>
<span class="c">#Open the file</span>
<span class="n">folder</span> <span class="o">=</span> <span class="s">&quot;C:/Users/Jessime/Research/&quot;</span>
<span class="n">file_path</span> <span class="o">=</span> <span class="n">folder</span><span class="o">+</span><span class="s">&quot;gencode.v22.long_noncoding_RNAs.gff3&quot;</span>
<span class="n">gff3</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span>
<span class="c">#Ignore the header lines</span>
<span class="k">print</span> <span class="s">&quot;#&quot;</span><span class="o">*</span><span class="mi">35</span><span class="o">+</span><span class="s">&quot;Header&quot;</span><span class="o">+</span><span class="s">&quot;#&quot;</span><span class="o">*</span><span class="mi">35</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">7</span><span class="p">):</span>
<span class="k">print</span> <span class="n">gff3</span><span class="o">.</span><span class="n">readline</span><span class="p">()</span>
<span class="k">print</span> <span class="s">&quot;#&quot;</span><span class="o">*</span><span class="mi">35</span><span class="o">+</span><span class="s">&quot;End Header&quot;</span><span class="o">+</span><span class="s">&quot;#&quot;</span><span class="o">*</span><span class="mi">35</span><span class="o">+</span><span class="s">&quot;</span><span class="se">\n</span><span class="s">&quot;</span><span class="o">*</span><span class="mi">3</span>
<span class="c">#Place to store transcript lines</span>
<span class="n">transcripts</span> <span class="o">=</span> <span class="p">[]</span>
<span class="c">#Loop through the rest of the lines</span>
<span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">gff3</span><span class="p">:</span>
<span class="n">columns</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">()</span> <span class="c">#Google &#39;get column of text python&#39;</span>
<span class="n">line_type</span> <span class="o">=</span> <span class="n">columns</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
<span class="c">#Check to see if the 3rd column is a transcript</span>
<span class="k">if</span> <span class="n">line_type</span> <span class="o">==</span> <span class="s">&quot;transcript&quot;</span><span class="p">:</span>
<span class="c">#If it is, save that line</span>
<span class="n">transcripts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">line</span><span class="p">)</span>
<span class="k">print</span> <span class="s">&quot;Number of transcripts: &quot;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">transcripts</span><span class="p">)</span>
<span class="c">#Place to store 01 transcript lines</span>
<span class="n">transcripts01</span> <span class="o">=</span> <span class="p">[]</span>
<span class="c">#Loop through each transcript</span>
<span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">transcripts</span><span class="p">:</span>
<span class="c">#Extract the name from the line</span>
<span class="n">name</span> <span class="o">=</span> <span class="n">get_name</span><span class="p">(</span><span class="n">line</span><span class="p">)</span>
<span class="c">#Check if name ends in 01</span>
<span class="k">if</span> <span class="n">name</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">:]</span> <span class="o">==</span> <span class="s">&quot;01&quot;</span><span class="p">:</span>
<span class="c">#If it does, save that line</span>
<span class="n">transcripts01</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">line</span><span class="p">)</span>
<span class="k">print</span> <span class="s">&quot;Number of 01 transcripts: &quot;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">transcripts01</span><span class="p">),</span> <span class="s">&quot;</span><span class="se">\n</span><span class="s">&quot;</span><span class="o">*</span><span class="mi">3</span>
<span class="c">#Make containers for the spans</span>
<span class="n">spans</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">span_lookup</span> <span class="o">=</span> <span class="p">{}</span> <span class="c">#A dictionary will be perfect for our purposes here</span>
<span class="c">#Loop through all of the 01 transcripts</span>
<span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">transcripts01</span><span class="p">:</span>
<span class="c">#Find the span</span>
<span class="n">columns</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
<span class="n">start</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">columns</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
<span class="n">end</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">columns</span><span class="p">[</span><span class="mi">4</span><span class="p">])</span>
<span class="n">span</span> <span class="o">=</span> <span class="n">end</span> <span class="o">-</span> <span class="n">start</span>
<span class="c">#Populate the dictionary</span>
<span class="n">name</span> <span class="o">=</span> <span class="n">get_name</span><span class="p">(</span><span class="n">line</span><span class="p">)</span>
<span class="n">span_lookup</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">span</span>
<span class="c">#Populate the list</span>
<span class="n">spans</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">span</span><span class="p">)</span>
<span class="k">print</span> <span class="s">&quot;Number of spans: &quot;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">spans</span><span class="p">)</span>
<span class="k">print</span> <span class="s">&quot;The span of RP11-72L22.1-001 is: &quot;</span><span class="p">,</span> <span class="n">span_lookup</span><span class="p">[</span><span class="s">&quot;RP11-72L22.1-001&quot;</span><span class="p">]</span>
<span class="k">print</span> <span class="s">&quot;The span of XIST-001 is: &quot;</span><span class="p">,</span> <span class="n">span_lookup</span><span class="p">[</span><span class="s">&quot;XIST-001&quot;</span><span class="p">],</span> <span class="s">&quot;</span><span class="se">\n</span><span class="s">&quot;</span><span class="o">*</span><span class="mi">3</span>
<span class="c">#Coordinate lists for the scatter plot</span>
<span class="n">x</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">spans</span><span class="p">))</span>
<span class="n">y</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">spans</span><span class="p">)</span>
<span class="c">#Generate scatter plot</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="n">gff3</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
</pre></div>
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<pre>###################################Header###################################
##gff-version 3
#description: evidence-based annotation of the human genome (GRCh38), version 22 (Ensembl 79) - long non-coding RNAs
#provider: GENCODE
#contact: [email protected]
#format: gff3
#date: 2015-03-06
##sequence-region chr1 1 248956422
###################################End Header###################################
Number of transcripts: 27670
Number of 01 transcripts: 15954
Number of spans: 15954
The span of RP11-72L22.1-001 is: 170391
The span of XIST-001 is: 32097
</pre>
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>
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</div><div class="fragment" data-fragment-index="71">
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
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<div class="text_cell_render border-box-sizing rendered_html">
<p>Congratulations! We have working code, that we created from scratch.</p>
<p>Now, if time allows, we can go back through and make edits. Edits should focus on keeping code well documents, easy to read, computationally efficient, and reasonably generalized.</p>
</div>
</div>
</div>
</div></section>
</section><section>
<section>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>What improvements could we make to our scripts?</p>
</div>
</div>
</div></section>
</section><section>
<section>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[33]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython2"><pre><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
<span class="c">#Ignore this line, it&#39;s just for the presentation</span>
<span class="o">%</span><span class="k">matplotlib</span> inline
<span class="k">def</span> <span class="nf">main</span><span class="p">(</span><span class="n">infile</span><span class="p">,</span> <span class="n">outfile</span><span class="p">):</span>
<span class="c">#Open the file</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">infile</span><span class="p">)</span> <span class="k">as</span> <span class="n">gff3</span><span class="p">,</span> <span class="nb">open</span><span class="p">(</span><span class="n">outfile</span><span class="p">,</span> <span class="s">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">outfile</span><span class="p">:</span>
<span class="c">#Ignore the header lines</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">7</span><span class="p">):</span>
<span class="n">gff3</span><span class="o">.</span><span class="n">readline</span><span class="p">()</span>
<span class="c">#Make containers for the spans</span>
<span class="n">spans</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">span_lookup</span> <span class="o">=</span> <span class="p">{}</span>
<span class="c">#Loop through the rest of the lines</span>
<span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">gff3</span><span class="p">:</span>
<span class="n">columns</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
<span class="n">line_type</span> <span class="o">=</span> <span class="n">columns</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
<span class="c">#Check to see if the 3rd column is a transcript</span>
<span class="k">if</span> <span class="n">line_type</span> <span class="o">==</span> <span class="s">&quot;transcript&quot;</span><span class="p">:</span>
<span class="c">#Extract the name from the line</span>
<span class="n">name</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">()[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">&quot;;&quot;</span><span class="p">)[</span><span class="mi">9</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">&quot;=&quot;</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span>
<span class="c">#Check if name ends in 01</span>
<span class="k">if</span> <span class="n">name</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">:]</span> <span class="o">==</span> <span class="s">&quot;01&quot;</span><span class="p">:</span>
<span class="c">#Populate the dictionary</span>
<span class="n">span</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">columns</span><span class="p">[</span><span class="mi">4</span><span class="p">])</span> <span class="o">-</span> <span class="nb">int</span><span class="p">(</span><span class="n">columns</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
<span class="n">span_lookup</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">span</span>
<span class="c">#Populate the list</span>
<span class="n">spans</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">span</span><span class="p">)</span>
<span class="c">#Save span lookup table</span>
<span class="n">outfile</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s">&quot;{}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">span_lookup</span><span class="p">))</span>
<span class="c">#Generate scatter plot</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">spans</span><span class="p">)),</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">spans</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="k">if</span> <span class="n">__name__</span> <span class="o">==</span> <span class="s">&quot;__main__&quot;</span><span class="p">:</span>
<span class="n">folder</span> <span class="o">=</span> <span class="s">&quot;C:/Users/Jessime/Research/&quot;</span>
<span class="n">file_path</span> <span class="o">=</span> <span class="n">folder</span><span class="o">+</span><span class="s">&quot;gencode.v22.long_noncoding_RNAs.gff3&quot;</span>
<span class="n">outfile</span> <span class="o">=</span> <span class="n">folder</span><span class="o">+</span><span class="s">&quot;transcript_spans.txt&quot;</span>
<span class="n">main</span><span class="p">(</span><span class="n">file_path</span><span class="p">,</span> <span class="n">outfile</span><span class="p">)</span>
</pre></div>
</div>
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