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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/38541?offset=10</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39244/chromomap-an-r-package-for-interactive-visualization-and-annotation-of-chromosomes</guid>
	<pubDate>Fri, 12 Apr 2019 05:30:41 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39244/chromomap-an-r-package-for-interactive-visualization-and-annotation-of-chromosomes</link>
	<title><![CDATA[chromoMap-An R package for Interactive Visualization and Annotation of Chromosomes]]></title>
	<description><![CDATA[<p>Provides interactive, configurable and elegant graphics visualization of the chromosomes or chromosome regions of any living organism allowing users to map chromosome elements (like genes, SNPs etc.) on the chromosome plot. It introduces a special plot viz. the "chromosome heatmap" that, in addition to mapping elements, can visualize the data associated with chromosome elements (like gene expression) in the form of heat colors which can be highly advantageous in the scientific interpretations and research work. The package provide multiple features like visualizing multiple sets, chromosome heat-maps, group annotations, adding hyperlinks, and labelling. The plots can be saved as HTML documents that can be customized and shared easily. In addition, you can include them in R Markdown or in R 'Shiny' applications.</p><p>Address of the bookmark: <a href="https://cran.r-project.org/web/packages/chromoMap/vignettes/chromoMap.html" rel="nofollow">https://cran.r-project.org/web/packages/chromoMap/vignettes/chromoMap.html</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44414/reconplot-an-r-package-for-the-visualization-and-interpretation-of-genomic-rearrangements</guid>
	<pubDate>Thu, 14 Dec 2023 12:33:19 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44414/reconplot-an-r-package-for-the-visualization-and-interpretation-of-genomic-rearrangements</link>
	<title><![CDATA[ReConPlot: an R package for the visualization and interpretation of genomic rearrangements]]></title>
	<description><![CDATA[<p>ReConPlot (REarrangement and COpy Number PLOT), an R package that provides functionalities for the joint visualization of SCNAs and SVs across one or multiple chromosomes. ReConPlot is based on the popular ggplot2 package, thus allowing customization of plots and the generation of publication-quality figures with minimal effort.</p><p>Address of the bookmark: <a href="https://academic.oup.com/bioinformatics/article/39/12/btad719/7460198?login=false" rel="nofollow">https://academic.oup.com/bioinformatics/article/39/12/btad719/7460198?login=false</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27070/venn-diagrams-on-r-studio</guid>
	<pubDate>Mon, 25 Apr 2016 16:22:28 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27070/venn-diagrams-on-r-studio</link>
	<title><![CDATA[Venn Diagrams on R Studio]]></title>
	<description><![CDATA[<h3>First step: Install &amp; load &ldquo;VennDiagram&rdquo; package.</h3>
<pre><code><span># install.packages('VennDiagram')</span>
<span>library</span><span>(</span><span>VennDiagram</span><span>)</span>
</code></pre>
<h3>Second step: Load data</h3>
<p>Add filepath if &ldquo;catdoge.csv&rdquo; is not in working-directory.</p>
<pre><code><span>d</span> <span>&lt;-</span> <span>read.csv</span><span>(</span><span>"catdoge.csv"</span><span>)</span></code><br><br></pre><p>Address of the bookmark: <a href="http://rstudio-pubs-static.s3.amazonaws.com/13301_6641d73cfac741a59c0a851feb99e98b.html" rel="nofollow">http://rstudio-pubs-static.s3.amazonaws.com/13301_6641d73cfac741a59c0a851feb99e98b.html</a></p>]]></description>
	<dc:creator>Jitendra Prajapati</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40583/trelliscope-flexibly-visualize-large-complex-data-in-great-detail-from-within-the-r-statistical-programming-environment</guid>
	<pubDate>Tue, 21 Jan 2020 04:22:49 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40583/trelliscope-flexibly-visualize-large-complex-data-in-great-detail-from-within-the-r-statistical-programming-environment</link>
	<title><![CDATA[Trelliscope: flexibly visualize large, complex data in great detail from within the R statistical programming environment.]]></title>
	<description><![CDATA[<p>Trelliscope provides a way to flexibly visualize large, complex data in great detail from within the R statistical programming environment. Trelliscope is a component in the<span>&nbsp;</span><a href="http://deltarho.org/docs-trelliscope/deltarho.org">DeltaRho</a><span>&nbsp;</span>environment.</p>
<p>For those familiar with<span>&nbsp;</span><a href="http://cm.bell-labs.com/cm/ms/departments/sia/project/trellis/">Trellis Display</a>,<span>&nbsp;</span><a href="http://docs.ggplot2.org/0.9.3.1/facet_wrap.html">faceting in ggplot</a>, or the notion of<span>&nbsp;</span><a href="http://en.wikipedia.org/wiki/Small_multiple">small multiples</a>, Trelliscope provides a scalable way to break a set of data into pieces, apply a plot method to each piece, and then arrange those plots in a grid and interactively sort, filter, and query panels of the display based on metrics of interest. With Trelliscope, we are able to create multipanel displays on data with a very large number of subsets and view them in an interactive and meaningful way.</p><p>Address of the bookmark: <a href="http://deltarho.org/docs-trelliscope/#introduction" rel="nofollow">http://deltarho.org/docs-trelliscope/#introduction</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37460/revigo-reduced-visualize-gene-ontology</guid>
	<pubDate>Tue, 31 Jul 2018 05:28:42 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37460/revigo-reduced-visualize-gene-ontology</link>
	<title><![CDATA[REVIGO: Reduced Visualize gene ontology]]></title>
	<description><![CDATA[<div>REViGO can take long lists of Gene Ontology terms and summarize them by removing redundant GO terms. The remaining terms can be visualized in semantic similarity-based scatterplots, interactive graphs, or tag clouds.&nbsp;<a href="http://dx.doi.org/10.1371/journal.pone.0021800">More about REViGO...</a>&nbsp;|&nbsp;<a href="http://revigo.irb.hr/about_hr.jsp"><img src="http://revigo.irb.hr/gfx/croatian-wCrown.png" alt="In Croatian" title="" width="12" height="15" style="border: 0px;"></a></div>
<div>Please enter a list of Gene Ontology IDs below, each on its own line. The GO IDs may be followed by p-values or another quantity which describes the GO term in a way meaningful to you.&nbsp;<img src="http://revigo.irb.hr/gfx/qmark.png" alt="For instance, you may provide a p-value          (statistical significance), a fold change, enrichment, or some          directly measured quantity such as average signal intensity from          microarrays, ion count from mass spec, or read count from RNA-seq.          You may also provide more than one value per line, although only the          first value will be used in GO term selection/clustering." title="" width="16" height="15" style="border: 0px;"></div><p>Address of the bookmark: <a href="http://revigo.irb.hr/" rel="nofollow">http://revigo.irb.hr/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<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/news/view/19087/dcgor</guid>
	<pubDate>Sat, 08 Nov 2014 14:54:28 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/19087/dcgor</link>
	<title><![CDATA[dcGOR]]></title>
	<description><![CDATA[<p>An R package for analysing ontologies and protein domain annotations has been published in PLoS Computational Biology (http://dx.doi.org/10.1371/journal.pcbi.1003929). The package is distributed as part of CRAN (http://cran.r-project.org/package=dcGOR), and also at GitHub for version control.<br /><br />The dedicated website is available in http://supfam.org/dcGOR, from which several demos are also provided:<br /><br />1. Analysing SCOP domains: http://supfam.org/dcGOR/demo-Fang.html<br /><br />2. Analysing Pfam domains: http://supfam.org/dcGOR/demo-Basu.html<br /><br />3. Analysing InterPro domains: http://supfam.org/dcGOR/demo-Customisation.html<br /><br />&nbsp;</p>]]></description>
	<dc:creator>Martin Jones</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27427/rcircos-an-r-package-for-circos-2d-track-plots</guid>
	<pubDate>Fri, 20 May 2016 11:01:13 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27427/rcircos-an-r-package-for-circos-2d-track-plots</link>
	<title><![CDATA[RCircos: an R package for Circos 2D track plots]]></title>
	<description><![CDATA[<p>RCircos package provides a simple and flexible way to make Circos 2D track plots with R and could be easily integrated into other R data processing and graphic manipulation pipelines for presenting large-scale multi-sample genomic research data. It can also serve as a base tool to generate complex Circos images.</p>
<p>More at https://bitbucket.org/henryhzhang/rcircos/src</p><p>Address of the bookmark: <a href="https://bitbucket.org/henryhzhang/rcircos/src" rel="nofollow">https://bitbucket.org/henryhzhang/rcircos/src</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38819/upsetr-an-r-package-for-the-visualization-of-intersecting-sets-and-their-properties</guid>
	<pubDate>Mon, 28 Jan 2019 18:38:44 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38819/upsetr-an-r-package-for-the-visualization-of-intersecting-sets-and-their-properties</link>
	<title><![CDATA[UpSetR: An R Package for the Visualization of Intersecting Sets and their Properties]]></title>
	<description><![CDATA[<p>UpSetR generates static&nbsp;<a href="http://vcg.github.io/upset/">UpSet</a>&nbsp;plots. The UpSet technique visualizes set intersections in a matrix layout and introduces aggregates based on groupings and queries. The matrix layout enables the effective representation of associated data, such as the number of elements in the aggregates and intersections, as well as additional summary statistics derived from subset or element attributes.</p>
<p>For further details about the original technique see the&nbsp;<a href="http://vcg.github.io/upset/about/">UpSet website</a>. You can also check out the&nbsp;<a href="https://gehlenborglab.shinyapps.io/upsetr/">UpSetR shiny app</a>.&nbsp;<a href="https://github.com/hms-dbmi/UpSetR-shiny">Here is the source code</a>&nbsp;for the shiny wrapper.</p>
<p>A&nbsp;<a href="https://github.com/ImSoErgodic/py-upset">Python package</a>&nbsp;called&nbsp;<a href="https://github.com/ImSoErgodic/py-upset">py-upset</a>&nbsp;to create UpSet plots has been created by GitHub user&nbsp;<a href="https://github.com/ImSoErgodic">ImSoErgodic</a>.</p><p>Address of the bookmark: <a href="https://github.com/hms-dbmi/UpSetR/" rel="nofollow">https://github.com/hms-dbmi/UpSetR/</a></p>]]></description>
	<dc:creator>BioStar</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>

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