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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/4357?</link>
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	<description><![CDATA[]]></description>
	
	
<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/4212/eivind-hovigs-lab</guid>
  <pubDate>Tue, 03 Sep 2013 19:06:29 -0500</pubDate>
  <link></link>
  <title><![CDATA[Eivind Hovig's Lab]]></title>
  <description><![CDATA[
<p>Bioinformatics relevant research topics are:</p>

<p>genomic scale studies<br />endogenous mechanisms of mutations, germ line and somatic <br />computational aspects of immunology in cancer <br />signalling networks<br />three-dimensional organization of information in the nucleus<br />gene silencing<br />metastatic cross-talk<br />kinase signaling<br />personalized medicine<br />detection of biomarkers in cancer <br />historical DNA variation</p>

<p>From : http://www.ous-research.no/hovig/</p>

<p>Group address:<br />Eivind Hovig, The Norwegian Radium Hospital, Montebello, 0310 Oslo,Norway<br />Email: ehovig@radium.uio.no</p>
]]></description>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/4415/a-tour-of-the-cell</guid>
	<pubDate>Tue, 10 Sep 2013 12:01:05 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/4415/a-tour-of-the-cell</link>
	<title><![CDATA[A Tour of the Cell]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/1Z9pqST72is" frameborder="0" allowfullscreen></iframe>Paul Andersen takes you on a tour of the cell.  He starts by explaining the difference between prokaryotic and eukaryotic cells.  He also explains why cells are small but not infinitely small.  He also explains how the organelles work together in a similar fashion.

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/bookmarks/view/32379/enrichr-a-comprehensive-gene-set-enrichment-analysis</guid>
	<pubDate>Thu, 27 Apr 2017 05:42:09 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32379/enrichr-a-comprehensive-gene-set-enrichment-analysis</link>
	<title><![CDATA[Enrichr: a comprehensive gene set enrichment analysis]]></title>
	<description><![CDATA[<p><span>Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at:&nbsp;</span><a href="http://amp.pharm.mssm.edu/Enrichr" target="">http://amp.pharm.mssm.edu/Enrichr</a><span>.</span></p>
<p>https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkw377</p><p>Address of the bookmark: <a href="http://amp.pharm.mssm.edu/Enrichr/" rel="nofollow">http://amp.pharm.mssm.edu/Enrichr/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/4591/the-breitbart-lab</guid>
  <pubDate>Tue, 17 Sep 2013 18:19:49 -0500</pubDate>
  <link></link>
  <title><![CDATA[The Breitbart lab]]></title>
  <description><![CDATA[
<p>Breitbart’s lab has created a new branch of biology called metagenomics in which one can sample and sequence genetic material collected from the environment.</p>

<p>Breitbart lab is located in the College of Marine Science at the University of South Florida. She is chosen as top "10 Brilliant" scientist by Popular Science magazine.<br />http://www.popsci.com/science/article/2013-09/mya-breitbart</p>

<p>Lab Link:<br />https://sites.google.com/site/breitbartgenomicslab/<br />http://www.marine.usf.edu/faculty/mya-breitbart.shtml</p>
]]></description>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/10116/economical-opportunity-transfere</guid>
  <pubDate>Mon, 21 Apr 2014 07:25:44 -0500</pubDate>
  <link></link>
  <title><![CDATA[Economical Opportunity transfere]]></title>
  <description><![CDATA[
<p>I´m PhD in biomedical sciencie, and CEO of biomedical research (Research Centers in Nutrition and Health, from Spain), focus in genomic, specialy in nutrigenomic and nutrigenetics, epigentic and metagenomic. My research groups (4 in my company, and 1 in the Complutense´s university  of Madrid) have resarch lines in chronic disseases as diabetes, degenerative as alzheimer, Multiple Esclerosis, cancer, obesity, ...</p>

<p>My offerd is about testing biomarkers and bioinformatic tools in patients, in pilot´s trials, to check the algoritms, the expresion, the response of tratament, or check if the "theory" is real, in specific paitiens, or population (we know that in normal publications networks have to look for validation in all popullation and ethnias, etc).</p>

<p>For USA and European Comision (publics projects), or pharma, testing the bioinformatic algorims, tools, etc and trasferd fo the comunity, health care,  etc is real important and is the way to get money and budget. So, i offerd create consorcius (bioinbformatics, pharmas, CINUSA Group) to check and testing in differents patieents).</p>

<p>For more information just write me to research@grupocinusa.com </p>

<p>Kind Regards.</p>

<p>Dr. Ismael San Mauro <br />CEO &amp; Resarch Manager<br />Rresearch Centers in Nutrition and Health (Spain)<br />Prof. Departament of Medicine (Complutense´s University of Madrid)</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/4763/inner-life-of-a-cell-full-versionmkv</guid>
	<pubDate>Mon, 23 Sep 2013 18:09:41 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/4763/inner-life-of-a-cell-full-versionmkv</link>
	<title><![CDATA[Inner Life Of A Cell - Full Version.mkv]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/yKW4F0Nu-UY" frameborder="0" allowfullscreen></iframe>Работа аппарата Гольджи и ЭПС при дифференцировке лейкоцита]]></description>
	
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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>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/5621/genome2014</guid>
  <pubDate>Tue, 15 Oct 2013 12:47:32 -0500</pubDate>
  <link></link>
  <title><![CDATA[Genome2014]]></title>
  <description><![CDATA[
<p>Genomics has profoundly changed our way of conducting research in microbiology. The power of high–throughput DNA sequencing technologies, in particular the recent development of next generation sequencing allows researchers now to address an increasingly diverse range of biological problems. The scale and efficiency of sequence-based analyses that can now be achieved is providing unprecedented progress in diverse areas that range from the analyses of genomes to related disciplines such as transcriptional profiling - or protein - nucleic acid interaction studies: Population and metagenomics studies can now be conducted in an unprecedented large scale, regulatory processes can be studied genome-wide under hundreds of different conditions. The genome wide study of the interaction of DNA or RNA with proteins brings completely new insight into regulatory processes and even single cell analyses become now possible. The many diverse applications of next–generation sequencing and the importance of the insights that are being gained through these methods are very exiting and challenging. It is the perfect time to come together and exchange new knowledge and technologies in this area.<br /> <br />Thus the conference on "Microbiology after the genomics revolution - Genomes 2014" will be an appropriate and timely occasion to offer an outstanding discussion forum for the best international researchers in all fields of cutting edge microbiology research to discuss newly discovered aspects of microbiology.</p>

<p>More @ http://www.genomes-2014.org/</p>
]]></description>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/22891/17-marie-curie-phd-position-available-immediately</guid>
  <pubDate>Tue, 23 Jun 2015 06:52:06 -0500</pubDate>
  <link></link>
  <title><![CDATA[17 Marie Curie PhD position available immediately]]></title>
  <description><![CDATA[
<p>Kindly look into following webpage:<br />http://medhealth.leeds.ac.uk/info/1450/scholarships/1795/marie_curie_phd_training_network</p>

<p>The closing date for application will be 26 June 2015.</p>
]]></description>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/4764/bbc-secret-universe-the-hidden-life-of-the-cell</guid>
	<pubDate>Mon, 23 Sep 2013 18:19:54 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/4764/bbc-secret-universe-the-hidden-life-of-the-cell</link>
	<title><![CDATA[BBC Secret Universe: The Hidden Life of the Cell]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/4GZXRMG5i_w" frameborder="0" allowfullscreen></iframe>This will help you to understand how a cell works

(C) BBC MMXII]]></description>
	
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