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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/44661?offset=90</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/27311/release-notes-for-genome-workbench-2105</guid>
	<pubDate>Thu, 12 May 2016 13:49:41 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/27311/release-notes-for-genome-workbench-2105</link>
	<title><![CDATA[Release Notes for Genome Workbench 2.10.5]]></title>
	<description><![CDATA[<p>New Features in latest release</p><ul>
<li>New ProSplign tool integrated with Genome Workbench (<a href="https://www.ncbi.nlm.nih.gov/tools/gbench/tutorial13">Tutorial</a>,&nbsp;<a href="https://www.youtube.com/watch?v=V9UqKJprzAg&amp;feature=youtu.be" target="_blank">Video</a>)</li>
<li>New export function for BAM/cSRA coverage graphs (<a href="https://www.ncbi.nlm.nih.gov/tools/gbench/tutorial14">Tutorial</a>)</li>
<li>New export function for alignments GFF3 format ((<a href="https://www.ncbi.nlm.nih.gov/tools/gbench/tutorial15">Tutorial</a>))</li>
<li>Tree View: implemented new export mode based on selections (tutorial coming)</li>
<li>Tree View: added support for&nbsp;<a href="https://www.ncbi.nlm.nih.gov/tools/gbench/tutorial3/#distance_based_circular_trees">distance based circular trees</a></li>
<li>Tree View: new rooting mode (Midpoint Root) results in more balanced trees (<a href="https://www.ncbi.nlm.nih.gov/tools/gbench/tutorial3#reroot_tree">Tutorial</a>)</li>
<li>Tree View: added possibility to right-click on an edge between two nodes and "Place Root at Middle of Branch" &ndash; to re-root at mid-branch (<a href="https://www.ncbi.nlm.nih.gov/tools/gbench/tutorial3#reroot_tree">Tutorial</a>)</li>
</ul>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27463/bpipe-a-tool-for-running-and-managing-bioinformatics-pipelines</guid>
	<pubDate>Sat, 21 May 2016 22:42:16 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27463/bpipe-a-tool-for-running-and-managing-bioinformatics-pipelines</link>
	<title><![CDATA[Bpipe - a tool for running and managing bioinformatics pipelines]]></title>
	<description><![CDATA[<p>Bpipe provides a platform for running big bioinformatics jobs that consist of a series of processing stages - known as 'pipelines'.</p>
<ul>
<li>January 20th, 2016 - New! Bpipe 0.9.9 released!</li>
<li>Download <a href="http://download.bpipe.org/versions/bpipe-0.9.9.tar.gz">latest</a>, <a href="http://download.bpipe.org">all</a></li>
<li><a href="http://docs.bpipe.org">Documentation</a></li>
<li><a href="https://groups.google.com/forum/#%21forum/bpipe-discuss">Mailing List</a> (Google Group)</li>
</ul>
<p>Bpipe has been published in <a href="http://bioinformatics.oxfordjournals.org/content/early/2012/04/11/bioinformatics.bts167.abstract">Bioinformatics</a>! If you use Bpipe, please cite:</p>
<p><em>Sadedin S, Pope B &amp; Oshlack A, Bpipe: A Tool for Running and Managing Bioinformatics Pipelines, Bioinformatics</em></p><p>Address of the bookmark: <a href="http://docs.bpipe.org/" rel="nofollow">http://docs.bpipe.org/</a></p>]]></description>
	<dc:creator>Radha Agarkar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27841/covcal-coverage-read-count-calculator</guid>
	<pubDate>Wed, 15 Jun 2016 18:08:13 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27841/covcal-coverage-read-count-calculator</link>
	<title><![CDATA[CovCal: Coverage / Read Count Calculator]]></title>
	<description><![CDATA[<h2>Coverage / Read Count Calculator</h2>
<h4>Calculate how much sequencing you need to hit a target depth of coverage (or vice versa).</h4>
<p><span>Instructions:</span> set the read length/configuration and genome size, then select what you want to calculate.</p>
<p>Written by <a href="http://stephenturner.us/" target="blank">Stephen Turner</a>, based on the <a href="http://www.ncbi.nlm.nih.gov/pubmed/3294162" target="_blank">Lander-Waterman formula</a>, inspired by <a href="http://core-genomics.blogspot.com/2016/05/how-many-reads-to-sequence-genome.html" target="_blank">a similar calculator</a> written by James Hadfield. Coverage is calculated as <em>C=LN/G</em> and reads as <em>N=CG/L</em> where <em>C</em> = Coverage (X),<em>L</em> = Read length (bp), <em>G</em> = Haploid genome size (bp), and <em>N</em> = Number of reads. Source code <a href="https://github.com/stephenturner/covcalc" target="_blank">on GitHub</a>.</p><p>Address of the bookmark: <a href="http://apps.bioconnector.virginia.edu/covcalc/" rel="nofollow">http://apps.bioconnector.virginia.edu/covcalc/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30971/hiveplot</guid>
	<pubDate>Thu, 16 Feb 2017 11:39:34 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30971/hiveplot</link>
	<title><![CDATA[HivePlot]]></title>
	<description><![CDATA[<p>The&nbsp;<em>hive plot</em>&nbsp;is a rational visualization method for drawing networks. Nodes are mapped to and positioned on radially distributed linear axes &mdash; this mapping is based on network structural properties. Edges are drawn as curved links. Simple and interpretable.</p>
<p>The purpose of the hive plot is to establish a new baseline for visualization of large networks &mdash; a method that is both general and tunable and useful as a starting point in visually exploring network structure.</p>
<p>More at&nbsp;http://www.hiveplot.com/</p><p>Address of the bookmark: <a href="http://www.hiveplot.com/" rel="nofollow">http://www.hiveplot.com/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34678/svfinder-tool-for-detecting-genomic-rearrangement-form-dna-seq-data</guid>
	<pubDate>Thu, 14 Dec 2017 15:51:40 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34678/svfinder-tool-for-detecting-genomic-rearrangement-form-dna-seq-data</link>
	<title><![CDATA[SVfinder: Tool for detecting genomic rearrangement form DNA-seq data]]></title>
	<description><![CDATA[<p><span>SVfinder provides genome-wide detection of structural variants from next generation paired-end sequencing reads.</span></p><p>Address of the bookmark: <a href="https://github.com/cauyrd/SVfinder" rel="nofollow">https://github.com/cauyrd/SVfinder</a></p>]]></description>
	<dc:creator>Robert M Willioms</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36012/gmol-an-interactive-tool-for-3d-genome-structure-visualization</guid>
	<pubDate>Wed, 21 Mar 2018 12:25:20 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36012/gmol-an-interactive-tool-for-3d-genome-structure-visualization</link>
	<title><![CDATA[GMOL: An Interactive Tool for 3D Genome Structure Visualization]]></title>
	<description><![CDATA[<p><span>GMOL was developed based upon our multi-scale approach that allows a user to scale between six separate levels within the genome. With GMOL, a user can choose any unit at any scale and scale it up or down to visualize its structure and retrieve corresponding genome sequences.</span></p><p>Address of the bookmark: <a href="https://www.nature.com/articles/srep20802" rel="nofollow">https://www.nature.com/articles/srep20802</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41736/synvisio-an-interactive-multiscale-synteny-visualization-tool-for-mcscanx</guid>
	<pubDate>Sun, 31 May 2020 02:01:14 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41736/synvisio-an-interactive-multiscale-synteny-visualization-tool-for-mcscanx</link>
	<title><![CDATA[SynVisio: An Interactive Multiscale Synteny Visualization Tool for McScanX.]]></title>
	<description><![CDATA[<p>SynVisio lets you explore the results of&nbsp;<a href="http://chibba.pgml.uga.edu/mcscan2/">McScanX</a>&nbsp;a popular synteny and collinearity detection toolkit and generate publication ready images.</p>
<p>SynVisio requires two files to run:</p>
<ul>
<li>The&nbsp;<strong>simplified gff file</strong>&nbsp;that was used as an input for a McScanX query.</li>
<li>The&nbsp;<strong>collinearity file</strong>&nbsp;generated as an output by McScanX for the same input query.</li>
<li>Optional&nbsp;<strong>track file</strong>&nbsp;in bedgraph format to annotate the generated charts.</li>
</ul>
<p>SynVisio offers different types of visualizations such as&nbsp;<strong>Linear Parallel plots</strong>,&nbsp;<strong>Hive plots</strong>,&nbsp;<strong>Stacked Parallel Plots&nbsp;</strong>and&nbsp;<strong>Dot plots</strong>. Users can configure the type of plots required and then choose the source and the target chromosomes that need to be mapped. Users also have option to download the generated visualizations in publication ready SVG or PNG formats.</p><p>Address of the bookmark: <a href="https://synvisio.github.io/#/" rel="nofollow">https://synvisio.github.io/#/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44571/panacus-a-counting-tool-for-pangenome-graphs</guid>
	<pubDate>Fri, 14 Jun 2024 14:42:28 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44571/panacus-a-counting-tool-for-pangenome-graphs</link>
	<title><![CDATA[Panacus : A Counting Tool for Pangenome Graphs]]></title>
	<description><![CDATA[<p dir="auto"><code>panacus</code>&nbsp;is a tool for calculating statistics for&nbsp;<a href="https://github.com/GFA-spec/GFA-spec/blob/master/GFA1.md">GFA</a>&nbsp;files. It supports GFA files with&nbsp;<code>P</code>&nbsp;and&nbsp;<code>W</code>&nbsp;lines, but requires that the graph is&nbsp;<code>blunt</code>, i.e., nodes do not overlap and consequently, each link (<code>L</code>) points from the end of one segment (<code>S</code>) to the start of another.</p>
<p dir="auto"><code>panacus</code>&nbsp;supports the following calculations:</p>
<ul dir="auto">
<li>coverage histogram</li>
<li>pangenome growth statistics</li>
<li>path-/group-resolved coverage table</li>
</ul><p>Address of the bookmark: <a href="https://github.com/marschall-lab/panacus" rel="nofollow">https://github.com/marschall-lab/panacus</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/45249/from-dna-chaos-to-a-clear-microbial-story</guid>
	<pubDate>Fri, 21 Aug 2026 11:22:54 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/45249/from-dna-chaos-to-a-clear-microbial-story</link>
	<title><![CDATA[From DNA Chaos to a Clear Microbial Story]]></title>
	<description><![CDATA[<p>Imagine opening a metagenomic dataset and finding millions of DNA fragments, each carrying a tiny piece of information about the microbial world inside a sample. The challenge is not generating the data &mdash; it is turning that data into a meaningful story.</p><p>This is where MeTAline comes in.</p><p>MeTAline is a reproducible workflow designed to simplify shotgun metagenomic analysis. It brings together important steps such as quality control, host-DNA removal, taxonomic profiling and functional analysis, using tools including Kraken2, MetaPhlAn4 and HUMAnN.</p><p>What makes it especially useful is its ability to handle large datasets while keeping analyses organized and reproducible. Built with Snakemake and supporting containerized environments, MeTAline can also be used on high-performance computing systems.</p><p>In simple terms, MeTAline helps researchers move from millions of confusing DNA fragments to a clearer picture of who is there, what they might be doing, and what the data could mean.</p><p>Sometimes, the biggest challenge in science isn't collecting more data &mdash; it's finding the story hidden inside it.</p><p>Read more at&nbsp;https://academic.oup.com/nargab/article/7/4/lqaf158/8340158?login=false</p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42963/davi-deep-learning-based-tool-for-alignment-and-single-nucleotide-variant-identification</guid>
	<pubDate>Tue, 16 Mar 2021 05:41:33 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42963/davi-deep-learning-based-tool-for-alignment-and-single-nucleotide-variant-identification</link>
	<title><![CDATA[DAVI: Deep learning-based tool for alignment and single nucleotide variant identification]]></title>
	<description><![CDATA[<p>DAVI consists of models for both global and local alignment and for variant calling. We have evaluated the performance of DAVI against existing state-of-the-art tool sets and found that its accuracy and performance is comparable to existing tools used for bench-marking. We further demonstrate that while existing tools are based on data generated from a specific sequencing technology, the models proposed in DAVI are generic and can be used across different NGS technologies as well as across different species</p>
<p>https://iopscience.iop.org/article/10.1088/2632-2153/ab7e19/pdf</p><p>Address of the bookmark: <a href="https://github.com/gguptaiitd/NEAT" rel="nofollow">https://github.com/gguptaiitd/NEAT</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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