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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/4763?</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/5812/animated-3d-cells-in-the-body</guid>
	<pubDate>Mon, 21 Oct 2013 06:28:18 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/5812/animated-3d-cells-in-the-body</link>
	<title><![CDATA[animated 3d cells in the body]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/UVtRGNElnkk" frameborder="0" allowfullscreen></iframe><p>cutting edge medical animation of cells</p>]]></description>
	
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/5731/influenza-animation-flu-virus-mechanism</guid>
	<pubDate>Thu, 17 Oct 2013 19:43:13 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/5731/influenza-animation-flu-virus-mechanism</link>
	<title><![CDATA[Influenza animation - flu virus mechanism]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/YSgkoldBNkI" frameborder="0" allowfullscreen></iframe>Animation of the mechanism of an influenza virus and how Crucell's antibodies target the HA1 proteins on the virus and prevent further spread of influenza. 

Client: Crucell
Direction, Design & Animation: Daniel Lim, 2Preform
Music & Sound Design: Javier Barrero, Logical Disorder
Production Company: David Hager, All Terrain Media]]></description>
	
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/6665/fertilization-conception</guid>
	<pubDate>Fri, 22 Nov 2013 03:00:35 -0600</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/6665/fertilization-conception</link>
	<title><![CDATA[Fertilization (Conception)]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/BFrVmDgh4v4" frameborder="0" allowfullscreen></iframe>View more AMAZING medical animations at http://www.nucleuslibrary.com

To download FREE medical animations of pregnancy and birth, visit http://www.prenateperl.com/yt2012  This 3D medical animation shows human fertilization, also known as conception.  Fertilization is the epic story of a single sperm facing incredible odds to unite with an egg and form a new human life. The sperm's journey is visualized with rich detail and narrative to convey a fresh understanding of a classic physiological tale.

This medical animation portrays the process of human fertilization. Shown at a cellular level magnification, sperm struggle through many obstacles in the female reproductive tract to reach the egg. Visualized at the molecular level, genetic material from the egg and a single sperm combines to form a new human being.]]></description>
	
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/5580/pharmacogenomics-at-mayo-clinic</guid>
	<pubDate>Mon, 14 Oct 2013 16:21:45 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/5580/pharmacogenomics-at-mayo-clinic</link>
	<title><![CDATA[Pharmacogenomics at Mayo Clinic]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/fGjG_9EEeeA" frameborder="0" allowfullscreen></iframe>The right drug, at the right dose, for the right patient. Mayo Clinic uses the latest technologies to understand how drugs will work in individual patients, maximizing drug efficacy and minimizing the potential for side effects.]]></description>
	
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/5934/retrovirus-replication-3d-animation</guid>
	<pubDate>Sat, 26 Oct 2013 09:07:25 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/5934/retrovirus-replication-3d-animation</link>
	<title><![CDATA[Retrovirus Replication 3D Animation]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/HhhRQ4t95OI" frameborder="0" allowfullscreen></iframe>The example used is the HIV Lentivirus. This video does a great job describing "complex" retrovirus transcription in a visually appealing way that is sufficient in detail for upper level coursework and possibly graduate coursework.
 * Modes of action for some anti-viral drugs are also described.

NOTE:  The viral genome in the form of DNA stays in the cell's chromosome! This is the predominant reason for the persistence of retroviral infections.]]></description>
	
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/13878/janet-iwasa-how-animations-can-help-scientists-test-a-hypothesis</guid>
	<pubDate>Sun, 10 Aug 2014 15:26:04 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/13878/janet-iwasa-how-animations-can-help-scientists-test-a-hypothesis</link>
	<title><![CDATA[Janet Iwasa: How animations can help scientists test a hypothesis]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/YvyeI-Axb70" frameborder="0" allowfullscreen></iframe>3D animation can bring scientific hypotheses to life. Molecular biologist (and TED Fellow) Janet Iwasa introduces a new open-source animation software designed just for scientists.

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>
	
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/4357/flow-cytometry-data-analysis</guid>
	<pubDate>Sat, 07 Sep 2013 18:09:48 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/4357/flow-cytometry-data-analysis</link>
	<title><![CDATA[Flow cytometry data analysis]]></title>
	<description><![CDATA[<p>Flow cytometry&nbsp;is a&nbsp;biophysical laser based instrument used in various purposes like&nbsp;cell counting,&nbsp;cell sorting,&nbsp;biomarker&nbsp;detection, etc.</p>
<p>More on this:</p>
<p><a href="http://bioinformatics.ca/workshops/2013/flow-cytometry-data-analysis-using-r-2013">http://bioinformatics.ca/workshops/2013/flow-cytometry-data-analysis-using-r-2013</a></p>
<p><a href="http://pyvideo.org/video/1234/fcm-a-python-library-for-flow-cytometry">http://pyvideo.org/video/1234/fcm-a-python-library-for-flow-cytometry</a></p><p>Address of the bookmark: <a href="http://www.ehu.es/biologiacomputacional/reprints/nmeth.2365.pdf" rel="nofollow">http://www.ehu.es/biologiacomputacional/reprints/nmeth.2365.pdf</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/34463/single-cell-rnaseq-data-analysis-tutorial</guid>
	<pubDate>Mon, 27 Nov 2017 16:24:29 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/34463/single-cell-rnaseq-data-analysis-tutorial</link>
	<title><![CDATA[Single Cell RNAseq data analysis tutorial !!]]></title>
	<description><![CDATA[<ul>
<li>A major breakthrough (replaced microarrays) in the late 00&rsquo;s and has been widely used since</li>
<li>Measures the&nbsp;average expression level&nbsp;for each gene across a large population of input cells</li>
<li>Useful for comparative transcriptomics, e.g.&nbsp;samples of the same tissue from different species</li>
<li>Useful for quantifying expression signatures from ensembles, e.g.&nbsp;in disease studies</li>
<li>Insufficient&nbsp;for studying heterogeneous systems, e.g.&nbsp;early development studies, complex tissues (brain)</li>
<li>Does&nbsp;not&nbsp;provide insights into the stochastic nature of gene expression</li>
</ul><p>Following are the useful links:</p><p><a href="http://hemberg-lab.github.io/scRNA.seq.course/scRNA-seq-course.pdf" target="_blank">Single Cell RNAseq data analysis Tutorial</a></p><p><a href="https://f1000research.com/articles/5-2122/v2" target="_blank">A step-by-step workflow for low-level analysis of single-cell RNA-seq data</a></p><p><a href="https://www.bioconductor.org/help/workflows/simpleSingleCell/" target="_blank">A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor</a></p><p>SCell: single-cell RNA-seq analysis software</p><p><a href="https://github.com/diazlab/SCell">https://github.com/diazlab/SCell</a></p><p>Beta-Poisson model for single-cell RNA-seq data analyses</p><p><a href="https://github.com/nghiavtr/BPSC">https://github.com/nghiavtr/BPSC</a></p><p>Sincera: A Computational Pipeline for Single Cell RNA-Seq Profiling Analysis</p><p><a href="https://research.cchmc.org/pbge/sincera.html">https://research.cchmc.org/pbge/sincera.html</a></p><p>SC3 &ndash; consensus clustering of single-cell RNA-Seq data</p><p><a href="http://biorxiv.org/content/early/2016/09/02/036558">http://biorxiv.org/content/early/2016/09/02/036558</a></p><p>Citrus: A toolkit for single cell sequencing analysis</p><p><a href="http://biorxiv.org/content/early/2016/09/14/045070">http://biorxiv.org/content/early/2016/09/14/045070</a></p><p>Single-Cell Resolution of Temporal Gene Expression during Heart Development</p><p><a href="http://www.cell.com/developmental-cell/fulltext/S1534-5807%2816%2930682-7">http://www.cell.com/developmental-cell/fulltext/S1534-5807(16)30682-7</a></p><p>Scalable latent-factor models applied to single-cell RNA-seq data separate biological drivers from confounding effects</p><p><a href="http://biorxiv.org/content/early/2016/11/15/087775">http://biorxiv.org/content/early/2016/11/15/087775</a></p><p>Single cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes</p><p><a href="http://genome.cshlp.org/content/early/2016/11/18/gr.212720.116.abstract">http://genome.cshlp.org/content/early/2016/11/18/gr.212720.116.abstract</a></p><p>SCODE: An efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation</p><p><a href="http://biorxiv.org/content/early/2016/11/21/088856">http://biorxiv.org/content/early/2016/11/21/088856</a></p><p>SCOUP is a probabilistic model to analyze single-cell expression data during differentiation</p><p><a href="https://github.com/hmatsu1226/SCOUP">https://github.com/hmatsu1226/SCOUP</a></p><p>scLVM is a modelling framework for single-cell RNA-seq data</p><p><a href="https://github.com/PMBio/scLVM">https://github.com/PMBio/scLVM</a></p><p>Selective Locally linear Inference of Cellular Expression Relationships (SLICER) algorithm for inferring cell trajectories</p><p><a href="https://github.com/jw156605/SLICER">https://github.com/jw156605/SLICER</a></p><p>SinQC: A Method and Tool to Control Single-cell RNA-seq Data Quality</p><p><a href="http://www.morgridge.net/SinQC.html">http://www.morgridge.net/SinQC.html</a></p><p>TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis</p><p><a href="https://github.com/zji90/TSCAN">https://github.com/zji90/TSCAN</a></p><p>Visualization and cellular hierarchy inference of single-cell data using SPADE</p><p><a href="http://www.nature.com/nprot/journal/v11/n7/full/nprot.2016.066.html">http://www.nature.com/nprot/journal/v11/n7/full/nprot.2016.066.html</a></p><p>OEFinder: Identify ordering effect genes in single cell RNA-seq data</p><p><a href="https://github.com/lengning/OEFinder">https://github.com/lengning/OEFinder</a></p>]]></description>
	<dc:creator>Robert M Willioms</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/4633/cancer-growth-animation</guid>
	<pubDate>Fri, 20 Sep 2013 06:16:51 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/4633/cancer-growth-animation</link>
	<title><![CDATA[Cancer Growth Animation]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/WXTsxPPcTEs" frameborder="0" allowfullscreen></iframe>This video demonstrates how cancer growth happens in human body.]]></description>
	
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/5581/microbiome-making-better-use-of-bacteria</guid>
	<pubDate>Mon, 14 Oct 2013 16:22:42 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/5581/microbiome-making-better-use-of-bacteria</link>
	<title><![CDATA[Microbiome - Making better use of bacteria]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/A-IqdPch9t0" frameborder="0" allowfullscreen></iframe>Bacterial cells outnumber human cells 10-to-1 in the average person. Bacterial genes outnumber human genes 100-to-1. Mayo Clinic and the Center for Individualized Medicine are working to understand these diverse populations and design better diagnoses and therapies that can be individualized to the patient. Diseases, such as clostridium difficile, celiac disease, and gluten sensitivities, are being studied. We're also gaining more insight in the many connections between digestive bacteria and autoimmune disorders, like diabetes, rheumatoid arthritis and multiple sclerosis.]]></description>
	
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