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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/38561?offset=190</link>
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	<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/30459/prodigal-prokaryotic-dynamic-programming-genefinding-algorithm</guid>
	<pubDate>Thu, 29 Dec 2016 03:26:45 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30459/prodigal-prokaryotic-dynamic-programming-genefinding-algorithm</link>
	<title><![CDATA[Prodigal (Prokaryotic Dynamic Programming Genefinding Algorithm)]]></title>
	<description><![CDATA[<p><span>Prodigal (</span><strong>Pro</strong><span>karyotic&nbsp;</span><strong>Dy</strong><span>namic Programming&nbsp;</span><strong>G</strong><span>enefinding&nbsp;</span><strong>Al</strong><span>gorithm) is a microbial (bacterial and archaeal) gene finding program developed at Oak Ridge National Laboratory and the University of Tennessee. Key features of Prodigal include:</span></p>
<ul>
<li><strong>Speed</strong>: Prodigal is an extremely fast gene recognition tool (written in very vanilla C). It can analyze an entire microbial genome in 30 seconds or less.</li>
<li><strong>Accuracy</strong>: Prodigal is a highly accurate gene finder. It correctly locates the 3' end of every gene in the experimentally verified Ecogene data set (except those containing introns). It possesses a very sophisticated ribosomal binding site scoring system that enables it to locate the translation initiation site with great accuracy (96% of the 5' ends in the Ecogene data set are located correctly).</li>
<li><strong>Specificity</strong>: Prodigal's false positive rate compares favorably with other gene identification programs, and usually falls under 5%.</li>
<li><strong>GC-Content Indifferent</strong>: Prodigal performs well even in high GC genomes, with over a 90% perfect match (5'+3') to the&nbsp;<em>Pseudomonas aeruginosa</em>&nbsp;curated annotations.</li>
<li><strong>Metagenomic Version</strong>: Prodigal can run in metagenomic mode and analyze sequences even when the organism is unknown.</li>
<li><strong>Ease of Use</strong>: Prodigal can be run in one step on a single genomic sequence or on a draft genome containing many sequences. It does not need to be supplied with any knowledge of the organism, as it learns all the properties it needs to on its own.</li>
<li><strong>Open Source</strong>: Prodigal source code is freely available under the General Public License.</li>
</ul>
<p>&nbsp;</p>
<div style="text-align: center;"><strong>Download the latest version of Prodigal at&nbsp;<a href="http://github.com/hyattpd/prodigal/releases/">the Prodigal github page.</a></strong>&nbsp;<br>or&nbsp;<br><strong>Browse the&nbsp;<a href="http://github.com/hyattpd/prodigal/wiki">wiki documenation.</a></strong>&nbsp;</div><p>Address of the bookmark: <a href="http://prodigal.ornl.gov/" rel="nofollow">http://prodigal.ornl.gov/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</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/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/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>
<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/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>

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