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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/38166?offset=450</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/45273/a-subway-map-for-the-genome</guid>
	<pubDate>Fri, 04 Sep 2026 04:14:14 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/45273/a-subway-map-for-the-genome</link>
	<title><![CDATA[A Subway Map for the Genome]]></title>
	<description><![CDATA[<p>Maya was looking at a genome graph on her computer. There were nodes, branches, and paths going in every direction.</p><p>&ldquo;It&rsquo;s like a subway map,&rdquo; she said, &ldquo;but for DNA.&rdquo;</p><p>That idea is exactly what Sequence Tube Map brings to life.</p><p>Instead of showing a genome as one long, confusing sequence, it displays different genomic paths like routes on a metro map. Where sequences are shared, the paths travel together. Where genetic variation occurs, they branch into different routes and may join again later.</p><p>This makes complex genome graphs much easier to explore.</p><p>Maya could now see how different haplotypes moved through the same genomic region and where they took different paths. She could even view sequencing reads mapped onto the graph, helping her understand what was happening in regions with genetic variation.</p><p>What once looked like a tangled network now looked like a journey.</p><p>Sequence Tube Map turns the complexity of graph genomes into a visual story&mdash;making it easier for scientists to see how DNA can take different routes.</p><p>Read more at&nbsp; https://github.com/vgteam/sequencetubemap&nbsp;</p><p>and</p><p>https://academic.oup.com/bioinformatics/article/35/24/5318/5542397</p><p>Demo at&nbsp;https://vgteam.github.io/sequenceTubeMap/</p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/45306/the-genes-that-travel-together-a-genomic-detective-story</guid>
	<pubDate>Fri, 11 Sep 2026 13:00:36 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/45306/the-genes-that-travel-together-a-genomic-detective-story</link>
	<title><![CDATA[The Genes That Travel Together: A Genomic Detective Story]]></title>
	<description><![CDATA[<p>Imagine looking through thousands of microbial genomes and discovering two genes that repeatedly appear together. The obvious conclusion is that they must somehow be connected&mdash;that perhaps they work together, participate in the same pathway, or depend on each other. But evolution has a way of leaving misleading clues. What if these two genes are found together simply because they were inherited from the same ancient ancestor? In that case, their apparent association may have little to do with their biological function; it may simply be a reflection of shared evolutionary history. This is the intriguing problem addressed by CORGIAS (https://github.com/ynishimuraLv/corgias), a computational framework developed to distinguish genuine gene associations from correlations created by common ancestry.</p><p>Instead of asking only whether two genes occur together across genomes, CORGIAS asks a deeper question: did these genes actually evolve together, repeatedly gaining or losing their presence in concert, or are they merely travelling together because of their inherited history? The framework introduces two complementary approaches, Ancestral State Adjustment (ASA) and the Simultaneous EVolution test (SEV), which incorporate evolutionary information into the search for gene associations. This distinction becomes increasingly important as genome sequencing continues to uncover enormous numbers of microbial genomes, many from organisms that have never been cultured and whose genes remain functionally mysterious. In this growing genomic landscape, simply finding a gene is no longer enough&mdash;we need clues about what that gene might be doing. CORGIAS approaches this problem almost like a genomic detective: rather than treating every association as evidence, it reconstructs the evolutionary story behind the association and asks whether the evidence survives that history. By doing so, it offers a way to separate coincidence from biological connection and potentially uncover functional relationships hidden within the vast microbial genomic landscape. The broader message is beautifully simple: genes do not evolve in isolation, and understanding what they do may require us to look beyond where they are today and reconstruct how they arrived there. Sometimes, the most important clue in a genome is not the gene itself, but the evolutionary journey it has taken.</p><p>More at&nbsp;https://academic.oup.com/nargab/article/7/4/lqaf182/8377910?</p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35272/biocircosjs-is-an-open-source-interactive-javascript-library-to-interactive-display-biological-data-on-the-web</guid>
	<pubDate>Fri, 19 Jan 2018 15:03:51 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35272/biocircosjs-is-an-open-source-interactive-javascript-library-to-interactive-display-biological-data-on-the-web</link>
	<title><![CDATA[BioCircos.js is an open source interactive Javascript library to interactive display biological data on the web]]></title>
	<description><![CDATA[<p><a href="http://bioinfo.ibp.ac.cn/biocircos/index.php">BioCircos.js</a>&nbsp;is an open source interactive&nbsp;<code>Javascript</code>&nbsp;library which provides an easy way to interactive display biological data on the web. It implements a raster-based&nbsp;<code>SVG</code>&nbsp;visualization using the open source Javascript framework jquery.js. BioCircos.js is multiplatform and works in all major internet browsers (<strong>Internet Explorer</strong>,&nbsp;<strong>Mozilla Firefox</strong>,&nbsp;<strong>Google Chrome</strong>,&nbsp;<strong>Safari</strong>,&nbsp;<strong>Opera</strong>). Its speed is determined by the client&rsquo;s hardware and internet browser. For smoothest user experience, we recommend&nbsp;<strong>Google Chrome</strong>.</p>
<p>BioCircos.js provides&nbsp;<strong>SNP</strong>,&nbsp;<strong>CNV</strong>,&nbsp;<strong>HEATMAP</strong>,&nbsp;<strong>LINK</strong>,&nbsp;<strong>LINE</strong>,&nbsp;<strong>SCATTER</strong>,&nbsp;<strong>ARC</strong>,&nbsp;<strong>TEXT</strong>, and&nbsp;<strong>HISTGRAM</strong>modules to display genome-wide genetic variations (SNPs, CNVs and chromosome rearrangement), gene expression and biomolecule interactions. BioCircos.js also provides&nbsp;<strong>BACKGROUND</strong>&nbsp;module to display background and axis circles. Tooltips showing detailed information of SVG elements are also provided.</p>
<p><a href="http://bioinfo.ibp.ac.cn/biocircos/document/demo/pages/paper01.html">Demo</a></p><p>Address of the bookmark: <a href="http://bioinfo.ibp.ac.cn/biocircos/document/index.html" rel="nofollow">http://bioinfo.ibp.ac.cn/biocircos/document/index.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38646/visnetwork-an-r-package-for-network-visualization-using-visjs-javascript-library</guid>
	<pubDate>Wed, 09 Jan 2019 11:00:32 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38646/visnetwork-an-r-package-for-network-visualization-using-visjs-javascript-library</link>
	<title><![CDATA[visNetwork: an R package for network visualization, using vis.js javascript library]]></title>
	<description><![CDATA[<div id="introduction">
<p><strong>visNetwork</strong>&nbsp;is an R package for network visualization, using&nbsp;<strong>vis.js</strong>&nbsp;javascript library (<a href="http://visjs.org/">http://visjs.org/</a>). All remarks and bugs are welcome on github :&nbsp;<a href="https://github.com/datastorm-open/visNetwork">https://github.com/datastorm-open/visNetwork</a>.</p>
</div>
<div id="features">
<h2>Features</h2>
<p>Based on&nbsp;<a href="http://www.htmlwidgets.org/">htmlwidgets</a>, so :</p>
<ul>
<li>compatible with&nbsp;<a href="http://shiny.rstudio.com/">shiny</a>, R Markdown documents, and RStudio viewer</li>
</ul>
<p>The package proposes all the features available in&nbsp;<strong>vis.js</strong>&nbsp;API, and even more with special features for R :</p>
<ul>
<li>easy to use</li>
<li>custom shapes, styles, colors, sizes, &hellip;</li>
<li>works smooth on any modern browser for up to a few thousand nodes and edges</li>
<li>interactivity controls (highlight, collapsed nodes, selection, zoom, physics, movement of nodes, tooltip, events, &hellip;)</li>
<li>visualize&nbsp;<code>rpart</code>&nbsp;tree</li>
<li></li>
</ul>
</div><p>Address of the bookmark: <a href="https://datastorm-open.github.io/visNetwork/" rel="nofollow">https://datastorm-open.github.io/visNetwork/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27070/venn-diagrams-on-r-studio</guid>
	<pubDate>Mon, 25 Apr 2016 16:22:28 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27070/venn-diagrams-on-r-studio</link>
	<title><![CDATA[Venn Diagrams on R Studio]]></title>
	<description><![CDATA[<h3>First step: Install &amp; load &ldquo;VennDiagram&rdquo; package.</h3>
<pre><code><span># install.packages('VennDiagram')</span>
<span>library</span><span>(</span><span>VennDiagram</span><span>)</span>
</code></pre>
<h3>Second step: Load data</h3>
<p>Add filepath if &ldquo;catdoge.csv&rdquo; is not in working-directory.</p>
<pre><code><span>d</span> <span>&lt;-</span> <span>read.csv</span><span>(</span><span>"catdoge.csv"</span><span>)</span></code><br><br></pre><p>Address of the bookmark: <a href="http://rstudio-pubs-static.s3.amazonaws.com/13301_6641d73cfac741a59c0a851feb99e98b.html" rel="nofollow">http://rstudio-pubs-static.s3.amazonaws.com/13301_6641d73cfac741a59c0a851feb99e98b.html</a></p>]]></description>
	<dc:creator>Jitendra Prajapati</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31714/krona</guid>
	<pubDate>Wed, 22 Mar 2017 04:47:35 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31714/krona</link>
	<title><![CDATA[Krona]]></title>
	<description><![CDATA[<p>Krona allows hierarchical data to be explored with zooming, multi-layered pie charts. Krona charts can be created using an <a href="https://github.com/marbl/Krona/wiki/ExcelTemplate">Excel template</a> or <a href="https://github.com/marbl/Krona/wiki/KronaTools">KronaTools</a>, which includes support for several bioinformatics tools and raw data formats. The interactive charts are self-contained and can be viewed with any modern web browser (see <a href="https://github.com/marbl/Krona/wiki/Browser%20support">Browser support</a>).</p>
<p><a href="http://marbl.github.io/Krona/img/screen_mgrast.png"><img src="https://camo.githubusercontent.com/27b71b1f1832523723c3d14dec764e7ad098438c/687474703a2f2f6d6172626c2e6769746875622e696f2f4b726f6e612f696d672f7468756d625f6d67726173742e706e67" width="210" height="167" alt="image" style="border: 0px;"></a></p><p>Address of the bookmark: <a href="https://github.com/marbl/Krona/wiki" rel="nofollow">https://github.com/marbl/Krona/wiki</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35418/karyoploter-plot-whole-genomes-with-arbitrary-data</guid>
	<pubDate>Fri, 02 Feb 2018 03:24:28 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35418/karyoploter-plot-whole-genomes-with-arbitrary-data</link>
	<title><![CDATA[karyoploteR: plot whole genomes with arbitrary data]]></title>
	<description><![CDATA[<p><span><a href="http://bioconductor.org/packages/karyoploteR">karyoploteR</a></span><span>&nbsp;is an R package to create karyoplots, that is, representations of whole genomes with arbitrary data plotted on them. It is inspired by the R base graphics system and does not depend on other graphics packages. The aim of karyoploteR is to offer the user an easy way to plot data along the genome to get broad genome-wide view to facilitate the identification of genome wide relations and distributions.</span></p><p>Address of the bookmark: <a href="https://bernatgel.github.io/karyoploter_tutorial/" rel="nofollow">https://bernatgel.github.io/karyoploter_tutorial/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40937/shinycircos-an-rshiny-application-for-interactive-creation-of-circos-plot</guid>
	<pubDate>Fri, 07 Feb 2020 03:26:58 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40937/shinycircos-an-rshiny-application-for-interactive-creation-of-circos-plot</link>
	<title><![CDATA[shinyCircos: an R/Shiny application for interactive creation of Circos plot]]></title>
	<description><![CDATA[<p><span>shinyCircos, a graphical user interface for interactive creation of Circos plot. shinyCircos can be easily installed either on computers for personal use or on local or public servers to provide online use to the community. Furthermore, various types of Circos plots could be easily generated and decorated with simple mouse-click.</span></p>
<p>Tutorial&nbsp;<a href="http://shinycircos.ncpgr.cn/shinyCircos_Help_Manual.pdf">http://shinycircos.ncpgr.cn/shinyCircos_Help_Manual.pdf</a></p>
<p>Github&nbsp;<a href="https://github.com/venyao/shinyCircos">https://github.com/venyao/shinyCircos</a></p><p>Address of the bookmark: <a href="http://150.109.59.144:3838/shinyCircos/" rel="nofollow">http://150.109.59.144:3838/shinyCircos/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44182/collection-of-graph-visualization-tools</guid>
	<pubDate>Wed, 25 Jan 2023 02:57:42 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44182/collection-of-graph-visualization-tools</link>
	<title><![CDATA[Collection of Graph Visualization tools !]]></title>
	<description><![CDATA[<p>Standard approaches to genome inference and analysis relate sequences to a single linear reference genome. This is efficient but has a fundamental problem: Differences from this reference are hard to observe and describe in a coherent way. Variation and sequence are separated.</p>
<p><a href="https://pangenome.github.io/images/genomic-vs-pangenomic-analysis.png"><img src="https://pangenome.github.io/images/genomic-vs-pangenomic-analysis.png" alt="image" width="45%" style="border: 0px; border: 0px;"></a><span>&nbsp;</span><a href="https://pangenome.github.io/images/genomic-vs-pangenomic-models.png"><img src="https://pangenome.github.io/images/genomic-vs-pangenomic-models.png" alt="image" width="54%" style="border: 0px; border: 0px;"></a></p>
<p><a href="https://fungidb.org/fungidb/app/downloads/Current_Release/GultimumBR650/" target="_blank">https://fungidb.org/fungidb/app/downloads/Current_Release/GultimumBR650/</a></p><p>Address of the bookmark: <a href="https://pangenome.github.io/" rel="nofollow">https://pangenome.github.io/</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/fun/view/14036/introduction-to-programming-write-short-programs-that-generate-graphics-and-animation</guid>
	<pubDate>Thu, 14 Aug 2014 23:29:04 -0500</pubDate>
	<link>https://bioinformaticsonline.com/fun/view/14036/introduction-to-programming-write-short-programs-that-generate-graphics-and-animation</link>
	<title><![CDATA[Introduction to programming. Write short programs that generate graphics and animation.]]></title>
	<description><![CDATA[<p>Introduction to programming. Write short programs that generate graphics and animation.</p><p>http://funprogramming.org/</p>]]></description>
	<dc:creator>Ram Yash Pal</dc:creator>
</item>

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