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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/44541?offset=200</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26729/ga4gh-data-working-group</guid>
	<pubDate>Sun, 20 Mar 2016 23:13:07 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26729/ga4gh-data-working-group</link>
	<title><![CDATA[GA4GH Data Working Group]]></title>
	<description><![CDATA[<p>GA4GH Data Working Group</p>
<p>Led by David Haussler (UCSC) and Richard Durbin (Sanger Institute), the Data Working Group (DWG) of the Global Alliance brings together the leading Genome Institutes and Centers with IT industry leaders to create global standards and tools for the secure, privacy respecting and interoperable sharing of Genomic data.</p>
<p>More at&nbsp;http://ga4gh.org/#/</p><p>Address of the bookmark: <a href="http://ga4gh.org/#/" rel="nofollow">http://ga4gh.org/#/</a></p>]]></description>
	<dc:creator>Jitendra Prajapati</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38304/lordfast-sensitive-and-fast-alignment-search-tool-for-long-noisy-read-sequencing-data</guid>
	<pubDate>Tue, 27 Nov 2018 04:43:57 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38304/lordfast-sensitive-and-fast-alignment-search-tool-for-long-noisy-read-sequencing-data</link>
	<title><![CDATA[lordFAST: sensitive and Fast Alignment Search Tool for LOng noisy Read sequencing Data]]></title>
	<description><![CDATA[<p><span>lordFAST is a sensitive tool for mapping long reads with high error rates. lordFAST is specially designed for aligning reads from PacBio sequencing technology but provides the user the ability to change alignment parameters depending on the reads and application.</span></p>
<p>lordFAST, a novel long-read mapper that is specifically designed to align reads generated by PacBio and potentially other SMS technologies to a reference. lordFAST not only has higher sensitivity than the available alternatives, it is also among the fastest and has a very low memory footprint.</p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="https://github.com/vpc-ccg/lordfast" rel="nofollow">https://github.com/vpc-ccg/lordfast</a></p>]]></description>
	<dc:creator>BioJoker</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/42324/comparative-genomics-data-set-including-240-mammals-released</guid>
	<pubDate>Thu, 19 Nov 2020 06:45:39 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/42324/comparative-genomics-data-set-including-240-mammals-released</link>
	<title><![CDATA[Comparative Genomics Data Set Including 240 Mammals Released !]]></title>
	<description><![CDATA[<p>The genome of 130 mammals was sequenced by a large international consortium and the data was analyzed together with 110 existing genomes to allow scientists to identify the important positions in the DNA. This report, published in Nature today will help advance research on human disease mutations and inform how best to protect endangered species.</p><p>In addition to the knowledge of the human genome, all these genomes, widely sampled across mammals, can be used to research how particular organisms respond to different conditions. Some otters, for example, have a thick, water-resistant shell, and some rodents, but not all, have adapted to hibernation. These animal traits will help us to understand human traits, such as metabolic diseases.</p><p><img src="https://media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41586-020-2876-6/MediaObjects/41586_2020_2876_Fig1_HTML.png?as=webp" alt="image" style="border: 0px; border: 0px;"></p><p>With climate change and more animal ecosystems being threatened by human activity, the protection of endangered species is becoming increasingly important. Scientists have historically researched several people in various populations of a species to understand the genetic variation that occurs in that species. This is important for understanding how particular species can be protected. In this study, animals on the Red List of Endangered Species of the International Union for Conservation of Nature had fewer differences in their genomes, which is consistent with their endangered status.</p><p>Ref @&nbsp;A comparative genomics multitool for scientific discovery and conservation&nbsp;https://www.nature.com/articles/s41586-020-2876-6</p><p>&nbsp;Data at&nbsp;http://zoonomiaproject.org/</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44557/fundamentals-of-data-visualization-by-claus-o-wilke</guid>
	<pubDate>Sat, 08 Jun 2024 16:07:19 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44557/fundamentals-of-data-visualization-by-claus-o-wilke</link>
	<title><![CDATA[Fundamentals of Data Visualization by Claus O. Wilke]]></title>
	<description><![CDATA[<p><span><span>The book is meant as a guide to making visualizations that accurately reflect the data, tell a story, and look professional. It has grown out of my experience of working with students and postdocs in my laboratory on thousands of data visualizations. Over the years, I have noticed that the same issues arise over and over. I have attempted to collect my accumulated knowledge from these interactions in the form of this book.</span></span></p>
<p><span>The entire book is written in R Markdown, using RStudio as my text editor and the&nbsp;</span><span>bookdown</span><span>&nbsp;package to turn a collection of markdown documents into a coherent whole. The book&rsquo;s source code is hosted on GitHub, at&nbsp;</span><a href="https://github.com/clauswilke/dataviz">https://github.com/clauswilke/dataviz</a><span>.&nbsp;</span></p><p>Address of the bookmark: <a href="https://clauswilke.com/dataviz/" rel="nofollow">https://clauswilke.com/dataviz/</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/14218/pimp-your-brain-bioinformatics</guid>
	<pubDate>Wed, 20 Aug 2014 22:09:21 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/14218/pimp-your-brain-bioinformatics</link>
	<title><![CDATA[Pimp your brain: Bioinformatics]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/KqelGy6Q8nE" frameborder="0" allowfullscreen></iframe>Jan Lisec from the Max Planck Institute of Molecular Plant Physiology explains, in this "pimp your brain" episode, what bioinformatics is and why bioinformatics is so important and indispensable for biological research.

In the video serial "Pimp your brain" scientists from the Max Planck Institute of Molecular Plant Physiology describe their research. More videos from the 'Pimp your brain' serial are available on www.youtube.com/playlist?list=PL-l9VItC9Gn2Ur2Xj6PTOAkjLUlVPbIOO

More videos are available on www.mpimp-golm.mpg.de]]></description>
	
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43362/machine-learning-for-genomics</guid>
	<pubDate>Thu, 09 Sep 2021 11:26:32 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43362/machine-learning-for-genomics</link>
	<title><![CDATA[Machine Learning for Genomics]]></title>
	<description><![CDATA[<h3>Module 1: Statistics for genomics (2-8 August 2021)</h3>
<ul>
<li>A simple intro to statistical distributions</li>
<li>hypothesis testing</li>
<li>linear models.</li>
</ul>
<p>reading:&nbsp;<a href="http://compgenomr.github.io/book/stats.html">http://compgenomr.github.io/book/stats.html</a></p>
<p>slides:&nbsp;<a href="https://github.com/BIMSBbioinfo/compgen2021/tree/main/week1/compgen2021_stats.pdf">https://github.com/BIMSBbioinfo/compgen2021/tree/main/week1/compgen2021_stats.pdf</a></p>
<p>exercises+code:&nbsp;<a href="https://github.com/BIMSBbioinfo/compgen2021/tree/main/week1/">https://github.com/BIMSBbioinfo/compgen2021/tree/main/week1/</a></p>
<h3><a href="https://github.com/BIMSBbioinfo/compgen2021#module-2-unsupervised-learning-for-genomics-9-15-august-2021"></a>Module 2: Unsupervised learning for genomics (9-15 August 2021)</h3>
<ul>
<li>Understanding basic intuition behind machine learning approaches.</li>
<li>Using unsupervised learning to cluster and visualise data points</li>
<li>Dimension reduction techniques for visualisation and as input to clustering methods</li>
</ul>
<p>reading:&nbsp;<a href="http://compgenomr.github.io/book/unsupervisedLearning.html">http://compgenomr.github.io/book/unsupervisedLearning.html</a></p>
<p>slides:&nbsp;<a href="https://github.com/BIMSBbioinfo/compgen2021/tree/main/week2/compgen2021_unsupervisedLearning.pdf">https://github.com/BIMSBbioinfo/compgen2021/tree/main/week2/compgen2021_unsupervisedLearning.pdf</a></p>
<p>exercises+code:&nbsp;<a href="https://github.com/BIMSBbioinfo/compgen2021/tree/main/week2/">https://github.com/BIMSBbioinfo/compgen2021/tree/main/week2/</a></p>
<h3><a href="https://github.com/BIMSBbioinfo/compgen2021#module-3-supervised-learning-for-genomics-16-22-august-2021"></a>Module 3: Supervised learning for genomics (16-22 August 2021)</h3>
<ul>
<li>Understanding and using supervised learning methods for predictive purposes</li>
<li>How to measure prediction performance</li>
<li>Understand and use cross-validation and related concepts</li>
</ul>
<p>reading:&nbsp;<a href="http://compgenomr.github.io/book/supervisedLearning.html">http://compgenomr.github.io/book/supervisedLearning.html</a></p>
<p>slides:&nbsp;<a href="https://github.com/BIMSBbioinfo/compgen2021/tree/main/week3/compgen2021_supervisedLearning.pdf">https://github.com/BIMSBbioinfo/compgen2021/tree/main/week3/compgen2021_supervisedLearning.pdf</a></p>
<p>exercises+code:&nbsp;<a href="https://github.com/BIMSBbioinfo/compgen2021/tree/main/week3/">https://github.com/BIMSBbioinfo/compgen2021/tree/main/week3/</a></p>
<p>https://github.com/BIMSBbioinfo/compgen2021</p><p>Address of the bookmark: <a href="https://github.com/BIMSBbioinfo/compgen2021" rel="nofollow">https://github.com/BIMSBbioinfo/compgen2021</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41107/machine-learning-in-perl</guid>
	<pubDate>Sun, 16 Feb 2020 15:32:03 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41107/machine-learning-in-perl</link>
	<title><![CDATA[Machine learning in Perl]]></title>
	<description><![CDATA[<p>this is a fourth blog post in the Machine learning in Perl series, focusing on the&nbsp;<a href="https://metacpan.org/pod/AI::MXNet">AI::MXNet</a>, a Perl interface to Apache MXNet, a modern and powerful machine learning library.</p>
<p>If you're interested in refreshing your memory or just new to the series, please check previous entries over here:&nbsp;<a href="http://blogs.perl.org/users/sergey_kolychev/2017/02/machine-learning-in-perl.html">1</a>&nbsp;<a href="http://blogs.perl.org/users/sergey_kolychev/2017/04/machine-learning-in-perl-part2-a-calculator-handwritten-digits-and-roboshakespeare.html">2</a>&nbsp;<a href="http://blogs.perl.org/users/sergey_kolychev/2017/10/machine-learning-in-perl-part3-deep-convolutional-generative-adversarial-network.html">3</a></p>
<p><a href="https://metacpan.org/pod/AI::MXNet">https://metacpan.org/pod/AI::MXNet</a></p><p>Address of the bookmark: <a href="http://blogs.perl.org/users/sergey_kolychev/2018/07/machine-learning-in-perl-kyuubi-goes-to-a-modelzoo-during-the-starry-night.html" rel="nofollow">http://blogs.perl.org/users/sergey_kolychev/2018/07/machine-learning-in-perl-kyuubi-goes-to-a-modelzoo-during-the-starry-night.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/43817/bioinfo-lab</guid>
  <pubDate>Fri, 04 Mar 2022 00:17:00 -0600</pubDate>
  <link></link>
  <title><![CDATA[Bioinfo Lab]]></title>
  <description><![CDATA[
<p>The Institute of Bioinformatics conducts internationally renowned research and provides profound education in bioinformatics. Its research focuses on development and application of machine learning and statistical methods in biology and medicine.</p>

<p>Contact:<br />Computer Science Building (Science Park 3)<br />Altenberger Str. 69, A-4040 Linz, Austria<br />Tel. +43 732 2468 4520 / Fax +43 732 2468 4539<br />E-mail secretary@bioinf.jku.at</p>

<p>http://www.bioinf.jku.at/</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36927/restrictiondigest-a-powerful-perl-module-for-simulating-genomic-restriction-digests</guid>
	<pubDate>Tue, 12 Jun 2018 13:17:13 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36927/restrictiondigest-a-powerful-perl-module-for-simulating-genomic-restriction-digests</link>
	<title><![CDATA[RestrictionDigest: A powerful Perl module for simulating genomic restriction digests]]></title>
	<description><![CDATA[RestrictionDigest can simulate the reference genome digestion and generate comprehensive information of the simulation. It can simulate single-enzyme digestion, double-enzyme digestion and size selection process. It can also analyze multiple genomes at one run and generates concise comparison of enzyme(s) performance across the genomes.

For more information, please see the academic paper published online (http://www.sciencedirect.com/science/article/pii/S071734581630001X).<p>Address of the bookmark: <a href="https://github.com/JINPENG-WANG/RestrictionDigest" rel="nofollow">https://github.com/JINPENG-WANG/RestrictionDigest</a></p>]]></description>
	<dc:creator>Neel</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>

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