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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/40707?offset=100</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26252/recombination-detection-tool</guid>
	<pubDate>Tue, 02 Feb 2016 10:11:14 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26252/recombination-detection-tool</link>
	<title><![CDATA[Recombination detection tool]]></title>
	<description><![CDATA[<p>A program to detect recombination hotspots using population genetic data.</p>
<p>More at https://github.com/auton1/LDhot</p><p>Address of the bookmark: <a href="https://github.com/auton1/LDhot" rel="nofollow">https://github.com/auton1/LDhot</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27099/rasttk-algorithm-for-building-custom-annotation-pipelines-and-annotating-batches-of-genomes</guid>
	<pubDate>Wed, 27 Apr 2016 11:07:59 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27099/rasttk-algorithm-for-building-custom-annotation-pipelines-and-annotating-batches-of-genomes</link>
	<title><![CDATA[RASTtk : algorithm for building custom annotation pipelines and annotating batches of genomes]]></title>
	<description><![CDATA[<p>The RAST (Rapid Annotation using Subsystem Technology) annotation engine was built in 2008 to annotate bacterial and archaeal genomes. It works by offering a standard software pipeline for identifying genomic features (i.e., protein-encoding genes and RNA) and annotating their functions. Recently, in order to make RAST a more useful research tool and to keep pace with advancements in bioinformatics, it has become desirable to build a version of RAST that is both customizable and extensible. In this paper, we describe the RAST tool kit (RASTtk), a modular version of RAST that enables researchers to build custom annotation pipelines. RASTtk offers a choice of software for identifying and annotating genomic features as well as the ability to add custom features to an annotation job. RASTtk also accommodates the batch submission of genomes and the ability to customize annotation protocols for batch submissions. This is the first major software restructuring of RAST since its inception.</p>
<p>More at http://www.nature.com/articles/srep08365</p><p>Address of the bookmark: <a href="http://rast.nmpdr.org/" rel="nofollow">http://rast.nmpdr.org/</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27430/mosaik-a-hash-based-algorithm-for-accurate-next-generation-sequencing-short-read-mapping</guid>
	<pubDate>Fri, 20 May 2016 18:53:49 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27430/mosaik-a-hash-based-algorithm-for-accurate-next-generation-sequencing-short-read-mapping</link>
	<title><![CDATA[MOSAIK: A Hash-Based Algorithm for Accurate Next-Generation Sequencing Short-Read Mapping]]></title>
	<description><![CDATA[<p><span>MOSAIK is a stable, sensitive and open-source program for mapping second and third-generation sequencing reads to a reference genome. Uniquely among current mapping tools, MOSAIK can align reads generated by all the major sequencing technologies, including Illumina, Applied Biosystems SOLiD, Roche 454, Ion Torrent and Pacific BioSciences SMRT. Indeed, MOSAIK was the only aligner to provide consistent mappings for all the generated data (sequencing technologies, low-coverage and exome) in the 1000 Genomes Project. To provide highly accurate alignments, MOSAIK employs a hash clustering strategy coupled with the Smith-Waterman algorithm. This method is well-suited to capture mismatches as well as short insertions and deletions. To support the growing interest in larger structural variant (SV) discovery, MOSAIK provides explicit support for handling known-sequence SVs, e.g. mobile element insertions (MEIs) as well as generating outputs tailored to aid in SV discovery.</span></p><p>Address of the bookmark: <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0090581" rel="nofollow">http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0090581</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27818/gaemr</guid>
	<pubDate>Tue, 14 Jun 2016 06:18:37 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27818/gaemr</link>
	<title><![CDATA[GAEMR]]></title>
	<description><![CDATA[<p>The&nbsp;<span>G</span>enome&nbsp;<span>A</span>ssembly&nbsp;<span>E</span>valuation&nbsp;<span>M</span>etrics and&nbsp;<span>R</span>eporting (GAEMR) package is an assembly analysis framework composed a number of integrated modules. These modules can be executed as a single program to generate a complete analysis report, or executed individually to generate specific charts and tables. GAEMR standardizes input by converting a variety of read types to Binary Alignment Map (BAM) format, allowing a single input format to be entered into GAEMR&rsquo;s analysis pipeline, hence enabling the generation of standard reports.</p>
<p>GAEMR&rsquo;s analysis philosophy is centered on contiguity, correctness, and completeness -- how many pieces in an assembly composed of, how well those pieces accurately represent the genome sequenced, and how much of that genome is represented by those pieces. By performing over twenty different analyses based on these principles, GAEMR gives a clear picture of the condition of a genome assembly.&nbsp;</p><p>Address of the bookmark: <a href="https://www.broadinstitute.org/software/gaemr/" rel="nofollow">https://www.broadinstitute.org/software/gaemr/</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/pages/view/35033/bbsplit-read-binning-tool-for-metagenomes-and-contaminated-libraries</guid>
	<pubDate>Wed, 03 Jan 2018 00:25:27 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/35033/bbsplit-read-binning-tool-for-metagenomes-and-contaminated-libraries</link>
	<title><![CDATA[BBSplit: Read Binning Tool for Metagenomes and Contaminated Libraries]]></title>
	<description><![CDATA[<p>BBSplit internally uses BBMap to map reads to multiple genomes at once, and determine which genome they match best. This is different than with ordinary mapping. If a genome (say, human) contains an exact repeat somewhere, reads mapping to it will be mapped ambiguously. But if you want to determine whether reads are mouse or human, it does not matter whether they map ambiguously within human, only whether they are ambiguous between human and mouse. BBSplit tracks this additional ambiguity information and decides how to use it based on the &ldquo;ambig2&rdquo; flag. The normal use of BBSplit is like Seal, either quantifying how many reads go to each reference, or splitting the reads into multiple output files, one per reference. BBSplit can only be run using references indexed with BBSplit, as they contain additional information regarding which sequences came from which reference file.</p><p><span>BBSplit is a tool that bins reads by mapping to multiple references simultaneously, using&nbsp;</span><a href="http://seqanswers.com/forums/showthread.php?t=41057" target="_blank">BBMap</a><span>. The reads go to the bin of the reference they map to best. There are also disambiguation options, such that reads that map to multiple references can be binned with all of them, none of them, one of them, or put in a special "ambiguous" file for each of them. Paired reads will always be kept together.</span><br /><br /><span>For example, if you had a library of something that was contaminated with e.coli and salmonella, you could do this:</span><br /><br /><strong>bbsplit.sh in=reads.fq ref=ecoli.fa,salmonella.fa basename=out_%.fq outu=clean.fq int=t</strong><br /><br /><span>This will produce 3 output files:</span><br /><strong>out_ecoli.fq</strong><span>&nbsp;(ecoli reads)</span><br /><strong>out_salmonella.fq</strong><span>&nbsp;(salmonella reads)</span><br /><strong>clean.fq</strong><span>&nbsp;(unmapped reads)</span><br /><br /><span>In this case, "int=t" means that the input file is paired and interleaved. For single-end reads you would leave that out. For paired reads in 2 files, you would do this:</span><br /><strong>bbsplit.sh in1=reads1.fq in2=reads2.fq ref=ecoli.fa,salmonella.fa basename=out_%.fq outu1=clean1.fq outu2=clean2.fq</strong></p><p><strong><span>BBSplit is available here:</span><br /><a href="https://sourceforge.net/projects/bbmap/" target="_blank">https://sourceforge.net/projects/bbmap/</a></strong></p><p><span>The sensitivity can be raised to be equivalent to BBMap with these flags: "minratio=0.56 minhits=1 maxindel=16000"</span></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38581/cvit-chromosome-viewing-tool</guid>
	<pubDate>Wed, 02 Jan 2019 04:10:09 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38581/cvit-chromosome-viewing-tool</link>
	<title><![CDATA[CViT: Chromosome Viewing Tool]]></title>
	<description><![CDATA[<p><span>CViT - Chromosome Viewing Tool. A collection of Perl scripts that enable quick visualizations of features on linkage groups, psuedochromosomes or cytogenetic maps. Intended for whole-genome views of data but can be used to create images of single chromosomes/linkage groups, contigs, or BACs, or even proteins -- any feature that has a location on a backbone. Handles most standard genetic/genomic coordinate systems. Reads GFF3 data and produces a PNG or SVG image.</span></p>
<p><span>https://www.hindawi.com/journals/ijpg/2011/373875/</span></p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/cvit/" rel="nofollow">https://sourceforge.net/projects/cvit/</a></p>]]></description>
	<dc:creator>Rahul Nayak</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/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/34493/plast-a-fast-accurate-and-ngs-scalable-bank-to-bank-sequence-similarity-search-tool</guid>
	<pubDate>Fri, 01 Dec 2017 04:10:54 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34493/plast-a-fast-accurate-and-ngs-scalable-bank-to-bank-sequence-similarity-search-tool</link>
	<title><![CDATA[PLAST: A fast, accurate and NGS scalable bank-to-bank sequence similarity search tool]]></title>
	<description><![CDATA[<p><strong>PLAST is a fast, accurate and NGS scalable bank-to-bank sequence similarity search tool providing significant accelerations of seeds-based heuristic comparison methods, such as the Blast suite of algorithms.</strong></p>
<p><strong>Relying on unique software architecture, PLAST takes full advantage of recent multi-core personal computers without requiring any additional hardware devices.</strong></p>
<p>PLAST stands for&nbsp;<em>Parallel Local Sequence Alignment Search Tool&nbsp;</em>and is was&nbsp;<a href="http://www.biomedcentral.com/1471-2105/10/329" target="_blank">published in BMC Bioinformatics.</a></p>
<p>PLAST is a general purpose sequence comparison tool providing the following benefits:</p>
<ul>
<li>PLAST is a high-performance sequence comparison tool designed to compare two sets of sequences (query vs. reference),</li>
<li>Reduces the processing time of sequences comparisons while providing highest quality results,</li>
<li>Contains a fully integrated data filtering engine capable of selecting relevant hits with user-defined criteria (E-Value, identity, coverage, alignment length, etc.),</li>
<li>Does not require any additional hardware, since it is a software solution. It is easy to install, cost-effective, takes full advantage of multi-core processors and uses a small RAM footprint,</li>
<li>Ready to be used on desktop computer, cluster, cloud as well as within distributed system running Hadoop.</li>
</ul>
<p>https://plast.inria.fr/</p><p>Address of the bookmark: <a href="https://plast.inria.fr/" rel="nofollow">https://plast.inria.fr/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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