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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/40834?offset=60</link>
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  <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/opportunity/view/43227/project-associate-i-project-associate-ii-senior-project-associate-igib</guid>
  <pubDate>Thu, 05 Aug 2021 16:11:32 -0500</pubDate>
  <link></link>
  <title><![CDATA[Project Associate-I | Project Associate-II | Senior Project Associate @ IGIB]]></title>
  <description><![CDATA[
<p>Experience in Next Generation Sequencing (NGS) application and interest in Genomics/ Clinical / Translational Applications. OR Good computational programming skills and deep interest in working on interface of Genomics and Clinical application. </p>

<p>Project Scientist-I <br />Experimental / Computation analysis experience in highthroughput genomics/ clinical application.</p>

<p>Project Manager <br />Experience in handling large biological projects involving high-throughput genomics/ clinical application.</p>

<p>Scientific Administrative Assistant <br />Lab Work. </p>

<p>More at https://vinodscaria.genomes.in/positionsopen</p>
]]></description>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/44400/pevzner-lab</guid>
  <pubDate>Thu, 02 Nov 2023 05:39:26 -0500</pubDate>
  <link></link>
  <title><![CDATA[Pevzner Lab !]]></title>
  <description><![CDATA[
<p>The laboratory works on genome sequencing, immunoproteogenomics, antibiotics sequencing, and comparative genomics - computational technologies that enabled new applications and allowed scientists to attack biological problems that remained beyond the reach of previous techniques.</p>

<p>https://bioalgorithms.ucsd.edu/research4.html</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/140/python-education-material</guid>
	<pubDate>Wed, 10 Jul 2013 16:38:23 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/140/python-education-material</link>
	<title><![CDATA[Python Education Material]]></title>
	<description><![CDATA[<p><span>Google provide free class for people with a little bit of programming experience who want to learn Python. The google python class includes written materials, lecture videos, and lots of code exercises to practice Python coding. These materials are used within Google to introduce Python to people who have just a little programming experience.</span></p><p><span>Find more @&nbsp;<a href="https://developers.google.com/edu/python/">https://developers.google.com/edu/python/</a></span></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/12963/cosmos-our-workflow-management-system-for-ngs-data</guid>
	<pubDate>Wed, 23 Jul 2014 07:29:14 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/12963/cosmos-our-workflow-management-system-for-ngs-data</link>
	<title><![CDATA[COSMOS, our workflow management system for NGS data]]></title>
	<description><![CDATA[<p><strong>COSMOS</strong>, our Python-based management system for implementing large-scale parallel workflows focusing on, but not restricted to, large-scale short-read "NGS" sequencing data is open-access published via <a href="http://bioinformatics.oxfordjournals.org/content/early/2014/06/29/bioinformatics.btu385.abstract">Advance Access</a> in <em>Bioinformatics</em> (<a href="http://scholar.harvard.edu/lancaster/publications/cosmos-python-library-massively-parallel-workflows">Gafni et al. 2014</a>).&nbsp; It is also available for download for non-commercial academic and research purposes at:</p>
<p><strong>&nbsp;<a href="http://cosmos.hms.harvard.edu/">http://cosmos.hms.harvard.edu/</a></strong>.</p><p>Address of the bookmark: <a href="https://cosmos.hms.harvard.edu/" rel="nofollow">https://cosmos.hms.harvard.edu/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/20471/bioinformatics-scripts</guid>
	<pubDate>Thu, 22 Jan 2015 22:29:39 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/20471/bioinformatics-scripts</link>
	<title><![CDATA[Bioinformatics Scripts]]></title>
	<description><![CDATA[<p>Some of the useful bioinformatics scripts.</p>
<p>For example ... contig-stats.pl is a Perl script that will automatically describe features of a sequence assembly.</p>
<p>http://milkweedgenome.org/?q=scripts</p><p>Address of the bookmark: <a href="http://milkweedgenome.org/?q=scripts" rel="nofollow">http://milkweedgenome.org/?q=scripts</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/pages/view/36603/learning-python-programming-a-bioinformatician-perspective</guid>
	<pubDate>Mon, 14 May 2018 16:33:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/36603/learning-python-programming-a-bioinformatician-perspective</link>
	<title><![CDATA[Learning Python Programming - a bioinformatician perspective !]]></title>
	<description><![CDATA[<p>Python Programming&nbsp;is a general purpose programming language that is open source, flexible, powerful and easy to use. One of the most important features of python is its rich set of utilities and libraries for data processing and analytics tasks. In the current era of big biological data, python and biopython is getting more popularity due to its easy-to-use features which supports big data processing.</p><p>In this tutorial series article, I will explore features and packages of python which are widely used in the big data, NGS, and bioinformatics. I will also walk through a real biological example which shows NGS data processing with the help of python packages and programming.</p><p>Python has a couple of points to recommend it to biologists and scientists specifically:</p><ul>
<li>It's widely used in the scientific community</li>
<li>It has a couple of very well designed libraries for doing complex scientific computing (although we won't encounter them in this book)</li>
<li>It lend itself well to being integrated with other, existing tools</li>
<li>It has features which make it easy to manipulate strings of characters (for example, strings of DNA bases and protein amino acid residues, which we as biologists are particularly fond of)</li>
</ul><p>In general, following are some of the important features of python which makes it a perfect fit for rapid application development.</p><ul>
<li>Python is interpreted language so the program does not need to be compiled. Interpreter parses the program code and generates the output.</li>
<li>Python is dynamically typed, so the variables types are defined automatically.</li>
<li>Python is strongly typed. So the developers need to cast the type manually.</li>
<li>Less code and more use makes it more acceptable.</li>
<li>Python is portable, extendable and scalable.</li>
</ul><p>There are two major Python versions, Python 2 and Python 3. Python 2 and 3 are quite different. This tutorial uses Python 3, because it more semantically correct and supports newer features.</p><p>I will post tutorial on daily basis on this page. Check the sub-pages on right side.</p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40214/gooey-turn-almost-any-python-command-line-program-into-a-full-gui-application-with-one-line</guid>
	<pubDate>Fri, 01 Nov 2019 00:29:27 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40214/gooey-turn-almost-any-python-command-line-program-into-a-full-gui-application-with-one-line</link>
	<title><![CDATA[Gooey: Turn (almost) any Python command line program into a full GUI application with one line]]></title>
	<description><![CDATA[<p><span>Turn (almost) any Python command line program into a full GUI application with one line</span></p>
<p>The easiest way to install Gooey is via&nbsp;<code>pip</code></p>
<pre><code>pip install Gooey 
</code></pre>
<p>Alternatively, you can install Gooey by cloning the project to your local directory</p>
<pre><code>git clone https://github.com/chriskiehl/Gooey.git
</code></pre>
<p>run&nbsp;<code>setup.py</code></p>
<pre><code>python setup.py install</code></pre><p>Address of the bookmark: <a href="https://github.com/chriskiehl/Gooey" rel="nofollow">https://github.com/chriskiehl/Gooey</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>

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