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<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/44633?offset=20</link>
	<atom:link href="https://bioinformaticsonline.com/related/44633?offset=20" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36360/dendropy-a-python-library-for-phylogenetic-computing</guid>
	<pubDate>Mon, 23 Apr 2018 05:49:50 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36360/dendropy-a-python-library-for-phylogenetic-computing</link>
	<title><![CDATA[DendroPy: a Python library for phylogenetic computing]]></title>
	<description><![CDATA[<p>DendroPy is a Python library for phylogenetic computing. It provides classes and functions for the simulation, processing, and manipulation of phylogenetic trees and character matrices, and supports the reading and writing of phylogenetic data in a range of formats, such as NEXUS, NEWICK, NeXML, Phylip, FASTA, etc. Application scripts for performing some useful phylogenetic operations, such as data conversion and tree posterior distribution summarization, are also distributed and installed as part of the libary. DendroPy can thus function as a stand-alone library for phylogenetics, a component of more complex multi-library phyloinformatic pipelines, or as a scripting &ldquo;glue&rdquo; that assembles and drives such pipelines.</p>
<p>The primary home page for DendroPy, with detailed tutorials and documentation, is at:</p>
<blockquote><div><a href="http://dendropy.org/">http://dendropy.org/</a></div></blockquote>
<p>DendroPy is also hosted in the official Python repository:</p>
<blockquote><div><a href="http://packages.python.org/DendroPy/">http://packages.python.org/DendroPy/</a></div></blockquote>
<div id="requirements-and-installation">
<h2>Requirements and Installation</h2>
<p>DendroPy 4.x runs under Python 3 (all versions &gt; 3.1) and Python 2 (Python 2.7 only).</p>
<p>You can install DendroPy by running:</p>
<pre>&nbsp;</pre>
<p>More information is available here:</p>
<blockquote><div><a href="http://dendropy.org/downloading.html">http://dendropy.org/downloading.html</a></div></blockquote>
</div>
<div id="documentation">
<h2>Documentation</h2>
<p>Full documentation is available here:</p>
<blockquote><div><a href="http://dendropy.org/">http://dendropy.org/</a></div></blockquote>
<p>This includes:</p>
<blockquote>
<ul>
<li><a href="http://dendropy.org/primer/index.html">A comprehensive &ldquo;getting started&rdquo; primer</a>&nbsp;.</li>
<li><a href="http://dendropy.org/library/index.html">API documentation</a>&nbsp;.</li>
<li><a href="http://dendropy.org/schemas/index.html">Descriptions of data formats supported for reading/writing</a>&nbsp;.</li>
</ul>
</blockquote>
<p>and more.</p>
</div><p>Address of the bookmark: <a href="https://pypi.org/project/DendroPy/" rel="nofollow">https://pypi.org/project/DendroPy/</a></p>]]></description>
	<dc:creator>Seema Singh</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>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/41041/post-doc-computational-biology-bioinformatics-network-biology-data-science-ngs-mfd</guid>
  <pubDate>Sat, 15 Feb 2020 06:13:35 -0600</pubDate>
  <link></link>
  <title><![CDATA[Post Doc Computational Biology, Bioinformatics - Network Biology &amp; Data Science, NGS (m/f/d)]]></title>
  <description><![CDATA[
<p>https://www.jobvector.de/jobs-stellenangebote/biologie-life-sciences/forschung-entwicklung/post-doc-computational-biology-bioinformatics-network-biology-data-science-ngs-129867.html?suid=e522e9793b41817e52ac58d6963b94e2519920df</p>

<p>Requirements<br />Doctoral degree in Bioinformatics, Computational Biology, (Bio)physics/-mathematics, Biochemistry/Biology or similar with strong quantitative and numeric focus<br />Ability to numerically process complex and large data sets<br />Good programming skills (R/Bioconductor and/or Python preferred, Linux is a plus)<br />Experience in analyzing next-generation sequencing data sets using network biology<br />Scientific publication record in applied bioinformatics<br />Familiarity with single cell NGS analyses and other –omics techniques is a plus, but not essential</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43101/luigi-a-python-package-that-helps-you-build-complex-pipelines-of-batch-jobs</guid>
	<pubDate>Thu, 24 Jun 2021 05:43:31 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43101/luigi-a-python-package-that-helps-you-build-complex-pipelines-of-batch-jobs</link>
	<title><![CDATA[Luigi: a Python package that helps you build complex pipelines of batch jobs.]]></title>
	<description><![CDATA[<p>Luigi is a Python (3.6, 3.7, 3.8, 3.9 tested) package that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization, handling failures, command line integration, and much more.</p>
<p>Run <code>pip install luigi</code> to install the latest stable version from <a href="https://pypi.python.org/pypi/luigi">PyPI</a>. <a href="https://luigi.readthedocs.io/en/stable/">Documentation for the latest release</a> is hosted on readthedocs.</p>
<p>Run <code>pip install luigi[toml]</code> to install Luigi with <a href="https://luigi.readthedocs.io/en/stable/configuration.html">TOML-based configs</a> support.</p><p>Address of the bookmark: <a href="https://github.com/spotify/luigi" rel="nofollow">https://github.com/spotify/luigi</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/36502/creating-conda-environment-for-python27</guid>
	<pubDate>Mon, 07 May 2018 08:56:52 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/36502/creating-conda-environment-for-python27</link>
	<title><![CDATA[Creating conda environment for python2.7]]></title>
	<description><![CDATA[<p>TIP: By default, environments are installed into the&nbsp;<code><span>envs</span></code>&nbsp;directory in your conda directory. Run&nbsp;<code><span>conda</span>&nbsp;<span>create</span>&nbsp;<span>--help</span></code>&nbsp;for information on specifying a different path.</p><p>Use the Terminal or an Anaconda Prompt for the following steps.</p><ol>
<li>
<p>To create an environment:</p>
<div>
<div>
<pre><span></span><span>conda</span> <span>create</span> <span>--</span><span>name</span> <span>myenv</span>
</pre>
</div>
</div>
<p>NOTE: Replace&nbsp;<code><span>myenv</span></code>&nbsp;with the environment name.</p>
</li>
<li>
<p>When conda asks you to proceed, type&nbsp;<code><span>y</span></code>:</p>
<div>
<div>
<pre><span></span>proceed ([y]/n)?
</pre>
</div>
</div>
</li>
</ol><p>This creates the myenv environment in&nbsp;<code><span>/envs/</span></code>. This environment uses the same version of Python that you are currently using, because you did not specify a version.</p><p>To create an environment with a specific version of Python:</p><div><div><pre><span></span>conda create -n myenv <span>python</span><span>=</span><span>3</span>.4
</pre></div></div><p>To create an environment with a specific package:</p><div><div><pre><span></span>conda create -n myenv scipy
</pre></div></div><p>OR:</p><div><div><pre><span></span>conda create -n myenv python
conda install -n myenv scipy
</pre></div></div><p>To create an environment with a specific version of a package:</p><div><div><pre><span></span>conda create -n myenv <span>scipy</span><span>=</span><span>0</span>.15.0
</pre></div></div><p>OR:</p><div><div><pre><span></span>conda create -n myenv python
conda install -n myenv <span>scipy</span><span>=</span><span>0</span>.15.0
</pre></div></div><p>To create an environment with a specific version of Python and multiple packages:</p><div><div><pre><span></span>conda create -n myenv <span>python</span><span>=</span><span>3</span>.4 <span>scipy</span><span>=</span><span>0</span>.15.0 astroid babel
</pre></div></div><p>TIP: Install all the programs that you want in this environment at the same time. Installing 1 program at a time can lead to dependency conflicts.</p><p>To automatically install pip or another program every time a new environment is created, add the default programs to the&nbsp;<a href="https://conda.io/docs/user-guide/configuration/use-condarc.html#config-add-default-pkgs">create_default_packages</a>&nbsp;section of your&nbsp;<code><span>.condarc</span></code>&nbsp;configuration file. The default packages are installed every time you create a new environment. If you do not want the default packages installed in a particular environment, use the&nbsp;<code><span>--no-default-packages</span></code>&nbsp;flag:</p><div><div><pre><span></span>conda create --no-default-packages -n myenv python
</pre></div></div><p>TIP: You can add much more to the&nbsp;<code><span>conda</span>&nbsp;<span>create</span></code>&nbsp;command. For details, run&nbsp;<code><span>conda</span>&nbsp;<span>create</span>&nbsp;<span>--help</span></code>.</p><p>➜ redundans git:(master) ✗ conda create --name py27 python=2.7<br />Solving environment: done</p><p><br />==&gt; WARNING: A newer version of conda exists. &lt;==<br /> current version: 4.5.0<br /> latest version: 4.5.2</p><p>Please update conda by running</p><p>$ conda update -n base conda</p><p>&nbsp;</p><p>## Package Plan ##</p><p>environment location: /home/urbe/anaconda3/envs/py27</p><p>added / updated specs: <br /> - python=2.7</p><p><br />The following packages will be downloaded:</p><p>package | build<br /> ---------------------------|-----------------<br /> wheel-0.31.0 | py27_0 61 KB<br /> python-2.7.15 | h1571d57_0 12.1 MB<br /> certifi-2018.4.16 | py27_0 142 KB<br /> sqlite-3.23.1 | he433501_0 1.5 MB<br /> setuptools-39.1.0 | py27_0 582 KB<br /> openssl-1.0.2o | h20670df_0 3.4 MB<br /> pip-10.0.1 | py27_0 1.7 MB<br /> ca-certificates-2018.03.07 | 0 124 KB<br /> ------------------------------------------------------------<br /> Total: 19.6 MB</p><p>The following NEW packages will be INSTALLED:</p><p>ca-certificates: 2018.03.07-0 <br /> certifi: 2018.4.16-py27_0 <br /> libedit: 3.1-heed3624_0 <br /> libffi: 3.2.1-hd88cf55_4 <br /> libgcc-ng: 7.2.0-hdf63c60_3 <br /> libstdcxx-ng: 7.2.0-hdf63c60_3 <br /> ncurses: 6.0-h9df7e31_2 <br /> openssl: 1.0.2o-h20670df_0<br /> pip: 10.0.1-py27_0 <br /> python: 2.7.15-h1571d57_0<br /> readline: 7.0-ha6073c6_4 <br /> setuptools: 39.1.0-py27_0 <br /> sqlite: 3.23.1-he433501_0<br /> tk: 8.6.7-hc745277_3 <br /> wheel: 0.31.0-py27_0 <br /> zlib: 1.2.11-ha838bed_2</p><p>Proceed ([y]/n)? y</p><p><br />Downloading and Extracting Packages<br />wheel 0.31.0: #################################################################################################################################################################################################### | 100% <br />python 2.7.15: ################################################################################################################################################################################################### | 100% <br />certifi 2018.4.16: ############################################################################################################################################################################################### | 100% <br />sqlite 3.23.1: ################################################################################################################################################################################################### | 100% <br />setuptools 39.1.0: ############################################################################################################################################################################################### | 100% <br />openssl 1.0.2o: ################################################################################################################################################################################################## | 100% <br />pip 10.0.1: ###################################################################################################################################################################################################### | 100% <br />ca-certificates 2018.03.07: ###################################################################################################################################################################################### | 100% <br />Preparing transaction: done<br />Verifying transaction: done<br />Executing transaction: done<br />#<br /># To activate this environment, use:<br /># &gt; source activate py27<br />#<br /># To deactivate an active environment, use:<br /># &gt; source deactivate<br />#</p><p>➜ redundans git:(master) ✗ source activate py27</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41487/tinycov-standalone-command-line-utility-written-in-python-to-plot-coverage-from-a-bam-file</guid>
	<pubDate>Mon, 23 Mar 2020 06:22:08 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41487/tinycov-standalone-command-line-utility-written-in-python-to-plot-coverage-from-a-bam-file</link>
	<title><![CDATA[tinycov: standalone command line utility written in python to plot coverage from a BAM file]]></title>
	<description><![CDATA[<p>Tinycov is a small standalone command line utility written in python to plot the coverage of a BAM file quickly. This software was inspired by&nbsp;<a href="https://github.com/matted/genome_coverage_plotter">Matt Edwards' genome coverage plotter</a>.</p>
<p>To install the stable version:&nbsp;<code>pip3 install --user tinycov</code></p>
<p>To install the development version:</p>
<pre><code>git clone https://github.com/cmdoret/tinycov.git
cd tinycov
pip install .</code></pre><p>Address of the bookmark: <a href="https://github.com/cmdoret/tinycov" rel="nofollow">https://github.com/cmdoret/tinycov</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/14218/pimp-your-brain-bioinformatics</guid>
	<pubDate>Wed, 20 Aug 2014 22:09:21 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/14218/pimp-your-brain-bioinformatics</link>
	<title><![CDATA[Pimp your brain: Bioinformatics]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/KqelGy6Q8nE" frameborder="0" allowfullscreen></iframe>Jan Lisec from the Max Planck Institute of Molecular Plant Physiology explains, in this "pimp your brain" episode, what bioinformatics is and why bioinformatics is so important and indispensable for biological research.

In the video serial "Pimp your brain" scientists from the Max Planck Institute of Molecular Plant Physiology describe their research. More videos from the 'Pimp your brain' serial are available on www.youtube.com/playlist?list=PL-l9VItC9Gn2Ur2Xj6PTOAkjLUlVPbIOO

More videos are available on www.mpimp-golm.mpg.de]]></description>
	
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32076/ngs-teaching-material</guid>
	<pubDate>Wed, 05 Apr 2017 04:29:06 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32076/ngs-teaching-material</link>
	<title><![CDATA[NGS teaching material]]></title>
	<description><![CDATA[<p><span>High throughput sequencing (HTS) technologies are being applied to a wide range of important topics in biology. However, the analyses of non-model organisms, for which little previous sequence information is available, pose specific problems. This course addresses the specific strengths and weaknesses of alternative HTS technologies, the computational resources needed for HTS, and how to analyze non-model species using HTS. The course consists of a practical training module, HTS bioinformatics training, and lecturing/seminars of HTS approaches specifically targeting non-model organisms.</span></p><p>Address of the bookmark: <a href="http://marinetics.org/teaching/hts/Assembly.html" rel="nofollow">http://marinetics.org/teaching/hts/Assembly.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/42811/bioinformatics-in-africa-part-4-morocco</guid>
	<pubDate>Sat, 06 Feb 2021 13:31:24 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/42811/bioinformatics-in-africa-part-4-morocco</link>
	<title><![CDATA[Bioinformatics in Africa: Part 4 - Morocco]]></title>
	<description><![CDATA[<p>Bioinformatics, in the UFR in Artificial Intelligence and Bioinformatics, deals with the management, the analysis, the modelling and the visualization of biological databases. Since the size of the databases is often exponential, the traditional algorithms are not very effective when seeking for a good computational solution.</p><p>To take care of this issue, many ways are opened to the researchers&nbsp;to&nbsp;improve&nbsp;the&nbsp;quality&nbsp;of&nbsp;the&nbsp;algorithms:</p><p>1. Usage of new information processing methods like artificial neuronal networks, genetic algorithms,&nbsp;etc. 2. Usage&nbsp;of&nbsp;Data&nbsp;mining&nbsp;&nbsp;to&nbsp;explore&nbsp;biochemical&nbsp;databases,<br />3. Usage of Machine learning on the biological examples to solve, for example, the problem of classification&nbsp;in&nbsp;Bioinformatics.</p><p>UFR&nbsp;offers&nbsp;in&nbsp;addition&nbsp;a&nbsp;doctoral&nbsp;training&nbsp;in&nbsp;Computer&nbsp;Science&nbsp;and&nbsp;Bioinformatics.</p><p>Doctoral&nbsp;module&nbsp;which&nbsp;includes:&nbsp;a&nbsp;Dipl&ocirc;me&nbsp;des&nbsp;Etudes&nbsp;Sup&eacute;rieures&nbsp;Approfondies&nbsp;(DESA)&nbsp; of&nbsp;two&nbsp;years;&nbsp;and&nbsp;a&nbsp;doctorate&nbsp;studies&nbsp;program&nbsp;with&nbsp;a&nbsp;national&nbsp;Ph.D.&nbsp;certification. Three&nbsp;specializations&nbsp;constitute&nbsp;the&nbsp;teaching&nbsp;trunk&nbsp;of&nbsp;the&nbsp;ENSAT:&nbsp;Computer&nbsp;engineering,&nbsp;Telecom&nbsp; engineering,&nbsp;and&nbsp;electronic&nbsp;systems&nbsp;engineering.</p><p>Research&nbsp;Interest&nbsp;and&nbsp;Activities:</p><p>The&nbsp;following&nbsp;are&nbsp;the&nbsp;present&nbsp;areas&nbsp;of&nbsp;research&nbsp;interest:</p><p>1. Machine&nbsp;Learning&nbsp;and&nbsp;Profile&nbsp;Gene&nbsp;Expression&nbsp;of&nbsp;Cancer<br />2. Predicting&nbsp;Protein&nbsp;structure <br />3. Hidden&nbsp;Markov&nbsp;Models&nbsp;(HMMs)&nbsp;and&nbsp;multiple&nbsp;alignments <br />4. Transformational&nbsp;Grammar&nbsp;for&nbsp;sequence&nbsp;modelling <br />5. Physical&nbsp;Mapping:&nbsp;STSs <br />6. Evolutionary&nbsp;Computation&nbsp;applied&nbsp;to&nbsp;Genomic&nbsp;and&nbsp;Proteomic <br />7. Predicate&nbsp;Logic&nbsp;and&nbsp;Protein&nbsp;Structure</p><p>Web&nbsp;site&nbsp;and&nbsp;links:</p><p>http://www.ensat.ac.ma/udiab http://www.pasteur.fr/pasteur/international/annonce_coursBioinfoannonce06_casa.pdf</p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/43008/list-of-useful-machine-ai-learning-resources</guid>
	<pubDate>Tue, 30 Mar 2021 08:56:06 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/43008/list-of-useful-machine-ai-learning-resources</link>
	<title><![CDATA[List of useful machine / ai learning resources !]]></title>
	<description><![CDATA[<p>ML&nbsp;cheatsheet !</p><p>https://github.com/remicnrd/ml_cheatsheet</p><p>Visual AI / ML</p><p>https://setosa.io/ev/</p><p>Simple and efficient tools for predictive data analysis</p><p><span>https://scikit-learn.org/stable/</span></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>

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