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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/35078?offset=170</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34549/kraken-a-universal-genomic-coordinate-translator-for-comparative-genomics</guid>
	<pubDate>Thu, 07 Dec 2017 04:45:43 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34549/kraken-a-universal-genomic-coordinate-translator-for-comparative-genomics</link>
	<title><![CDATA[kraken: A universal genomic coordinate translator for comparative genomics]]></title>
	<description><![CDATA[<p><span>If you planning on conducting a study involving dozens of large genomes, then you do not have to run all pairwise synteny alignments .. simply try&nbsp;kraken: A universal genomic coordinate translator for comparative genomics</span></p><p>Address of the bookmark: <a href="https://github.com/nedaz/kraken" rel="nofollow">https://github.com/nedaz/kraken</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38006/scribl-html5-canvas-genomics-graphic-library</guid>
	<pubDate>Thu, 25 Oct 2018 09:38:53 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38006/scribl-html5-canvas-genomics-graphic-library</link>
	<title><![CDATA[Scribl : HTML5 canvas genomics graphic library]]></title>
	<description><![CDATA[<p>Scribl is a javascript, Canvas-based graphics library that easily generates biological visuals of genomic regions, alignments, and assembly data. Scribl can also be used in conventional offline pipelines, since everything needed to generate charts can be contained in a single html file.</p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="http://chmille4.github.io/Scribl/" rel="nofollow">http://chmille4.github.io/Scribl/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40369/phyloxml-xml-for-evolutionary-biology-and-comparative-genomics</guid>
	<pubDate>Sun, 08 Dec 2019 09:41:18 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40369/phyloxml-xml-for-evolutionary-biology-and-comparative-genomics</link>
	<title><![CDATA[phyloXML: XML for evolutionary biology and comparative genomics]]></title>
	<description><![CDATA[<p><a href="http://www.biomedcentral.com/1471-2105/10/356/">phyloXML</a><span>&nbsp;(</span><a href="http://www.phyloxml.org/examples_syntax/phyloxml_syntax_example_1.html">example</a><span>) is an&nbsp;</span><a href="http://en.wikipedia.org/wiki/XML">XML</a><span>&nbsp;language designed to describe phylogenetic trees (or networks) and associated data. PhyloXML provides elements for commonly used features, such as taxonomic information, gene names and identifiers, branch lengths, support values, and gene duplication and speciation events. Using these standardized elements allows interoperability between various applications and databases. Furthermore, both due to extensible nature of XML itself and the provision of &lt;property&gt; elements by phyloXML, extensibility as well as domain specific applications are ensured. The structure of phyloXML is described by&nbsp;</span><a href="http://en.wikipedia.org/wiki/XML_Schema_%28W3C%29">XML Schema Definition (XSD)</a><span>&nbsp;language.</span></p>
<p><a href="http://www.phyloxml.org/archaeopteryx-js/adh.html">http://www.phyloxml.org/archaeopteryx-js/adh.html</a></p><p>Address of the bookmark: <a href="http://www.phyloxml.org/" rel="nofollow">http://www.phyloxml.org/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/41394/ngsymposium-in-computational-biology</guid>
  <pubDate>Mon, 09 Mar 2020 06:00:30 -0500</pubDate>
  <link></link>
  <title><![CDATA[NGSymposium in Computational Biology]]></title>
  <description><![CDATA[
<p>We have a great pleasure to invite you to the NGSymposium in Computational Biology to celebrate the 5th anniversary of the NGSchool Summer Schools. This international conference will make way for exchanging knowledge and experiences between experienced and early-stage researchers as well as bioinformaticians. The meeting will be held on 31.07 - 1.08.2020 in Warsaw. It will be a satellite event to the #NGSchool2020: Statistical Learning in Genomics. It will cover a wide range of topics from basic and applied biomedical sciences: bioinformatics, genomics, transcriptomics, computational biology, Machine Learning.</p>

<p>Registration of active participants will be open from February, 27 12 PM CET to April 17, 23:59 CET. In registration forms you will be asked for providing us with some basic information about yourself. You will also be able to submit your abstract. You can save your registration form after filling it partially and come back later to supply more data e.g. upload an abstract. Your registration will be completed only with the payment of the registration fee reaching our accounts - please make sure to transfer the money in advance!</p>

<p>Registration of passive participants will be open after closing of registration of active participants.</p>

<p>Details an registration: https://ngschool.eu/conference/</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42570/breeding-insight</guid>
	<pubDate>Wed, 06 Jan 2021 19:49:21 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42570/breeding-insight</link>
	<title><![CDATA[Breeding Insight]]></title>
	<description><![CDATA[<p><span><span>Breeding Insight&nbsp;at Cornell University will leverage recent improvements in genomics and open source informatics components, and in&nbsp;partnership with small breeding programs, will enable these programs to harness&nbsp;&nbsp;powerful digital tools to accelerate their genetic gains</span></span></p>
<p><span>Breeding Insight is funded by&nbsp;the&nbsp;</span><span><a href="https://www.ars.usda.gov/about-ars/" target="_blank">U.S. Department of Agriculture (USDA) Agricultural Research Service (ARS)</a></span><span>&nbsp;through Cornell University. The USDA ARS delivers scientific solutions to national and global agricultural challenges. As a global leader&nbsp;in agricultural discovery through scientific excellence, ARS is committed to delivering cutting-edge, scientific tools and innovative solutions for American farmers, producers, industry, and communities to support the nourishment and well-being of all people; sustaining our nation&rsquo;s agroecosystems and natural resources; and ensuring the economic competitiveness and excellence of our agriculture.</span></p><p>Address of the bookmark: <a href="https://www.breedinginsight.org/" rel="nofollow">https://www.breedinginsight.org/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43683/genview-a-phylogeny-based-comparative-genomics-software-to-analyze-the-genetic-environment-of-genes</guid>
	<pubDate>Tue, 28 Dec 2021 01:49:03 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43683/genview-a-phylogeny-based-comparative-genomics-software-to-analyze-the-genetic-environment-of-genes</link>
	<title><![CDATA[GEnView: A phylogeny based comparative genomics software to analyze the genetic environment of genes]]></title>
	<description><![CDATA[<p><span>A phylogeny based comparative genomics software to analyze the genetic environment of genes. The user can select one or several taxa and provide one or several reference protein(s). Genomes and plasmids (based on user choice) will be downloaded from the NCBI Assembly/NR database and searched for the respective gene. Alternatively, custom genomes can be provided. User selected stretches (20kbp by default) of the genes genetic environment are extracted, annotated and aligned between all genomes. The sequences are then visualized, enabling comparison of synteny and gene content.</span></p>
<p><span>More at&nbsp;https://pubmed.ncbi.nlm.nih.gov/34951622/</span></p><p>Address of the bookmark: <a href="https://github.com/EbmeyerSt/GEnView" rel="nofollow">https://github.com/EbmeyerSt/GEnView</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44661/lovis4u-locus-visualisation-tool-for-comparative-genomics</guid>
	<pubDate>Tue, 17 Sep 2024 02:30:57 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44661/lovis4u-locus-visualisation-tool-for-comparative-genomics</link>
	<title><![CDATA[LoVis4u: Locus Visualisation tool for comparative genomics]]></title>
	<description><![CDATA[<p dir="auto"><a href="https://github.com/art-egorov/lovis4u/blob/main/docs/img/lovis4u_logo.png" target="_blank"><img src="https://github.com/art-egorov/lovis4u/raw/main/docs/img/lovis4u_logo.png" alt="image" width="300" style="border: 0px; border: 0px;"></a></p>
<div dir="auto">
<h2 dir="auto">Description</h2>
<a href="https://github.com/art-egorov/lovis4u#description"></a></div>
<p dir="auto"><span>LoVis4u</span>&nbsp;is a bioinformatics tool for&nbsp;<span>Lo</span>ci&nbsp;<span>Vis</span>ualisation.</p>
<p dir="auto"><span>LoVis4u, a command-line tool and Python API designed for highly customizable and fast visualisation of multiple genomic loci. LoVis4u generates vector images in PDF format based on annotation data from GenBank or GFF files. It is capable of visualising entire genomes of bacteriophages as well as plasmids and user-defined regions of longer prokaryotic genomes. Additionally, LoVis4u offers optional data processing steps to identify and highlight accessory and core genes in input sequences.</span></p>
<p dir="auto">https://art-egorov.github.io/lovis4u/</p>
<p dir="auto">&nbsp;</p><p>Address of the bookmark: <a href="https://github.com/art-egorov/lovis4u" rel="nofollow">https://github.com/art-egorov/lovis4u</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/view/119</guid>
	<pubDate>Wed, 10 Jul 2013 14:35:34 -0500</pubDate>
	<link>https://bioinformaticsonline.com/view/119</link>
	<title><![CDATA[Which are the best statistical programming languages to study for a bioinformatician?]]></title>
	<description><![CDATA[<p><span>In Bio-informatics based&nbsp;genome sequencing and predicting metabolic pathways&nbsp;research jobs&nbsp;I used Matlab, SAS, SPSS, R and several Bioconductor packages. Matlab had a lot of powerful tools and was easy to use, whereas SPSS is for non-programmers and R need programming skills. I am wondering what other people think is best? or there might not be one specific language but a few that lend themselves best to Bio-informatics work that is math heavy and deals with a large amount of data.</span></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/857/smyth-lab</guid>
  <pubDate>Sun, 14 Jul 2013 12:26:18 -0500</pubDate>
  <link></link>
  <title><![CDATA[Smyth Lab]]></title>
  <description><![CDATA[
<p>Statistical functional genomics in experimental medicine<br />The genome projects and the accelerated development of high-throughput genomic technologies such as microarrays have revolutionised biology. Making the most of this revolution requires the marriage of researchers from mathematical and biological backgrounds.</p>

<p>Research Area:<br />Linear models for microarray data<br />Digital gene expression technologies<br />Detection of molecular pathways<br />Bioinformatics resources for medical research</p>

<p>Link @ http://www.wehi.edu.au/faculty_members/professor_gordon_smyth/</p>
]]></description>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/914/welch-lab</guid>
  <pubDate>Mon, 15 Jul 2013 18:21:13 -0500</pubDate>
  <link></link>
  <title><![CDATA[Welch Lab]]></title>
  <description><![CDATA[
<p>They are based in the Department of Genetics at the University of Cambridge. </p>

<p>The research covers diverse areas of evolutionary biology, and molecular evolution in particular. It combines theoretical and empirical approaches, and particularly evolutionary inference from genome sequence data.</p>

<p>Links @ http://www.gen.cam.ac.uk/research/welch/GroupPage/Home.html</p>
]]></description>
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