<?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/21367?offset=970</link>
	<atom:link href="https://bioinformaticsonline.com/related/21367?offset=970" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/12288/genomic-medicine-bruce-korf-2014</guid>
	<pubDate>Tue, 24 Jun 2014 07:58:44 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/12288/genomic-medicine-bruce-korf-2014</link>
	<title><![CDATA[Genomic Medicine - Bruce Korf (2014)]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/FYldIrsXHKw" frameborder="0" allowfullscreen></iframe>May 21, 2014 - Current Topics in Genome Analysis 2014
A lecture series covering contemporary areas in genomics and bioinformatics. More: http://www.genome.gov/COURSE2014]]></description>
	
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39917/chromomap-an-r-package-for-interactive-visualization-and-annotation-of-chromosomes</guid>
	<pubDate>Sat, 07 Sep 2019 10:45:31 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39917/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><code>chromoMap</code>&nbsp;provides interactive, configurable and elegant graphics visualization of chromosomes or chromosomal regions allowing users to map chromosome elements (like genes,SNPs etc.) on the chromosome plot.Each chromosome is composed of loci(representing a specific range determined based on chromosome length) that, on hover, shows details about the annotations in that locus range. The plots can be saved as HTML documents that can be shared easily. In addition, you can include them in R Markdown or in R Shiny applications.</p>
<p>Some of the prominent features of the package are:</p>
<ul>
<li>visualizing polyploidy simultaneously on the same plot.</li>
<li>annotating groups of elements as distinct colors.</li>
<li>creating chromosome heatmaps.</li>
<li>adjusting chromosome range or visualizing chromosome regions such as genes</li>
<li>adding labels to the plot</li>
<li>adding hyperlinks to each element</li>
</ul><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>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/40085/github-replacement</guid>
	<pubDate>Thu, 26 Sep 2019 03:42:39 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/40085/github-replacement</link>
	<title><![CDATA[Github replacement !]]></title>
	<description><![CDATA[<p><span>For a number of reasons researchers have been trying out&nbsp;</span><a href="https://www.noamross.net/2019/09/24/drake-docker-and-gitlab-ci/gitlab.com" target="_blank">GitLab</a><span>&nbsp;as a replacement&nbsp;</span><span>for for both GitHub and various continuous integration systems, and have&nbsp;</span><span>been exploring configurations useful for model-fitting pipelines. Researchers turned&nbsp;</span><span>one of these into an&nbsp;</span><a href="https://gitlab.com/ecohealthalliance/drake-gitlab-docker-example" target="_blank">example repository</a><span>&nbsp;that shows how to use GitLab together&nbsp;</span><span>with the&nbsp;</span><a href="https://www.rocker-project.org/" target="_blank">Rocker</a><span>&nbsp;Docker images and the&nbsp;</span><a href="https://docs.ropensci.org/drake/" target="_blank"><strong>drake</strong></a><span>&nbsp;build system to reproducibly run a project pipeline, using the cacheing functionality across all three tools to&nbsp;</span><span>make things reasonably speedy and enable both local and remote builds. </span></p><p><span>Check it out&nbsp;</span><span>at&nbsp;</span><a href="https://gitlab.com/ecohealthalliance/drake-gitlab-docker-example" target="_blank">https://gitlab.com/ecohealthalliance/drake-gitlab-docker-example</a><span>.</span></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/40770/scientist-bioinformatics-positions</guid>
  <pubDate>Thu, 30 Jan 2020 06:53:40 -0600</pubDate>
  <link></link>
  <title><![CDATA[Scientist Bioinformatics Positions]]></title>
  <description><![CDATA[
<p>Bioinformatics-Multi_Omics_Integration</p>

<p>https://www.researchgate.net/job/939073_Senior_Scientist_Bioinformatics-Multi_Omics_Integration</p>

<p> <br />Senior_Scientist_Bioinformatics-Transcriptomics_Analysis     </p>

<p>https://www.researchgate.net/job/939075_Senior_Scientist_Bioinformatics-Transcriptomics_Analysis-Belgium_France_Switzerland_The_Netherlands</p>

<p>Senior Scientist Bioinformatics - Network Analytics</p>

<p>https://www.researchgate.net/job/939070_Senior_Scientist_Bioinformatics-Network_Analytics_Belgium_France_Switzerland_the_Netherlands</p>

<p>Team Leader Bioinformatics Data Sciences - Mechelen, Belgium</p>

<p>https://www.researchgate.net/job/938787_Team_Leader_Bioinformatics_Data_Sciences-Mechelen_Belgium</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41272/rainbowr-reliable-association-inference-by-optimizing-weights-with-r-r-package-for-snp-set-gwas-and-multi-kernel-mixed-model</guid>
	<pubDate>Fri, 28 Feb 2020 23:27:37 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41272/rainbowr-reliable-association-inference-by-optimizing-weights-with-r-r-package-for-snp-set-gwas-and-multi-kernel-mixed-model</link>
	<title><![CDATA[RAINBOWR: Reliable Association INference By Optimizing Weights with R (R package for SNP-set GWAS and multi-kernel mixed model)]]></title>
	<description><![CDATA[<p><code>RAINBOWR</code>(Reliable Association INference By Optimizing Weights with R) is a package to perform several types of <code>GWAS</code> as follows.</p>
<ul>
<li>Single-SNP GWAS with <code>RGWAS.normal</code> function</li>
<li>SNP-set (or gene set) GWAS with <code>RGWAS.multisnp</code> function (which tests multiple SNPs at the same time)</li>
<li>Check epistatic (SNP-set x SNP-set interaction) effects with <code>RGWAS.epistasis</code> (very slow and less reliable)</li>
</ul>
<p>https://github.com/KosukeHamazaki/RAINBOWR</p>
<p>https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007663</p>
<p>https://cran.r-project.org/web/packages/RAINBOWR/index.html</p><p>Address of the bookmark: <a href="https://github.com/KosukeHamazaki/RAINBOWR" rel="nofollow">https://github.com/KosukeHamazaki/RAINBOWR</a></p>]]></description>
	<dc:creator>Surabhi Chaudhary</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42299/platypus-%E2%80%93-r-package-for-object-detection-and-image-segmentation</guid>
	<pubDate>Mon, 09 Nov 2020 02:56:25 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42299/platypus-%E2%80%93-r-package-for-object-detection-and-image-segmentation</link>
	<title><![CDATA[Platypus – R package for object detection and image segmentation.]]></title>
	<description><![CDATA[<p><a href="https://github.com/maju116/platypus" target="_blank">platypus</a>&nbsp;is an R package for object detection and semantic segmentation. Currently using&nbsp;</p>
<div>platypus&nbsp;you can perform:</div>
<ul>
<li>multi-class semantic segmentation using&nbsp;U-Net&nbsp;architecture</li>
<li>multi-class object detection using&nbsp;YOLOv3&nbsp;architecture</li>
</ul>
<p>You can install the latest version of&nbsp;platypus&nbsp;with&nbsp;remotes&nbsp;package:</p>
<div>
<div>
<div>
<div>remotes::install_github("maju116/platypus")</div>
</div>
</div>
</div>
<p>Note that in order to install&nbsp;platypus&nbsp;you need to install&nbsp;keras&nbsp;and&nbsp;tensorflow&nbsp;packages and&nbsp;Tensorflow&nbsp;version&nbsp;&gt;= 2.0.0&nbsp;(&nbsp;Tensorflow 1.x&nbsp;will not be supported!)</p><p>Address of the bookmark: <a href="https://github.com/maju116/platypus" rel="nofollow">https://github.com/maju116/platypus</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43848/r-shiny-in-life-sciences-%E2%80%93-top-7-dashboard-examples</guid>
	<pubDate>Fri, 01 Apr 2022 19:05:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43848/r-shiny-in-life-sciences-%E2%80%93-top-7-dashboard-examples</link>
	<title><![CDATA[R Shiny in Life Sciences – Top 7 Dashboard Examples]]></title>
	<description><![CDATA[<p><span>&nbsp;R Shiny is one of the easiest ways for developers to make production-ready dashboards when speed and functionality are crucial. Shiny is approachable with a lot of documentation available, and because of this, a lot of developers/researchers with non-coding backgrounds are able to produce some impressive results. The whole ecosystem is easy to get your head around and pretty much limitless with regard to what you can do.</span></p><p>Address of the bookmark: <a href="https://www.r-bloggers.com/2022/03/r-shiny-in-life-sciences-top-7-dashboard-examples/" rel="nofollow">https://www.r-bloggers.com/2022/03/r-shiny-in-life-sciences-top-7-dashboard-examples/</a></p>]]></description>
	<dc:creator>Neel</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/news/view/1737/perl-in-a-day</guid>
	<pubDate>Sat, 10 Aug 2013 21:14:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/1737/perl-in-a-day</link>
	<title><![CDATA[Perl in a day !!]]></title>
	<description><![CDATA[<p>This pdf based tutorial in good resource to understand the basic of Perl in a day</p><p><a href="http://ritg.med.harvard.edu/training/perl/RC_Perl_Intro.pdf">http://ritg.med.harvard.edu/training/perl/RC_Perl_Intro.pdf</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/14186/pybedtools</guid>
	<pubDate>Wed, 20 Aug 2014 01:03:41 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/14186/pybedtools</link>
	<title><![CDATA[pybedtools]]></title>
	<description><![CDATA[<p>pybedtools is a Python wrapper for Aaron Quinlan's BEDtools programs (https://github.com/arq5x/bedtools), which are widely used for genomic interval manipulation or "genome algebra". pybedtools extends BEDTools by offering feature-level manipulations from with Python. See full online documentation, including installation instructions, at http://pythonhosted.org/pybedtools/.</p><p>More at http://pythonhosted.org/pybedtools/</p><p>A powerful toolset for genome arithmetic.http://code.google.com/p/bedtools/</p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>

</channel>
</rss>