<?xml version='1.0'?><rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:georss="http://www.georss.org/georss" xmlns:atom="http://www.w3.org/2005/Atom" >
<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/41820?offset=250</link>
	<atom:link href="https://bioinformaticsonline.com/related/41820?offset=250" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41814/gggenes-a-ggplot2-extension-for-drawing-gene-arrow-maps</guid>
	<pubDate>Tue, 02 Jun 2020 11:43:34 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41814/gggenes-a-ggplot2-extension-for-drawing-gene-arrow-maps</link>
	<title><![CDATA[gggenes: a ggplot2 extension for drawing gene arrow maps.]]></title>
	<description><![CDATA[<p>Install the stable version of gggenes from CRAN:</p>
<p><code><a href="https://www.rdocumentation.org/packages/utils/topics/install.packages">install.packages("gggenes")</a></code></p>
<p>If you want the development version, install it from GitHub:</p>
<p><code><a href="https://www.rdocumentation.org/packages/devtools/topics/reexports">devtools::install_github("wilkox/gggenes")</a></code></p>
<p>More at&nbsp;<a href="https://github.com/wilkox/gggenes">https://github.com/wilkox/gggenes</a></p><p>Address of the bookmark: <a href="http://wilkox.org/gggenes" rel="nofollow">http://wilkox.org/gggenes</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42619/metaeuk-sensitive-high-throughput-gene-discovery-and-annotation-for-large-scale-eukaryotic-metagenomics</guid>
	<pubDate>Wed, 13 Jan 2021 19:29:32 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42619/metaeuk-sensitive-high-throughput-gene-discovery-and-annotation-for-large-scale-eukaryotic-metagenomics</link>
	<title><![CDATA[MetaEuk - sensitive, high-throughput gene discovery and annotation for large-scale eukaryotic metagenomics]]></title>
	<description><![CDATA[<p><span>MetaEuk is a modular toolkit designed for large-scale gene discovery and annotation in eukaryotic metagenomic contigs. Metaeuk combines the fast and sensitive homology search capabilities of&nbsp;</span><a href="https://github.com/soedinglab/MMseqs2">MMseqs2</a><span>&nbsp;with a dynamic programming procedure to recover optimal exons sets. It reduces redundancies in multiple discoveries of the same gene and resolves conflicting gene predictions on the same strand. MetaEuk is GPL-licensed open source software that is implemented in C++ and available for Linux and macOS. The software is designed to run on multiple cores.</span></p><p>Address of the bookmark: <a href="https://github.com/soedinglab/metaeuk" rel="nofollow">https://github.com/soedinglab/metaeuk</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/1178/r-package-for-visualising-go-enrichment</guid>
	<pubDate>Mon, 22 Jul 2013 12:25:09 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/1178/r-package-for-visualising-go-enrichment</link>
	<title><![CDATA[R package for visualising GO enrichment]]></title>
	<description><![CDATA[<p>An R package that visualizes the GO enrichment results as word clouds and arranges them together with figures of experimental data. This allows us to draw informative summary plots for analyses such as differential expression or clustering, where for each gene list we display its behaviour in the experiment alongside with its GO annotations.</p><p>Links @ http://raivokolde.github.io/GOsummaries/</p><p>Lab @ http://biit.cs.ut.ee/about/main</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27850/clusterprofiler</guid>
	<pubDate>Thu, 16 Jun 2016 18:57:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27850/clusterprofiler</link>
	<title><![CDATA[clusterProfiler]]></title>
	<description><![CDATA[<p>statistical analysis and visulization of functional profiles for genes and gene clusters<br><br>Bioconductor version: Release (3.3)<br><br>This package implements methods to analyze and visualize functional profiles (GO and KEGG) of gene and gene clusters.<br><br>Author: Guangchuang Yu &lt;guangchuangyu at gmail.com&gt; with contributions from Li-Gen Wang and Giovanni Dall'Olio.<br><br>Maintainer: Guangchuang Yu &lt;guangchuangyu at gmail.com&gt;<br><br>Citation (from within R, enter citation("clusterProfiler")):<br><br>Yu G, Wang L, Han Y and He Q (2012). &ldquo;clusterProfiler: an R package for comparing biological themes among gene clusters.&rdquo; OMICS: A Journal of Integrative Biology, 16(5), pp. 284-287.<br>Installation<br><br>To install this package, start R and enter:<br><br>## try http:// if https:// URLs are not supported<br>source("https://bioconductor.org/biocLite.R")<br>biocLite("clusterProfiler")</p>
<p>https://www.bioconductor.org/packages/devel/bioc/vignettes/clusterProfiler/inst/doc/clusterProfiler.html</p><p>Address of the bookmark: <a href="https://www.bioconductor.org/packages/devel/bioc/vignettes/clusterProfiler/inst/doc/clusterProfiler.html" rel="nofollow">https://www.bioconductor.org/packages/devel/bioc/vignettes/clusterProfiler/inst/doc/clusterProfiler.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

</channel>
</rss>