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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/37796?offset=170</link>
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	<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/30002/excavator2tool</guid>
	<pubDate>Wed, 30 Nov 2016 04:09:19 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30002/excavator2tool</link>
	<title><![CDATA[EXCAVATOR2tool]]></title>
	<description><![CDATA[<p><span>EXCAVATOR2 is a collection of bash, R and Fortran scripts and codes that analyses Whole Exome Sequencing (WES) data to identify CNVs. EXCAVATOR2 enhances the identification of all genomic CNVs, both overlapping and non-overlapping targeted exons by integrating the analysis of In-targets and Off- targets reads. Specifically, it improves the precision of calling CNVs overlapping targeted exons from WES data and enlarges the spectrum of detectable CNVs to off-target events.</span><br><span>EXCAVATOR2 can be effectively employed for the identification of CNVs in small as well as large-scale re-sequencing population and cancer studies. Lastly, it&rsquo;s of particular interest that all WES experiments can be re-analysed using our method with the beneficial effect to identify novelCNVs in extra-exonic regions by having the full-genome CN profile.</span></p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/excavator2tool/" rel="nofollow">https://sourceforge.net/projects/excavator2tool/</a></p>]]></description>
	<dc:creator>Bulbul</dc:creator>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34141/rami-a-tool-for-identification-and-characterization-of-phylogenetic-clusters-in-microbial-communities</guid>
	<pubDate>Mon, 07 Aug 2017 18:49:27 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34141/rami-a-tool-for-identification-and-characterization-of-phylogenetic-clusters-in-microbial-communities</link>
	<title><![CDATA[RAMI: a tool for identification and characterization of phylogenetic clusters in microbial communities]]></title>
	<description><![CDATA[<p>RAMI, which clusters related nodes in a phylogenetic tree based on the patristic distance. RAMI also produces indices of cluster properties and other indices used in population and community studies on-the-fly.</p>
<p><strong>Availability:</strong>&nbsp;RAMI is licensed under GNU GPL and can be run or downloaded from&nbsp;<a href="http://www.acgt.se/online.html" target="">http://www.acgt.se/online.html</a>.</p><p>Address of the bookmark: <a href="https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btp051" rel="nofollow">https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btp051</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>
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	<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/36857/%E2%80%9Cone-code-to-find-them-all%E2%80%9D-a-perl-tool-to-conveniently-parse-repeatmasker-output-files</guid>
	<pubDate>Mon, 04 Jun 2018 03:45:15 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36857/%E2%80%9Cone-code-to-find-them-all%E2%80%9D-a-perl-tool-to-conveniently-parse-repeatmasker-output-files</link>
	<title><![CDATA[“One code to find them all”: a perl tool to conveniently parse RepeatMasker output files]]></title>
	<description><![CDATA[One code to find them all is a set of perl scripts to extract useful information from RepeatMasker about transposable elements, retrieve their sequences and get some quantitative information.

Assemble RepeatMasker hits into complete TE copies, including LTR-retrotransposon
Retrieve corresponding TE sequences, and flanking sequences, from the local fasta files
Compute summary statistics for each TE family (number of TE copies, genome coverage...)
Ambiguous cases such as nested TE can be assembled into copies automatically or manually
Allow for working with a TE user-defined library
Allow for working with only a user-chosen set of TE families


http://doua.prabi.fr/software/one-code-to-find-them-all<p>Address of the bookmark: <a href="http://doua.prabi.fr/software/one-code-to-find-them-all" rel="nofollow">http://doua.prabi.fr/software/one-code-to-find-them-all</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</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/37576/lrcstats-a-tool-for-evaluating-long-reads-correction-methods</guid>
	<pubDate>Wed, 22 Aug 2018 11:05:04 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37576/lrcstats-a-tool-for-evaluating-long-reads-correction-methods</link>
	<title><![CDATA[LRCstats: a tool for evaluating long reads correction methods]]></title>
	<description><![CDATA[<p><span>LRCstats is an open-source pipeline for benchmarking DNA long read correction algorithms for long reads outputted by third generation sequencing technology such as machines produced by Pacific Biosciences. The reads produced by third generation sequencing technology, as the name suggests, are longer in length than reads produced by next generation sequencing technologies, such as those produced by Illumina. However, long reads are plagued by high error rates, which can cause issues in downstream analysis. Long read correction algorithms reduce the error rate of long reads either through self-correcting methods or using accurate, short reads outputted by next generation sequencing technologies to correct long reads.</span></p><p>Address of the bookmark: <a href="https://github.com/cchauve/lrcstats" rel="nofollow">https://github.com/cchauve/lrcstats</a></p>]]></description>
	<dc:creator>Aaryan Lokwani</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37800/heatmapper-web-enabled-heat-mapping-for-all</guid>
	<pubDate>Mon, 01 Oct 2018 08:34:41 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37800/heatmapper-web-enabled-heat-mapping-for-all</link>
	<title><![CDATA[Heatmapper: web-enabled heat mapping for all]]></title>
	<description><![CDATA[<p><span>Heatmapper is a freely available web server that allows users to interactively visualize their data in the form of heat maps through an easy-to-use graphical interface. Heatmapper is a versatile tool that allows users to easily create a wide variety of heat maps for many different data types and applications. Heatmapper allows users to generate, cluster and visualize: </span></p>
<p><span>1)&nbsp;</span><span>expression-based heat maps</span><span>&nbsp;from transcriptomic, proteomic and metabolomic experiments; 2)&nbsp;</span><span>pairwise distance maps</span><span>; </span></p>
<p><span>3)&nbsp;</span><span>correlation maps</span><span>; </span></p>
<p><span>4)&nbsp;</span><span>image overlay heat maps</span><span>; </span></p>
<p><span>5)&nbsp;</span><span>latitude and longitude heat maps</span><span>&nbsp;and </span></p>
<p><span>6)&nbsp;</span><span>geopolitical (choropleth) heat maps</span><span>. </span></p>
<p><span>Heatmapper offers a number of simple and intuitive customization options for easy adjustments to each heat map&rsquo;s appearance and plotting parameters. Heatmapper also allows users to interactively explore their numeric data values by hovering their cursor over each heat map, or by using a searchable/sortable data table view.</span></p>
<p><span>Ref&nbsp;https://www.ncbi.nlm.nih.gov/pubmed/27190236</span></p><p>Address of the bookmark: <a href="http://www2.heatmapper.ca/" rel="nofollow">http://www2.heatmapper.ca/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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