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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/39441?offset=280</link>
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	<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>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42923/flanker</guid>
	<pubDate>Sat, 27 Feb 2021 22:04:53 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42923/flanker</link>
	<title><![CDATA[Flanker]]></title>
	<description><![CDATA[<p><span>Flanker, a Python package which performs alignment-free clustering of gene flanking sequences in a consistent format, allowing investigation of&nbsp;<span>mobile genetic elements (</span>MGEs) without prior knowledge of their structure.&nbsp;<span>Flanker can be flexibly parameterised to finetune outputs by characterising upstream and downstream regions separately and investigating variable lengths of flanking sequence.</span></span></p>
<p><span><img src="https://github.com/wtmatlock/flanker/raw/main/docs/frontpage.png" alt="image" style="border: 0px;"></span></p><p>Address of the bookmark: <a href="https://github.com/wtmatlock/flanker" rel="nofollow">https://github.com/wtmatlock/flanker</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34567/jobtree-based-python-wrapper-to-run-the-genome-simulation-tool-suite-evolver</guid>
	<pubDate>Fri, 08 Dec 2017 16:26:32 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34567/jobtree-based-python-wrapper-to-run-the-genome-simulation-tool-suite-evolver</link>
	<title><![CDATA[jobTree based python wrapper to run the genome simulation tool suite Evolver]]></title>
	<description><![CDATA[<p><span>evolverSimControl</span><span>&nbsp;(</span><span>eSC</span><span>) can be used to simulate multi-chromosome genome evolution on an arbitrary phylogeny (</span><a href="http://evolution.genetics.washington.edu/phylip/newicktree.html">Newick format</a><span>). In addition to simply running evolver,&nbsp;</span><span>eSC</span><span>&nbsp;also automatically creates statistical summaries of the simulation as it runs including text and image files. Also included are convenience scripts to: check on a running simulation and see detailed status and logging information; extract fasta sequence files from the leaf nodes of a completed simulation; extract pairwise multiple alignment files (</span><a href="http://genome.ucsc.edu/FAQ/FAQformat.html#format5">.maf</a><span>) from leaf and branch nodes from a completed simulation and with the help of&nbsp;</span><a href="https://github.com/dentearl/mafTools/">mafJoin</a><span>, join them together into a single maf covering the entire simulation.</span></p><p>Address of the bookmark: <a href="https://github.com/dentearl/evolverSimControl" rel="nofollow">https://github.com/dentearl/evolverSimControl</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40272/seq-a-high-performance-pythonic-language-for-bioinformatics</guid>
	<pubDate>Sat, 23 Nov 2019 08:58:12 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40272/seq-a-high-performance-pythonic-language-for-bioinformatics</link>
	<title><![CDATA[Seq: A high-performance, Pythonic language for bioinformatics]]></title>
	<description><![CDATA[<p>&nbsp;</p>
<p>Seq is a programming language for computational genomics and bioinformatics. With a Python-compatible syntax and a host of domain-specific features and optimizations, Seq makes writing high-performance genomics software as easy as writing Python code, and achieves performance comparable to (and in many cases better than) C/C++.</p>
<p>Learn more by following the&nbsp;<a href="https://github.com/seq-lang/seq/blob/master/docs/sphinx/tutorial.rst">tutorial</a>&nbsp;or from the&nbsp;<a href="https://github.com/seq-lang/seq/blob/master/docs/sphinx/cookbook.rst">cookbook</a>.</p><p>Address of the bookmark: <a href="https://seq-lang.org" rel="nofollow">https://seq-lang.org</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44373/mitohifi-a-python-pipeline-for-mitochondrial-genome-assembly-from-pacbio-high-fidelity-reads</guid>
	<pubDate>Tue, 05 Sep 2023 07:31:35 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44373/mitohifi-a-python-pipeline-for-mitochondrial-genome-assembly-from-pacbio-high-fidelity-reads</link>
	<title><![CDATA[MitoHiFi: a python pipeline for mitochondrial genome assembly from PacBio high fidelity reads]]></title>
	<description><![CDATA[<p dir="auto">MitoHiFi v3.2 is a python pipeline distributed under&nbsp;<a href="https://github.com/marcelauliano/MitoHiFi/blob/master/LICENSE">MIT License</a>&nbsp;!</p>
<p dir="auto">MitoHiFi was first developed to assemble the mitogenomes for a wide range of species in the Darwin Tree of Life Project (DToL)</p>
<p dir="auto">https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-023-05385-y&nbsp;</p>
<p dir="auto"><a href="https://github.com/marcelauliano/MitoHiFi/blob/master/docs/dtol-logo-round-300x132.png" target="_blank"><img src="https://github.com/marcelauliano/MitoHiFi/raw/master/docs/dtol-logo-round-300x132.png" alt="" style="border: 0px; border: 0px;"></a></p><p>Address of the bookmark: <a href="https://github.com/marcelauliano/MitoHiFi" rel="nofollow">https://github.com/marcelauliano/MitoHiFi</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40996/sequanix-a-dynamic-graphical-interface-for-snakemake-workflows</guid>
	<pubDate>Wed, 12 Feb 2020 01:20:34 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40996/sequanix-a-dynamic-graphical-interface-for-snakemake-workflows</link>
	<title><![CDATA[Sequanix: a dynamic graphical interface for Snakemake workflows]]></title>
	<description><![CDATA[<ol>
<li>A Python library dedicated to NGS analysis (e.g., tools to visualise standard NGS formats).</li>
<li>A set of <a href="https://sequana.readthedocs.io/en/master/pipelines.html#pipelines"><span>pipelines</span></a> dedicated to NGS in the form of Snakefiles (Makefile-like with Python syntax based on snakemake framework) with more than 80 re-usable rules (see <a href="https://sequana.readthedocs.io/en/master/rules.html#rules"><span>Rules</span></a>).</li>
<li>Original tools to help in the creation of such pipelines including HTML reports.</li>
<li><dl><dt><a href="https://sequana.readthedocs.io/en/master/applications.html#applications"><span>Standalone applications</span></a>:</dt><dd><ol>
<li><a href="https://sequana.readthedocs.io/en/master/applications.html#standalone-sequana-coverage"><span>sequana_coverage</span></a> ease the extraction of genomic regions of interest and genome coverage information</li>
<li><a href="https://sequana.readthedocs.io/en/master/applications.html#standalone-sequana-taxonomy"><span>sequana_taxonomy</span></a> performs a quick taxonomy of your FastQ. This requires dedicated databases to be downloaded.</li>
<li><a href="https://sequana.readthedocs.io/en/master/applications.html#sequanix"><span>Sequanix: GUI for snakemake workflows</span></a>, a GUI for Snakemake workflows (hence Sequana pipelines as well)</li>
</ol>
<p>More at https://sequana.readthedocs.io/en/master/</p>
</dd></dl></li>
</ol><p>Address of the bookmark: <a href="https://github.com/sequana/sequana" rel="nofollow">https://github.com/sequana/sequana</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43353/judi-just-do-it</guid>
	<pubDate>Mon, 06 Sep 2021 02:44:35 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43353/judi-just-do-it</link>
	<title><![CDATA[JUDI: Just Do It]]></title>
	<description><![CDATA[<p><em>judi</em><span>&nbsp;comes from the idea of bringing the power and efficiency of&nbsp;</span><em>doit</em><span>&nbsp;to execute any kind of task under many combinations of parameter settings.</span></p>
<p><span>https://github.com/ncbi/JUDI</span></p><p>Address of the bookmark: <a href="https://github.com/ncbi/JUDI" rel="nofollow">https://github.com/ncbi/JUDI</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36017/alpha-a-toolkit-for-automated-local-phylogenomic-analyses</guid>
	<pubDate>Wed, 21 Mar 2018 18:12:06 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36017/alpha-a-toolkit-for-automated-local-phylogenomic-analyses</link>
	<title><![CDATA[ALPHA: A Toolkit for Automated Local Phylogenomic Analyses]]></title>
	<description><![CDATA[<p><span>Automated Local Phylogenomic Analyses, or ALPHA, is a python-based application that provides an intuitive user interface for phylogenetic analyses and data visualization. It has four distinct modes that are useful for different types of phylogenetic analysis: RAxML, File Converter, MS Comparison, and D-statistic.</span></p><p>Address of the bookmark: <a href="https://github.com/chilleo/ALPHA" rel="nofollow">https://github.com/chilleo/ALPHA</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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