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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/34571?offset=190</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39380/mgert-mobile-genetic-elements-retrieving-tool</guid>
	<pubDate>Sat, 18 May 2019 08:58:01 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39380/mgert-mobile-genetic-elements-retrieving-tool</link>
	<title><![CDATA[MGERT: Mobile Genetic Elements Retrieving Tool]]></title>
	<description><![CDATA[<p><em>MGERT</em><span>&nbsp;is a computational pipeline for easy retrieving of MGE's coding sequences of a particular family from genome assemblies.&nbsp;</span><em>MGERT</em><span>&nbsp;utilizes several established bioinformatic tools combined into single pipeline which hides different technical quirks from an inexperienced user.</span></p><p>Address of the bookmark: <a href="https://github.com/andrewgull/MGERT" rel="nofollow">https://github.com/andrewgull/MGERT</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44487/r-package-for-pca-analysis</guid>
	<pubDate>Sun, 24 Mar 2024 20:06:24 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44487/r-package-for-pca-analysis</link>
	<title><![CDATA[R Package for PCA Analysis]]></title>
	<description><![CDATA[<p><span>An R package for performing principal component analysis (PCA) of genomics data. The package performs PCA, generates the publication-ready plots, and identifies population-specific outlier individuals. The package can be accessed on GitHub:&nbsp;https://github.com/Devashish13/PopulationStructure</span></p><p>Address of the bookmark: <a href="https://rpubs.com/Devashish13/PCAGenomics" rel="nofollow">https://rpubs.com/Devashish13/PCAGenomics</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/45270/when-dna-data-tells-the-wrong-story-crossfit-assist-to-resolve</guid>
	<pubDate>Fri, 04 Sep 2026 03:41:38 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/45270/when-dna-data-tells-the-wrong-story-crossfit-assist-to-resolve</link>
	<title><![CDATA[When DNA Data Tells the Wrong Story - CrossFit assist to resolve !]]></title>
	<description><![CDATA[<p>Imagine comparing humans and chimpanzees to discover which genes make us different. You have millions of RNA-sequencing reads&mdash;but there is a catch: the computer may align these reads more easily to one species' genome than the other.</p><p>Suddenly, a technical error can look like a biological difference.</p><p>This is the problem Kenneth A. Barr and Yoav Gilad tackle with CrossFilt, a new computational tool introduced in Genome Biology journal.</p><p>CrossFilt acts like a careful referee. It keeps only sequencing reads that can be reliably compared across species, filtering out reads that could create alignment bias.</p><p>When tested on human, chimpanzee, and rhesus macaque data, CrossFilt reduced false discoveries and produced more reliable cross-species comparisons.</p><p>The message is simple: before asking what makes species different, we need to make sure our methods aren't creating those differences.</p><p>In genomics, sometimes the most important discovery is finding&mdash;and removing&mdash;the bias hiding in the data.</p><p>Read more at&nbsp;https://link.springer.com/article/10.1186/s13059-026-04082-2?</p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35420/telomerehunter</guid>
	<pubDate>Fri, 02 Feb 2018 04:23:59 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35420/telomerehunter</link>
	<title><![CDATA[TelomereHunter]]></title>
	<description><![CDATA[<p><span>TelomereHunter is a tool for estimating telomere content from human whole-genome sequencing data. It is designed to take BAM files from a tumor and a matching control sample as input. However, it is also possible to run TelomereHunter with one input file. TelomereHunter extracts and sorts telomeric reads from the input sample(s). For the estimation of telomere content, GC biases are taken into account. Finally, the results of TelomereHunter are visualized in several diagrams.</span><br><br><span>TelomereHunter is available for download at the following address:&nbsp;</span><a href="https://pypi.python.org/pypi/telomerehunter/" target="_blank">https://pypi.python.org/pypi/telomerehunter/</a></p><p>Address of the bookmark: <a href="http://www.dkfz.de/en/applied-bioinformatics/telomerehunter/telomerehunter.html" rel="nofollow">http://www.dkfz.de/en/applied-bioinformatics/telomerehunter/telomerehunter.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34380/chsminer-a-gui-tool-to-identify-chromosomal-homologous-segments</guid>
	<pubDate>Sat, 18 Nov 2017 16:55:49 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34380/chsminer-a-gui-tool-to-identify-chromosomal-homologous-segments</link>
	<title><![CDATA[CHSMiner: a GUI tool to identify chromosomal homologous segments]]></title>
	<description><![CDATA[<div id="ASec1">
<h3>Background</h3>
<p>The identification of chromosomal homologous segments (CHS) within and between genomes is essential for comparative genomics. Various processes including insertion/deletion and inversion could cause the degeneration of CHSs.</p>
</div>
<div id="ASec2">
<h3>Results</h3>
<p>Here we present a Java software CHSMiner that detects CHSs based on shared gene content alone. It implements fast greedy search algorithm and rigorous statistical validation, and its friendly graphical interface allows interactive visualization of the results. We tested the software on both simulated and biological realistic data and compared its performance with similar existing software and data source.</p>
</div>
<div id="ASec3">
<h3>Conclusion</h3>
<p>CHSMiner is characterized by its integrated workflow, fast speed and convenient usage. It will be useful for both experimentalists and bioinformaticians interested in the structure and evolution of genomes.</p>
<p>&nbsp;</p>
<p>https://github.com/zhenwang100/CHSMiner</p>
</div><p>Address of the bookmark: <a href="https://almob.biomedcentral.com/articles/10.1186/1748-7188-4-2" rel="nofollow">https://almob.biomedcentral.com/articles/10.1186/1748-7188-4-2</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34922/camsa-a-tool-for-comparative-analysis-and-merging-of-scaffold-assemblies</guid>
	<pubDate>Thu, 28 Dec 2017 09:10:26 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34922/camsa-a-tool-for-comparative-analysis-and-merging-of-scaffold-assemblies</link>
	<title><![CDATA[CAMSA :: a tool for Comparative Analysis and Merging of Scaffold Assemblies]]></title>
	<description><![CDATA[<p>CAMSA &ndash; is a tool for&nbsp;<span>C</span>omparative&nbsp;<span>A</span>nalysis and&nbsp;<span>M</span>erging of&nbsp;<span>S</span>caffold&nbsp;<span>A</span>ssemblies, distributed both as a standalone software package and as Python library under the MIT license.</p>
<p>Main features:</p>
<ol>
<li>works with any number of scaffold assemblies in de-novo non-progressive fashion</li>
<li>allows to simultaneously work with scaffold assemblies obtained from any&nbsp;<em>in silico</em>&nbsp;and&nbsp;<em>in vitro</em>&nbsp;techniques, supporting multiple existing formats via built-in converters</li>
<li>creates an extensive report with several comparative quality metrics (both on assembly level and on the level of individual assembly points)</li>
<li>constructs a merged combined scaffold assembly</li>
<li>provides an interactive framework for a visual comparative analysis of the given assemblies</li>
</ol><p>Address of the bookmark: <a href="https://cblab.org/camsa/" rel="nofollow">https://cblab.org/camsa/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36752/minmax-a-versatile-tool-for-calculating-and-comparing-synonymous-codon-usage-and-its-impact-on-protein-folding</guid>
	<pubDate>Thu, 24 May 2018 02:53:31 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36752/minmax-a-versatile-tool-for-calculating-and-comparing-synonymous-codon-usage-and-its-impact-on-protein-folding</link>
	<title><![CDATA[%MinMax: A versatile tool for calculating and comparing synonymous codon usage and its impact on protein folding.]]></title>
	<description><![CDATA[%MM calculates whether a given gene sequence encodes amino acids using the most common codons possible, the least common codons possible, or (most typically) some combination of these extremes. See our PLoS ONE paper for more details on how the %MinMax algorithm works. 

%MinMax results are averaged over an 18-codon sliding window; hence the result for "codon window = 1" is the average codon usage for codons 1-18, codon window 2 = codons 2-19, etc.<p>Address of the bookmark: <a href="http://www.codons.org/" rel="nofollow">http://www.codons.org/</a></p>]]></description>
	<dc:creator>Surabhi Chaudhary</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36905/d-genies-a-tool-for-dotplot-large-genomes-in-an-interactive-efficient-and-simple-way</guid>
	<pubDate>Mon, 11 Jun 2018 09:41:22 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36905/d-genies-a-tool-for-dotplot-large-genomes-in-an-interactive-efficient-and-simple-way</link>
	<title><![CDATA[D-GENIES: A tool for Dotplot large Genomes in an Interactive, Efficient and Simple way]]></title>
	<description><![CDATA[D-GENIES – for Dotplot large Genomes in an Interactive, Efficient and Simple way – is an online tool designed to compare two genomes. It supports large genome and you can interact with the dot plot to improve the visualisation.

We use minimap version 2 to align the two genomes. Then, the PAF file is parsed and plotted into an interactive plot written with d3.js library.

D-Genies also allows to display dot plots from other aligners by uploading their PAF or MAF alignment file.

http://dgenies.toulouse.inra.fr/<p>Address of the bookmark: <a href="http://dgenies.toulouse.inra.fr/" rel="nofollow">http://dgenies.toulouse.inra.fr/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37496/gsearch-a-fast-and-flexible-general-search-tool-for-whole-genome-sequencing</guid>
	<pubDate>Mon, 06 Aug 2018 17:19:15 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37496/gsearch-a-fast-and-flexible-general-search-tool-for-whole-genome-sequencing</link>
	<title><![CDATA[gSearch: a fast and flexible general search tool for whole-genome sequencing]]></title>
	<description><![CDATA[<p><span>gSearch compares sequence variants in the Genome Variation Format (GVF) or Variant Call Format (VCF) with a pre-compiled annotation or with variants in other genomes. Its search algorithms are subsequently optimized and implemented in a multi-threaded manner.&nbsp;</span></p><p>Address of the bookmark: <a href="http://ml.ssu.ac.kr/gSearch/index.html" rel="nofollow">http://ml.ssu.ac.kr/gSearch/index.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37643/lorma-a-tool-for-correcting-sequencing-errors-in-long-reads</guid>
	<pubDate>Thu, 06 Sep 2018 16:21:01 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37643/lorma-a-tool-for-correcting-sequencing-errors-in-long-reads</link>
	<title><![CDATA[LoRMA: A tool for correcting sequencing errors in long reads]]></title>
	<description><![CDATA[<p><span>An error correction method that uses long reads only. The method consists of two phases: first, we use an iterative alignment-free correction method based on de Bruijn graphs with increasing length of&nbsp;</span><em>k</em><span>-mers, and second, the corrected reads are further polished using long-distance dependencies that are found using multiple alignments. According to our experiments, the proposed method is the most accurate one relying on long reads only for read sets with high coverage. Furthermore, when the coverage of the read set is at least 75&times;, the throughput of the new method is at least 20% higher.</span></p>
<blockquote>
<p><span>conda install -c atgc-montpellier lorma</span></p>
</blockquote><p>Address of the bookmark: <a href="https://gite.lirmm.fr/lorma/lorma-releases/wikis/home" rel="nofollow">https://gite.lirmm.fr/lorma/lorma-releases/wikis/home</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>

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