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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/34493?offset=490</link>
	<atom:link href="https://bioinformaticsonline.com/related/34493?offset=490" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35252/hgt-finder-a-new-tool-for-horizontal-gene-transfer-finding-and-application-to-aspergillus-genomes</guid>
	<pubDate>Wed, 17 Jan 2018 05:03:19 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35252/hgt-finder-a-new-tool-for-horizontal-gene-transfer-finding-and-application-to-aspergillus-genomes</link>
	<title><![CDATA[HGT-Finder: A New Tool for Horizontal Gene Transfer Finding and Application to Aspergillus genomes]]></title>
	<description><![CDATA[<p><span>HGT-Finder: </span></p>
<p><span>(i) can be used for HGT detection in both prokaryotes and eukaryotes, </span></p>
<p><span>(ii) can report a statistical&nbsp;</span><em>P</em><span>&nbsp;value for each gene to indicate how likely it is to be horizontally transferred, and </span></p>
<p><span>(iii) is fully automated (requires minimal human intervention), as well as very easy to install and run.&nbsp;</span></p><p>Address of the bookmark: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4626719/" rel="nofollow">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4626719/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<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/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>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33866/perlbrew-admin-free-perl-installation-management-tool</guid>
	<pubDate>Wed, 12 Jul 2017 03:53:08 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33866/perlbrew-admin-free-perl-installation-management-tool</link>
	<title><![CDATA[Perlbrew: admin-free perl installation management tool.]]></title>
	<description><![CDATA[<p>perlbrew is an admin-free perl installation management tool. The latest version is 0.79, read the release note:&nbsp;<a href="https://perlbrew.pl/Release-0.79.html">Release 0.79</a>.&nbsp;</p>
<p>Copy &amp; Paste this line into your terminal:</p>
<pre><code>\curl -L https://install.perlbrew.pl | bash
</code></pre>
<p>Or, if your system does not have curl but something else:</p>
<pre><code># Linux
\wget -O - https://install.perlbrew.pl | bash

# FreeBSD
\fetch -o- https://install.perlbrew.pl | sh
</code></pre>
<p>If you prefer to install with cpan, there are two steps:</p>
<pre><code>sudo cpan App::perlbrew
perlbrew init
</code></pre>
<p>If it is installed with cpan, the perlbrew executable should be installed as&nbsp;<code>/usr/bin/perlbrew</code>&nbsp;or&nbsp;<code>/usr/local/bin/perlbrew</code>. For all users who want to use perlbrew, a prior&nbsp;<code>perlbrew init</code>&nbsp;needs to be executed.</p>
<p>The default perlbrew root directory is&nbsp;<code>~/perl5/perlbrew</code>, which can be changed by setting&nbsp;<code>PERLBREW_ROOT</code>environment variable before the installation and initialization. For more advanced installation process, please read&nbsp;<a href="http://metacpan.org/module/App::perlbrew">the perlbrew document</a>.</p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="https://perlbrew.pl/" rel="nofollow">https://perlbrew.pl/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34571/mugsy-multiple-whole-genome-alignment-tool</guid>
	<pubDate>Fri, 08 Dec 2017 17:41:14 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34571/mugsy-multiple-whole-genome-alignment-tool</link>
	<title><![CDATA[Mugsy: multiple whole genome alignment tool]]></title>
	<description><![CDATA[<p><span>Mugsy is a multiple whole genome aligner. Mugsy uses Nucmer for pairwise alignment, a custom graph based segmentation procedure for identifying collinear regions, and the segment-based progressive multiple alignment strategy from Seqan::TCoffee. Mugsy accepts draft genomes in the form of multi-FASTA files and does not require a reference genome.</span></p>
<p>To cite Mugsy, use:</p>
<p>Angiuoli SV and Salzberg SL.&nbsp;<a href="http://bioinformatics.oxfordjournals.org/content/27/3/334">Mugsy: Fast multiple alignment of closely related whole genomes.</a><em>Bioinformatics</em>&nbsp;2011 27(3):334-4</p><p>Address of the bookmark: <a href="http://mugsy.sourceforge.net/" rel="nofollow">http://mugsy.sourceforge.net/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35249/gpopsim-a-simulation-tool-for-whole-genome-genetic-data</guid>
	<pubDate>Wed, 17 Jan 2018 03:47:46 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35249/gpopsim-a-simulation-tool-for-whole-genome-genetic-data</link>
	<title><![CDATA[GPOPSIM: a simulation tool for whole-genome genetic data]]></title>
	<description><![CDATA[<p><span>GPOPSIM is a simulation tool for pedigree, phenotypes, and genomic data, with a variety of population and genome structures and trait genetic architectures. It provides flexible parameter settings for a wide discipline of users, especially can simulate multiple genetically correlated traits with desired genetic parameters and underlying genetic architectures.</span></p><p>Address of the bookmark: <a href="https://github.com/SCAU-AnimalGenetics/GPOPSIM" rel="nofollow">https://github.com/SCAU-AnimalGenetics/GPOPSIM</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36833/bfc-a-standalone-high-performance-tool-for-correcting-sequencing-errors-from-illumina-sequencing-data</guid>
	<pubDate>Thu, 31 May 2018 09:35:23 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36833/bfc-a-standalone-high-performance-tool-for-correcting-sequencing-errors-from-illumina-sequencing-data</link>
	<title><![CDATA[BFC: a standalone high-performance tool for correcting sequencing errors from Illumina sequencing data]]></title>
	<description><![CDATA[BFC is a standalone high-performance tool for correcting sequencing errors from Illumina sequencing data. It is specifically designed for high-coverage whole-genome human data, though also performs well for small genomes.

The BFC algorithm is a variant of the classical spectrum alignment algorithm introduced by Pevzner et al (2001). It uses an exhaustive search to find a k-mer path through a read that minimizes a heuristic objective function jointly considering penalties on correction, quality and k-mer support. This algorithm was first implemented in my fermi assembler and then refined a few times in fermi, fermi2 and now in BFC. In the k-mer counting phase, BFC uses a blocked bloom filter to filter out most singleton k-mers and keeps the rest in a hash table (Melsted and Pritchard, 2011). The use of bloom filter is how BFC is named, though other correctors such as Lighter and Bless actually rely more on bloom filter than BFC.

https://github.com/lh3/bfc<p>Address of the bookmark: <a href="https://github.com/lh3/bfc" rel="nofollow">https://github.com/lh3/bfc</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36907/higlass-a-tool-for-exploring-genomic-contact-matrices-and-tracks</guid>
	<pubDate>Mon, 11 Jun 2018 09:44:49 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36907/higlass-a-tool-for-exploring-genomic-contact-matrices-and-tracks</link>
	<title><![CDATA[HiGlass: a tool for exploring genomic contact matrices and tracks.]]></title>
	<description><![CDATA[HiGlass is a tool for exploring genomic contact matrices and tracks. Please take a look at the examples and documentation for a description of the ways that it can be configured to explore and compare contact matrices. To load private data, HiGlass can be run locally within a Docker container. The HiC data in the examples below is from Rao et al. (2014)

http://higlass.io/<p>Address of the bookmark: <a href="http://higlass.io/" rel="nofollow">http://higlass.io/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37524/fmlrc-a-long-read-error-correction-tool-using-the-multi-string-burrows-wheeler-transform</guid>
	<pubDate>Fri, 10 Aug 2018 13:29:28 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37524/fmlrc-a-long-read-error-correction-tool-using-the-multi-string-burrows-wheeler-transform</link>
	<title><![CDATA[FMLRC: a long-read error correction tool using the multi-string Burrows Wheeler Transform]]></title>
	<description><![CDATA[<p><span>FMLRC, or FM-index Long Read Corrector, is a tool for performing hybrid correction of long read sequencing using the BWT and FM-index of short-read sequencing data. Given a BWT of the short-read sequencing data, FMLRC will build an FM-index and use that as an implicit de Bruijn graph. Each long read is then corrected independently by identifying low frequency k-mers in the long read and replacing them with the closest matching high frequency k-mers in the implicit de Bruijn graph. In contrast to other de Bruijn graph based implementations, FMLRC is not restricted to a particular k-mer size and instead uses a two pass method with both a short "k-mer" and a longer "K-mer". This allows FMLRC to correct through low complexity regions that are computational difficult for short k-mers.</span></p><p>Address of the bookmark: <a href="https://github.com/holtjma/fmlrc" rel="nofollow">https://github.com/holtjma/fmlrc</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>

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