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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/38443?offset=460</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/36418/r-350-has-been-released</guid>
	<pubDate>Thu, 26 Apr 2018 11:31:58 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/36418/r-350-has-been-released</link>
	<title><![CDATA[R 3.5.0 has been Released!]]></title>
	<description><![CDATA[<ul>
<li>The latest version of R is a major release! It comes with a ton of new features, including performance and speed improvements</li>
<li>All R packages will now be byte-compiled, hence boosting packages installed from GitHub</li>
<li>You may need to re-install all previously installed R packages; old scripts however will continue to work normally</li>
</ul><p>More at&nbsp;<a href="https://cran.r-project.org/doc/manuals/r-release/NEWS.html">https://cran.r-project.org/doc/manuals/r-release/NEWS.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37732/making-2d-hilbert-curve</guid>
	<pubDate>Mon, 17 Sep 2018 05:43:35 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37732/making-2d-hilbert-curve</link>
	<title><![CDATA[Making 2D Hilbert Curve]]></title>
	<description><![CDATA[<p><a href="https://en.wikipedia.org/wiki/Hilbert_curve">Hilbert curve</a>&nbsp;is a type of space-filling curves that folds one dimensional axis into a two dimensional space, but still keeps the locality. It has advantages to visualize data with long axis in following two aspects:</p>
<ol>
<li>greatly improve resolution of the visualization fron n to&nbsp;<span><span><span><span><span><span><span>&radic;</span></span><span><span><span><span>n</span></span></span></span></span></span></span></span><span>n</span></span>;</li>
<li>easy to visualize clusters because generally data points in the axis will also be close in the 2D space.</li>
</ol>
<p>This package aims to provide an easy and flexible way to visualize data through Hilbert curve. The implementation and example figures are based on following sources:</p>
<ul>
<li><a href="http://mkweb.bcgsc.ca/hilbert/">http://mkweb.bcgsc.ca/hilbert/</a></li>
<li><a href="http://corte.si/posts/code/hilbert/portrait/index.html">http://corte.si/posts/code/hilbert/portrait/index.html</a></li>
<li><a href="http://bioconductor.org/packages/devel/bioc/html/HilbertVis.html">http://bioconductor.org/packages/devel/bioc/html/HilbertVis.html</a></li>
</ul><p>Address of the bookmark: <a href="https://bioconductor.org/packages/devel/bioc/vignettes/HilbertCurve/inst/doc/HilbertCurve.html" rel="nofollow">https://bioconductor.org/packages/devel/bioc/vignettes/HilbertCurve/inst/doc/HilbertCurve.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38385/decipher-a-software-toolset-for-deciphering-and-managing-biological-sequences-efficiently-using-the-r</guid>
	<pubDate>Sun, 09 Dec 2018 19:06:17 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38385/decipher-a-software-toolset-for-deciphering-and-managing-biological-sequences-efficiently-using-the-r</link>
	<title><![CDATA[DECIPHER; a software toolset for deciphering and managing biological sequences efficiently using the R]]></title>
	<description><![CDATA[<p><span>DECIPHER is a software toolset that can be used for deciphering and managing biological sequences efficiently using the&nbsp;</span><a href="http://www.r-project.org/">R</a><span>&nbsp;programming language. The&nbsp;</span><a href="http://www.r-project.org/">R</a><span>&nbsp;package is distributed as platform independent source code under the&nbsp;</span><a href="http://www.gnu.org/copyleft/gpl.html">GPL version 3 license</a><span>. Some functionality of the program is accessible online through web tools.</span></p>
<p><span style="font-size: medium; text-align: justify;">&nbsp;</span></p><p>Address of the bookmark: <a href="http://www2.decipher.codes/" rel="nofollow">http://www2.decipher.codes/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<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/39956/alluvial-diagram</guid>
	<pubDate>Sat, 21 Sep 2019 07:31:08 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39956/alluvial-diagram</link>
	<title><![CDATA[alluvial diagram]]></title>
	<description><![CDATA[<p><span style="color: #000000; font-size: 14px; font-style: normal; font-weight: 400; text-align: start; background-color: #ffffff; float: none;">Alluvial diagram is a variant of a Parallel Coordinates Plot (PCP) but for categorical variables. Variables are assigned to vertical axes that are parallel. Values are represented with blocks on each axis. Observations are represented with<span>&nbsp;</span></span><em style="color: #000000; font-size: 14px; font-weight: 400; text-align: start; background-color: #ffffff;">alluvia</em><span style="color: #000000; font-size: 14px; font-style: normal; font-weight: 400; text-align: start; background-color: #ffffff; float: none;"><span>&nbsp;</span>(sing. &ldquo;alluvium&rdquo;) spanning across all the axes.</span></p><p>Address of the bookmark: <a href="https://cran.r-project.org/web/packages/alluvial/vignettes/alluvial.html" rel="nofollow">https://cran.r-project.org/web/packages/alluvial/vignettes/alluvial.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40721/efs-an-ensemble-feature-selection-tool-implemented-as-r-package-and-web-application</guid>
	<pubDate>Tue, 28 Jan 2020 05:12:23 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40721/efs-an-ensemble-feature-selection-tool-implemented-as-r-package-and-web-application</link>
	<title><![CDATA[EFS: an ensemble feature selection tool implemented as R-package and web-application]]></title>
	<description><![CDATA[<p><span>The software EFS (Ensemble Feature Selection) makes use of multiple feature selection methods and combines their normalized outputs to a quantitative ensemble importance. Currently, eight different feature selection methods have been integrated in EFS, which can be used separately or combined in an ensemble.</span></p>
<p><a href="https://biodatamining.biomedcentral.com/articles/10.1186/s13040-017-0142-8">https://biodatamining.biomedcentral.com/articles/10.1186/s13040-017-0142-8</a></p><p>Address of the bookmark: <a href="http://efs.heiderlab.de/" rel="nofollow">http://efs.heiderlab.de/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/41043/postdoctoral-scientist-genome-analytics-genome-bioinformatics-mf</guid>
  <pubDate>Sun, 16 Feb 2020 02:57:40 -0600</pubDate>
  <link></link>
  <title><![CDATA[Postdoctoral scientist genome analytics/ genome bioinformatics (m/f/*)]]></title>
  <description><![CDATA[
<p>https://www.uksh.de/jobs/Stellenangebote-nr-20190570-p-8.html<br />Your profile:<br />Degree in bioinformatics, biostatistics, or equivalent<br />Experience in the processing and analysis of large-scale genomics data using compute clusters / high-performance computing<br />Strong competence in working in Unix/Linux environments (shell)<br />Strong programming skills (in particular: Python, R, Perl)<br />Experience with using git and snakemake<br />Fluent English language skills, both spoken and written<br />Strong communication skills and motivation to work in a young, interdisciplinary, dynamic team</p>

<p>Additional Information:</p>

<p>If you have any questions about scientific aspects of this position, please contact Prof. Lars Bertram, head of LIGA (lars.bertram@uni-luebeck.de).</p>

<p>Please contact Ms. Anna Wolbert for further questions about administrative details (recruiting@uksh.de).</p>

<p>Weitere Informationen erhalten Sie auch unter www.uksh.de/karriere.</p>

<p>Wir freuen uns auf Ihre Bewerbung bis zum 15.03.2020 unter Angabe unserer Ausschreibungsnummer 20190570.119.CL.</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42012/phewas-r-package-is-designed-to-provide-an-accessible-interface-to-the-phenome-wide-association-study</guid>
	<pubDate>Thu, 30 Jul 2020 22:06:37 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42012/phewas-r-package-is-designed-to-provide-an-accessible-interface-to-the-phenome-wide-association-study</link>
	<title><![CDATA[PheWAS: R package is designed to provide an accessible interface to the phenome wide association study]]></title>
	<description><![CDATA[<p>The PheWAS R package is designed to provide an accessible interface to the phenome wide association study. For a description of the methods available and some simple examples, please see the&nbsp;<a href="https://github.com/PheWAS/PheWAS/blob/master/inst/doc/PheWAS-package.pdf?raw=true">package vignette</a>&nbsp;or the R documentation. For installation help, see below. ##Installing the PheWAS Package The PheWAS package can be installed using the devtools package. The following code when executed in R will get you started:</p>
<pre><code>install.packages("devtools")
#It may be necessary to install required as not all package dependencies are installed by devtools:
install.packages(c("dplyr","tidyr","ggplot2","MASS","meta","ggrepel","DT"))
devtools::install_github("PheWAS/PheWAS")
library(PheWAS)</code></pre><p>Address of the bookmark: <a href="https://github.com/PheWAS/PheWAS" rel="nofollow">https://github.com/PheWAS/PheWAS</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43546/introduction-to-phylogenies-in-r</guid>
	<pubDate>Wed, 13 Oct 2021 02:27:21 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43546/introduction-to-phylogenies-in-r</link>
	<title><![CDATA[Introduction to phylogenies in R]]></title>
	<description><![CDATA[<p><span>R phylogenetics is built on the contributed packages for phylogenetics in R, and there are many such packages. Let's begin today by installing a few critical packages, such as ape, phangorn, phytools, and geiger. To get the most recent CRAN version of these packages, you will need to have R 3.3.x installed on your computer!</span></p><p>Address of the bookmark: <a href="http://www.phytools.org/Cordoba2017/ex/2/Intro-to-phylogenies.html" rel="nofollow">http://www.phytools.org/Cordoba2017/ex/2/Intro-to-phylogenies.html</a></p>]]></description>
	<dc:creator>Abhi</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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