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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/43112?offset=150</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26559/microscope</guid>
	<pubDate>Fri, 04 Mar 2016 05:26:31 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26559/microscope</link>
	<title><![CDATA[Microscope]]></title>
	<description><![CDATA[<p>Microscope Platform user documentation.</p>
<p>The MicroScope platform is available at this URL:</p>
<p><a href="https://www.genoscope.cns.fr/agc/microscope">https://www.genoscope.cns.fr/agc/microscope</a></p><p>Address of the bookmark: <a href="http://microscope.readthedocs.org/en/latest/index.html" rel="nofollow">http://microscope.readthedocs.org/en/latest/index.html</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27104/gatb-genome-analysis-toolbox-with-de-bruijn-graph</guid>
	<pubDate>Thu, 28 Apr 2016 11:16:51 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27104/gatb-genome-analysis-toolbox-with-de-bruijn-graph</link>
	<title><![CDATA[GATB : Genome Analysis Toolbox with de-Bruijn graph]]></title>
	<description><![CDATA[<p>The&nbsp;<strong><strong>Genome Analysis Toolbox with de-Bruijn graph</strong> (GATB)</strong> provides a set of <a href="https://gatb.inria.fr/gatb-global-architecture/">highly efficient algorithms to analyse NGS data sets</a>. These methods enable the analysis of data sets of any size on multi-core desktop computers, including very huge amount of reads data coming from any kind of organisms such as bacteria, plants, animals and even complex samples (<em>e.g.</em> metagenomes).</p>
<p>More at https://gatb.inria.fr/</p><p>Address of the bookmark: <a href="https://gatb.inria.fr/" rel="nofollow">https://gatb.inria.fr/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30336/finding-patterns-in-biological-sequences</guid>
	<pubDate>Thu, 22 Dec 2016 10:30:49 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30336/finding-patterns-in-biological-sequences</link>
	<title><![CDATA[Finding Patterns in Biological Sequences]]></title>
	<description><![CDATA[<p>In this report we provide an overview of known techniques for discovery of patterns of biological sequences (DNA and proteins). We also provide biological motivation, and methods of biological verification of such patterns. Finally we list publicly available tools and databases for pattern discovery. On-line supplement is available through http://genetics.uwaterloo.ca/&sim;tvinar/cs798g/motif.</p><p>Address of the bookmark: <a href="http://engr.case.edu/li_jing/papers/00798gpattern.pdf" rel="nofollow">http://engr.case.edu/li_jing/papers/00798gpattern.pdf</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33398/tiny-python36-notebook</guid>
	<pubDate>Sat, 03 Jun 2017 03:16:28 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33398/tiny-python36-notebook</link>
	<title><![CDATA[Tiny Python3.6 Notebook]]></title>
	<description><![CDATA[<p><span>This is not so much an instructional manual, but rather notes, tables, and examples for Python syntax. It was created by the author as an additional resource during training, meant to be distributed as a physical notebook. Participants (who favor the physical characteristics of dead tree material) could add their own notes, thoughts, and have a valuable reference of curated examples.</span></p><p>Address of the bookmark: <a href="https://github.com/mattharrison/Tiny-Python-3.6-Notebook/blob/master/python.rst" rel="nofollow">https://github.com/mattharrison/Tiny-Python-3.6-Notebook/blob/master/python.rst</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31574/biostats-class-tutorial</guid>
	<pubDate>Thu, 16 Mar 2017 01:50:50 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31574/biostats-class-tutorial</link>
	<title><![CDATA[BioStats class tutorial]]></title>
	<description><![CDATA[<p>Nice biostat turorial by&nbsp;<strong>Ingo Ruczinski</strong></p><p>Address of the bookmark: <a href="http://www.biostat.jhsph.edu/~iruczins/teaching/" rel="nofollow">http://www.biostat.jhsph.edu/~iruczins/teaching/</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40489/machine-learning-training-and-courses-in-bioinformatics</guid>
	<pubDate>Tue, 31 Dec 2019 19:33:07 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40489/machine-learning-training-and-courses-in-bioinformatics</link>
	<title><![CDATA[Machine learning training and courses in bioinformatics !]]></title>
	<description><![CDATA[<p>Machine learning techniques have been successful in analyzing biological data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. In this class, we will learn basics about probabilistic models and machine learning techniques. We will focus on probabilistic models (Markov models, Hidden Markov models, and Bayesian networks) for biological sequence analysis and systems biology. Other machine learning techniques, such as Naive bayes, neural networks and SVMs will only be covered briefly.</p>
<p>More at&nbsp;http://homes.sice.indiana.edu/yye/lab/teaching/spring2017-I529/</p>
<p>More tutorial at&nbsp;</p>
<p><a href="http://calla.rnet.missouri.edu/cheng_courses/mlbioinfo/mlbioinfo.htm">http://calla.rnet.missouri.edu/cheng_courses/mlbioinfo/mlbioinfo.htm</a></p>
<p><a href="http://www.raetschlab.org/lectures/MLBioinformatics">http://www.raetschlab.org/lectures/MLBioinformatics</a></p>
<p><a href="http://www.raetschlab.org/lectures/bertinoro08">http://www.raetschlab.org/lectures/bertinoro08</a></p>
<p>Book at&nbsp;</p>
<p><a href="https://personal.utdallas.edu/~pradiptaray/teaching/7_deep_learning_bioinfo.pdf">https://personal.utdallas.edu/~pradiptaray/teaching/7_deep_learning_bioinfo.pdf</a></p><p>Address of the bookmark: <a href="http://homes.sice.indiana.edu/yye/lab/teaching/spring2017-I529/" rel="nofollow">http://homes.sice.indiana.edu/yye/lab/teaching/spring2017-I529/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/13226/you-and-your-friend-have-similar-dna</guid>
	<pubDate>Sun, 27 Jul 2014 20:44:05 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/13226/you-and-your-friend-have-similar-dna</link>
	<title><![CDATA[You and your friend have similar DNA !!!]]></title>
	<description><![CDATA[<p>New research out of Massachusetts claims that people often choose friends that are similar to them in genetics and they are more accurate than you might suppose. A study published on PNAS&nbsp;http://www.pnas.org/content/111/Supplement_3/10796.full found that people are apt to pick friends who are genetically similar to themselves - so much so that friends tend to be as alike at the genetic level as a person's fourth cousin.</p><div style="text-align: center;"><img src="http://i.kinja-img.com/gawker-media/image/upload/s--CwLwHa43--/18fbmlokxcmqcjpg.jpg" alt="image" width="300" height="271" style="border: 0px; border: 0px;"></div><p>Scientists with a long-running Framingham Heart Study looked at 1,932 people (examination of about 1.5 million markers of genetic variations), comparing unrelated friends to unrelated strangers. They found that friends shared about 1% of their genes &mdash; a percentage much higher than those shared with strangers.This new findings made it clear that people have more DNA in common with those who are selected as friends than with strangers in the same population.&nbsp;</p><p>The genes that lined up the most were olfactory genes, which deal with smell. The ones that lined up the least were immune system genes. The researchers weren't sure why that happened :/. Olfactory genes might be a straightforward explanation: People who like the same smells tend to be drawn to similar environments, where they meet others with the same tendencies.</p><p>Reference:</p><p>http://www.pnas.org/content/111/Supplement_3/10796.full</p><p>Image : http://i.kinja-img.com</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/43607/classification-of-sars-cov2-variant</guid>
	<pubDate>Fri, 26 Nov 2021 12:53:12 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/43607/classification-of-sars-cov2-variant</link>
	<title><![CDATA[Classification of SARS-CoV2 Variant !]]></title>
	<description><![CDATA[<p>The scientists established some guidelines for determining whether a variant is a legitimate branch of an existing lineage:</p><p>The variant should be transmitted from its original location to another "geographically distinct population"&mdash;say, another country or a province of a large and populous country.<br />It should differ from its ancestor by at least one nucleotide.<br />At least 95% of its genetic code should have been sequenced at least five times from different samples.</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39383/geck-trio-based-comparative-benchmarking-of-variant-calls</guid>
	<pubDate>Sun, 19 May 2019 20:54:12 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39383/geck-trio-based-comparative-benchmarking-of-variant-calls</link>
	<title><![CDATA[geck: trio-based comparative benchmarking of variant calls]]></title>
	<description><![CDATA[<p><span>Determine the accuracy of our model by comparing the precision and recall of GATK Unified Genotyper and Haplotype Caller on the high-confidence SNPs of the NIST Ashkenazim trio and the two independent Platinum Genome trios. We show that our method is able to estimate&nbsp;</span><em>differential</em><span>&nbsp;precision and recall between the two pipelines with&nbsp;</span><span>10<span>&minus;3</span></span><span>uncertainty.</span></p><p>Address of the bookmark: <a href="https://github.com/sbg/geck" rel="nofollow">https://github.com/sbg/geck</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41916/truvari-structural-variant-comparison-tool-for-vcfs</guid>
	<pubDate>Tue, 30 Jun 2020 21:30:44 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41916/truvari-structural-variant-comparison-tool-for-vcfs</link>
	<title><![CDATA[truvari: Structural variant comparison tool for VCFs]]></title>
	<description><![CDATA[<p>Structural variant comparison tool for VCFs</p>
<p>Given benchmark and comparsion sets of SVs, calculate the recall, precision, and f-measure.</p>
<p><a href="https://github.com/spiralgenetics/www.spiralgenetics.com">Spiral Genetics</a></p>
<p><a href="https://docs.google.com/presentation/d/17mvC1XOpOm7khAbZwF3SgtG2Rl4M9Mro37yF2nN7GhE/edit">Motivation</a></p><p>Address of the bookmark: <a href="https://github.com/spiralgenetics/truvari" rel="nofollow">https://github.com/spiralgenetics/truvari</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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