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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/41820?offset=130</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38443/genoplotr-plot-gene-and-genome-maps-project</guid>
	<pubDate>Wed, 12 Dec 2018 08:33:41 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38443/genoplotr-plot-gene-and-genome-maps-project</link>
	<title><![CDATA[genoPlotR - plot gene and genome maps project!]]></title>
	<description><![CDATA[<p>genoPlotR is a R package to produce reproducible, publication-grade graphics of gene and genome maps. It allows the user to read from usual format such as protein table files and blast results, as well as home-made tabular files.</p>
<h3>Features</h3>
<ul>
<li>Linear representation of several segments of DNA</li>
<li>Comparisons represented by areas between the segments (like Artemis, for example)</li>
<li>Reads from common formats: Genbank, EMBL, blast, Mauve, and from user-generated tab files</li>
<li>Plot several subsegments of the same segment on the same line, separated by a //</li>
<li>Automatic or manual placement of the segments on the plot</li>
<li>Add annotations to all the lines</li>
<li>Create smart, automatic annotations for genomes, based on gene names</li>
<li>Add a user-generated tree</li>
<li>Add a global scale or a scale to each line</li>
<li>Use user-defined graphical functions to represent genes</li>
<li></li>
</ul><p>Address of the bookmark: <a href="http://genoplotr.r-forge.r-project.org/" rel="nofollow">http://genoplotr.r-forge.r-project.org/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<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>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31278/metapred2cs</guid>
	<pubDate>Fri, 03 Mar 2017 05:15:07 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31278/metapred2cs</link>
	<title><![CDATA[MetaPred2CS]]></title>
	<description><![CDATA[<p style="text-align: justify;"><strong>MetaPred2CS Web server&nbsp;</strong>is a meta-predictor based on&nbsp;<a href="http://www.ncbi.nlm.nih.gov/pubmed/17160063">Support Vector Machine (SVM)</a>&nbsp;that combines 6 individual sequence based protein-protein interaction prediction methods to predict&nbsp;<strong>prokaryotic two-component system&nbsp;</strong>protein-protein interactions (PPIs). The methods implemented in MetaPred2CS are 2 co-evolutionary methods:&nbsp;<a href="http://www.ncbi.nlm.nih.gov/pubmed/11933068">in-silico two hybrid (i2h)</a>&nbsp;and&nbsp;<a href="http://www.ncbi.nlm.nih.gov/pubmed/11707606">mirror tree (MT)</a>&nbsp;methods and 4 genomics context based methods:&nbsp;<a href="http://www.ncbi.nlm.nih.gov/pubmed/15947018">phylogenetic profiling (PP)</a>,&nbsp;<a href="http://www.ncbi.nlm.nih.gov/pubmed/10573422">gene fusion (GF)</a>,&nbsp;<a href="http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.0030043">gene neighbourhood (GN)</a>&nbsp;and and&nbsp;<a href="http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.0030043">gene operon methods (GO)</a>.</p>
<p>&nbsp;http://metapred2cs.ibers.aber.ac.uk/</p><p>Address of the bookmark: <a href="https://github.com/martinjvickers/MetaPred2CS" rel="nofollow">https://github.com/martinjvickers/MetaPred2CS</a></p>]]></description>
	<dc:creator>Manisha Mishra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27847/anvio</guid>
	<pubDate>Thu, 16 Jun 2016 18:15:41 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27847/anvio</link>
	<title><![CDATA[Anvio]]></title>
	<description><![CDATA[<p>In a nutshell</p>
<p>Anvi&rsquo;o is an analysis and visualization platform for &lsquo;omics data.</p>
<p>Please find the methods paper here: https://peerj.com/articles/1319/</p>
<p>Anvi&rsquo;o would not have been possible without the help of many people who directly or indirectly contributed to its development. Here is the acknowledgements section of our methods paper</p>
<p><span>An analysis and visualization platform for 'omics data</span><span>&nbsp;</span><span><a href="http://merenlab.org/projects/anvio">http://merenlab.org/projects/anvio</a></span></p>
<p><span>Paper&nbsp;https://peerj.com/articles/1839/</span></p><p>Address of the bookmark: <a href="https://github.com/meren/anvio" rel="nofollow">https://github.com/meren/anvio</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/28199/genome-workbench-2107</guid>
	<pubDate>Fri, 01 Jul 2016 12:09:59 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/28199/genome-workbench-2107</link>
	<title><![CDATA[Genome Workbench 2.10.7]]></title>
	<description><![CDATA[<p>Genome Workbench 2.10.7 is here! New features include added support for local custom BLAST databases and improvements to Tree View.</p><p>For the full list of features, improvements and fixes, see the release notes:<a href="https://ncbi.nlm.nih.gov/tools/gbench/releasenotes" target="_blank">https://ncbi.nlm.nih.gov/tools/gbench/releasenotes</a></p><p>New Features</p><ul>
<li>BLAST Tool: added support for local custom BLAST databases</li>
<li>Graphical Sequence View: added log scaling option for graph tracks</li>
<li>Generic Table View:&nbsp;<a href="https://www.ncbi.nlm.nih.gov/tools/gbench/tutorial17">new tutorial</a>&nbsp;added</li>
</ul><p>Bug Fixes and Improvements</p><ul>
<li>Project Tree View: Genomic Collections/Assemblies now show accessions, not just names</li>
<li>Tree View: layout updated to better accommodate nodes of different sizes</li>
<li>Table Import Dialog (MacOS): fixed issue with table visibility</li>
<li>Fixed bug where different molecules IDs in GenBank could resolve to the same sequence</li>
<li>Graphical Sequence View: fixed issue where sequence track was not shown for some sequences</li>
<li>Graphical Sequence View: fixed protein coloration methods</li>
<li>Graphical Sequence View: improved rendering of Markers to better indicate boundaries and produce higher quality PDF images</li>
<li>Create Gene Model tool: fixed scenario when gene model tool failed with local sequences</li>
<li>Search View: ORF Finder &ndash; fixed incorrect protein lengths</li>
<li>Fixed bug with not opening project file (.gbp) on a click</li>
<li>Fixed issues in GVF import</li>
<li>Fixed BLAST Search tool against NCBI databases not working</li>
<li>Fixed tblastn (protein BLAST) not working in standalone mode</li>
<li>Fixed GTF export failure</li>
</ul>]]></description>
	<dc:creator>Gudiya Pal</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28415/scarpa</guid>
	<pubDate>Wed, 13 Jul 2016 07:59:25 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28415/scarpa</link>
	<title><![CDATA[Scarpa]]></title>
	<description><![CDATA[<p><strong>Scarpa</strong>&nbsp;is a stand-alone scaffolding tool for NGS data. It can be used together with virtually any genome assembler and any NGS read mapper that supports SAM format. Other features include support for multiple libraries and an option to estimate insert size distributions from data. Scarpa is available free of charge for academic and commercial use under the GNU General Public License (GPL).</p>
<p>See the&nbsp;<a href="http://compbio.cs.toronto.edu/hapsembler/hapsembler-2.21_manual.pdf">user manual</a>&nbsp;or the&nbsp;<a href="http://compbio.cs.toronto.edu/hapsembler/scarpa_paper.pdf">paper</a>&nbsp;for more information about Scarpa. Click&nbsp;<a href="http://compbio.cs.toronto.edu/hapsembler/ScarpaSupplementary.pdf">here</a>&nbsp;for the supplementary material.</p><p>Address of the bookmark: <a href="http://compbio.cs.toronto.edu/hapsembler/scarpa.html" rel="nofollow">http://compbio.cs.toronto.edu/hapsembler/scarpa.html</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31100/vaguevelvet-assembler-graphical-front-end</guid>
	<pubDate>Fri, 24 Feb 2017 08:56:49 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31100/vaguevelvet-assembler-graphical-front-end</link>
	<title><![CDATA[VAGUE:Velvet Assembler Graphical Front End]]></title>
	<description><![CDATA[<p>VAGUE is a vague acronym for "Velvet Assembler Graphical Front End", which means it is a GUI for the Velvet <em>de novo</em> assembler. The command line version of Velvet can be complicated for beginners to use, but VAGUE makes it clear and simple</p>
<p>More at&nbsp;http://www.vicbioinformatics.com/software.vague.shtml</p><p>Address of the bookmark: <a href="http://www.vicbioinformatics.com/software.vague.shtml" rel="nofollow">http://www.vicbioinformatics.com/software.vague.shtml</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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