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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/44783?offset=410</link>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/22403/ryan-e-mills-lab</guid>
  <pubDate>Tue, 26 May 2015 09:29:24 -0500</pubDate>
  <link></link>
  <title><![CDATA[Ryan E. Mills Lab]]></title>
  <description><![CDATA[
<p>Our research group is primarily focused on the analysis of whole genome sequence data to identify genetic variation (primarily structural variation) and examine their potential functional impact in disease phenotypes. We are particularly interested in analyzing complex regions of the genome that are not easily resolved through modern sequencing approaches and which may exhibit interesting mechanistic origins.</p>

<p>We are also interested in the large-scale integration of genomic, expression, methylation and proteomic data sets, as well as the application of whole genome sequence analysis in clinical diagnostics. </p>

<p>More at http://millslab.ccmb.med.umich.edu/index.html</p>
]]></description>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/26456/the-mills-lab</guid>
  <pubDate>Wed, 24 Feb 2016 16:18:38 -0600</pubDate>
  <link></link>
  <title><![CDATA[The Mills lab]]></title>
  <description><![CDATA[
<p>The laboratory is focused on the discovery and analysis of structural variation (SVs) from genomic sequence data. As part of the 1000 Genomes Project and other endeavors, we have helped produce initial fine-scale maps using a variety of SV discovery approaches including: (i) paired-end mapping (or read pair analysis) based on abnormally mapped pairs of clone ends; (ii) read-depth analysis, which detects deletions and duplications through analysis of the read depth-of-coverage; (iii) split read analysis, which detects SVs by evaluating gapped sequence alignments; and (iv) sequence assembly, which enables the discovery of novel (non-reference) sequence insertions.</p>

<p>http://millslab.org/research.html</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29574/beagle</guid>
	<pubDate>Thu, 27 Oct 2016 11:19:00 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29574/beagle</link>
	<title><![CDATA[Beagle]]></title>
	<description><![CDATA[<p>Beagle is a software package that performs genotype calling, genotype phasing, imputation of ungenotyped markers, and identity-by-descent segment detection.</p>
<p>Beagle version 4.1 has a more accurate genotype phasing algorithm and a very fast and accurate genotype imputation algorithm. Version 4.1 also has several changes to the command line arguments which are described in the&nbsp;<a href="http://faculty.washington.edu/browning/beagle/release_notes" target="_blank">release notes</a>. The "ped" argument has no effect in version 4.1. If your data contains nuclear families and you want to model the parent-offspring relationships when phasing genotypes, please use&nbsp;<a href="https://faculty.washington.edu/browning/beagle/b4_0.html">version 4.0</a>.</p>
<p>If you use Beagle 4.1 in a published analysis, please report the program version and cite the appropriate article.</p>
<p>The citation for Beagle's phasing algorithm is:</p>
<p>S R Browning and B L Browning (2007) Rapid and accurate haplotype phasing and missing data inference for whole genome association studies by use of localized haplotype clustering. Am J Hum Genet 81:1084-1097.<a href="http://dx.doi.org/doi:10.1086/521987" target="_blank">doi:10.1086/521987</a></p>
<p>The citation for Beagle's genotype imputation algorithm is:</p>
<p>B L Browning and S R Browning (2016). Genotype imputation with millions of reference samples. Am J Hum Genet 98:116-126.<a href="http://dx.doi.org/doi:10.1016/j.ajhg.2015.11.020" target="_blank">doi:10.1016/j.ajhg.2015.11.020</a></p>
<p>The citation for Beagle's IBD detection algorithm is:</p>
<p>B L Browning and S R Browning (2013). Improving the accuracy and efficiency of identity-by-descent detection in population data. Genetics 194(2):459-71.<a href="http://dx.doi.org/doi:10.1534/genetics.113.150029" target="_blank">doi:10.1534/genetics.113.150029</a></p><p>Address of the bookmark: <a href="http://faculty.washington.edu/browning/beagle/beagle.html" rel="nofollow">http://faculty.washington.edu/browning/beagle/beagle.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38581/cvit-chromosome-viewing-tool</guid>
	<pubDate>Wed, 02 Jan 2019 04:10:09 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38581/cvit-chromosome-viewing-tool</link>
	<title><![CDATA[CViT: Chromosome Viewing Tool]]></title>
	<description><![CDATA[<p><span>CViT - Chromosome Viewing Tool. A collection of Perl scripts that enable quick visualizations of features on linkage groups, psuedochromosomes or cytogenetic maps. Intended for whole-genome views of data but can be used to create images of single chromosomes/linkage groups, contigs, or BACs, or even proteins -- any feature that has a location on a backbone. Handles most standard genetic/genomic coordinate systems. Reads GFF3 data and produces a PNG or SVG image.</span></p>
<p><span>https://www.hindawi.com/journals/ijpg/2011/373875/</span></p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/cvit/" rel="nofollow">https://sourceforge.net/projects/cvit/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41485/chromosight-computer-vision-based-program-for-pattern-recognition-in-chromosome-hi-c-contact-maps</guid>
	<pubDate>Mon, 23 Mar 2020 06:20:04 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41485/chromosight-computer-vision-based-program-for-pattern-recognition-in-chromosome-hi-c-contact-maps</link>
	<title><![CDATA[chromosight: Computer vision based program for pattern recognition in chromosome (Hi-C) contact maps]]></title>
	<description><![CDATA[<p>Python package to detect chromatin loops (and other patterns) in Hi-C contact maps.</p>
<p>Stable version with pip:</p>
<div>
<pre>pip3 install --user chromosight</pre>
</div>
<p>Stable version with conda:</p>
<div>
<pre>conda install -c bioconda -c conda-forge chromosight</pre>
</div>
<p>or, if you want to get the latest development version:</p>
<pre><code>pip3 install --user -e git+https://github.com/koszullab/chromosight.git@master#egg=chromosight</code></pre><p>Address of the bookmark: <a href="https://github.com/koszullab/Chromosight" rel="nofollow">https://github.com/koszullab/Chromosight</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/4100/should-you-get-sequenced-not-all-bad-genes-predict-disease</guid>
	<pubDate>Thu, 29 Aug 2013 15:10:53 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/4100/should-you-get-sequenced-not-all-bad-genes-predict-disease</link>
	<title><![CDATA[Should you get sequenced? Not all bad genes predict disease]]></title>
	<description><![CDATA[<p><span>&ldquo;What we really don&rsquo;t know yet is whether the predictive aspects of the genome are going to turn out to be beneficial or potentially harmful&rdquo;</span></p>
<p><span><span>&ldquo;As we roll out genomic medicine we are fighting against this society-wide misconception that having the bad gene means you&rsquo;re going to get the disease. That&rsquo;s only true in a very few cases.&rdquo;</span></span></p>
<p><span><span><strong>Source</strong>:Today Health</span></span></p><p>Address of the bookmark: <a href="http://www.today.com/health/should-you-get-sequenced-not-all-bad-genes-predict-disease-8C11017154" rel="nofollow">http://www.today.com/health/should-you-get-sequenced-not-all-bad-genes-predict-disease-8C11017154</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/11311/stephen-friend-the-hunt-for-unexpected-genetic-heroes</guid>
	<pubDate>Sat, 31 May 2014 14:31:47 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/11311/stephen-friend-the-hunt-for-unexpected-genetic-heroes</link>
	<title><![CDATA[Stephen Friend: The hunt for "unexpected genetic heroes"]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/Yagdvqn2YMU" frameborder="0" allowfullscreen></iframe>What can we learn from people with the genetics to get sick — who don't? With most inherited diseases, only some family members will develop the disease, while others who carry the same genetic risks dodge it. Stephen Friend suggests we start studying those family members who stay healthy. Hear about the Resilience Project, a massive effort to collect genetic materials that may help decode inherited disorders.

TEDTalks is a daily video podcast of the best talks and performances from the TED Conference, where the world's leading thinkers and doers give the talk of their lives in 18 minutes (or less). Look for talks on Technology, Entertainment and Design -- plus science, business, global issues, the arts and much more.
Find closed captions and translated subtitles in many languages at http://www.ted.com/translate

Follow TED news on Twitter: http://www.twitter.com/tednews
Like TED on Facebook: https://www.facebook.com/TED

Subscribe to our channel: http://www.youtube.com/user/TEDtalksDirector]]></description>
	
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43643/corona-virus-literature</guid>
	<pubDate>Sun, 12 Dec 2021 23:30:56 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43643/corona-virus-literature</link>
	<title><![CDATA[Corona Virus Literature !]]></title>
	<description><![CDATA[<p><span><span>LitCovid</span>&nbsp;is a curated literature hub for tracking up-to-date scientific information about the 2019 novel Coronavirus.</span><span>&nbsp;It is the most comprehensive resource on the subject, providing a central access to&nbsp;</span><span>201482</span><span>&nbsp;(and&nbsp;</span><span>growing</span><span>) relevant articles in PubMed. The articles are updated daily and are further categorized by different research topics (e.g.&nbsp;</span><span>Long Covid</span><span>) and geographic locations for improved access. You can learn more at&nbsp;</span><a href="https://www.nature.com/articles/d41586-020-00694-1" target="_blank">Chen et al. Nature</a><span>&nbsp;(2020) or our&nbsp;</span><span>FAQ</span><span>, and download our data&nbsp;</span><a href="https://www.ncbi.nlm.nih.gov/research/coronavirus/#data-download">here</a><span>.</span></p>
<p>https://www.ncbi.nlm.nih.gov/research/coronavirus/</p><p>Address of the bookmark: <a href="https://www.ncbi.nlm.nih.gov/research/coronavirus/" rel="nofollow">https://www.ncbi.nlm.nih.gov/research/coronavirus/</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/2882/phylogenetics</guid>
	<pubDate>Fri, 23 Aug 2013 03:53:43 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/2882/phylogenetics</link>
	<title><![CDATA[Phylogenetics]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/fQwI90bkJl4" frameborder="0" allowfullscreen></iframe>006 - Phylogenetics

Paul Andersen discusses the specifics of phylogenetics.  The evolutionary relationships of organisms are discovered through both morphological and molecular data.  A specific type of phylogenetic tree, the cladogram, is also covered.

Intro Music Atribution
Title: I4dsong_loop_main.wav
Artist: CosmicD
Link to sound: http://www.freesound.org/people/CosmicD/sounds/72556/
Creative Commons Atribution License]]></description>
	
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/5254/mike-ritchie-lab</guid>
  <pubDate>Wed, 02 Oct 2013 15:25:45 -0500</pubDate>
  <link></link>
  <title><![CDATA[Mike Ritchie Lab]]></title>
  <description><![CDATA[
<p>Mike Ritchie Lab primary research focus is the detection of susceptibility genes for common diseases such as cancer, diabetes, hypertension, and cardiovascular disease, among others. The approaches will involve the development and application of new statistical methods with a focus on the detection of gene-gene interactions associated with human disease.</p>

<p>Gene expression and protein expression patterns between normal and non-normal tissues is a growing area of research that may lead to the identification of candidate genes for understanding the etiology of common, complex diseases. </p>

<p>Lab homepage @ http://ritchielab.psu.edu/ritchielab/</p>
]]></description>
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