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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/36360?offset=30</link>
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	<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/bookmarks/view/35800/scikit-bio%E2%84%A2-is-an-open-source-bsd-licensed-python-package-providing-data-structures-algorithms-and-educational-resources-for-bioinformatics</guid>
	<pubDate>Fri, 02 Mar 2018 04:29:47 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35800/scikit-bio%E2%84%A2-is-an-open-source-bsd-licensed-python-package-providing-data-structures-algorithms-and-educational-resources-for-bioinformatics</link>
	<title><![CDATA[scikit-bio™ is an open-source, BSD-licensed, python package providing data structures, algorithms, and educational resources for bioinformatics.]]></title>
	<description><![CDATA[<p><span>scikit-bio is currently in beta. We are very actively developing it, and&nbsp;</span><strong>backward-incompatible interface changes can and will arise</strong><span>. To avoid these types of changes being a surprise to our users, our public APIs are decorated to make it clear to users when an API can be relied upon (stable) and when it may be subject to change (experimental). See the&nbsp;</span><a href="https://github.com/biocore/scikit-bio/blob/master/doc/source/user/api_stability.rst">API stability docs</a><span>&nbsp;for more details, including what we mean by&nbsp;</span><em>stable</em><span>&nbsp;and&nbsp;</span><em>experimental</em><span>&nbsp;in this context.</span></p><p>Address of the bookmark: <a href="http://scikit-bio.org/" rel="nofollow">http://scikit-bio.org/</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/34049/libsvm-a-library-for-support-vector-machines</guid>
	<pubDate>Wed, 02 Aug 2017 06:49:05 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34049/libsvm-a-library-for-support-vector-machines</link>
	<title><![CDATA[LIBSVM -- A Library for Support Vector Machines]]></title>
	<description><![CDATA[<p><strong>LIBSVM&nbsp;</strong>is an integrated software for support vector classification, (C-SVC,&nbsp;<a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#nuandone">nu-SVC</a>), regression (epsilon-SVR,&nbsp;<a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#nuandone">nu-SVR</a>) and distribution estimation (<a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#nuandone">one-class SVM</a>). It supports multi-class classification.</p>
<p>Since version 2.8, it implements an SMO-type algorithm proposed in this paper:<br>R.-E. Fan, P.-H. Chen, and C.-J. Lin.&nbsp;<a href="https://www.csie.ntu.edu.tw/~cjlin/papers/quadworkset.pdf">Working set selection using second order information for training SVM</a>. Journal of Machine Learning Research 6, 1889-1918, 2005. You can also find a pseudo code there. (<a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html#f203">how to cite LIBSVM</a>)</p>
<p><span style="color: #ff0000;">Our goal is to help users from other fields to easily use SVM as a tool.&nbsp;</span><strong>LIBSVM&nbsp;</strong>provides a simple interface where users can easily link it with their own programs. Main features of&nbsp;<strong>LIBSVM</strong>&nbsp;include</p>
<ul>
<li>Different SVM formulations</li>
<li>Efficient multi-class classification</li>
<li>Cross validation for model selection</li>
<li>Probability estimates</li>
<li>Various kernels (including precomputed kernel matrix)</li>
<li>Weighted SVM for unbalanced data</li>
<li>Both C++ and&nbsp;<a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#java">Java</a>&nbsp;sources</li>
<li><a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#GUI">GUI</a>&nbsp;demonstrating SVM classification and regression</li>
<li><a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#python">Python</a>,&nbsp;<a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#R">R</a>,&nbsp;<a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#matlab">MATLAB</a>,&nbsp;<a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#perl">Perl</a>,&nbsp;<a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#ruby">Ruby</a>,&nbsp;<a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#weka">Weka</a>,&nbsp;<a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#lisp">Common LISP</a>,&nbsp;<a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#clisp">CLISP</a>,&nbsp;<a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#haskell">Haskell</a>,&nbsp;<a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#ocaml">OCaml</a>,&nbsp;<a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#labview">LabVIEW</a>, and&nbsp;<a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#PHP">PHP</a>&nbsp;interfaces.&nbsp;<a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#csharp">C# .NET</a>&nbsp;code and&nbsp;<a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#cuda">CUDA</a>&nbsp;extension is available.&nbsp;<br>It's also included in some data mining environments:&nbsp;<a href="http://rapid-i.com/">RapidMiner</a>,&nbsp;<a href="http://pcp.sourceforge.net/">PCP</a>, and&nbsp;<a href="http://lionoso.org/">LIONsolver</a>.</li>
<li>Automatic model selection which can generate contour of cross validation accuracy.</li>
<li></li>
</ul>
<p>https://www.csie.ntu.edu.tw/~cjlin/libsvm/</p><p>Address of the bookmark: <a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/" rel="nofollow">https://www.csie.ntu.edu.tw/~cjlin/libsvm/</a></p>]]></description>
	<dc:creator>Neel</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/41734/supernova-generates-phased-whole-genome-de-novo-assemblies-from-a-chromium-prepared-library</guid>
	<pubDate>Sun, 31 May 2020 01:59:30 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41734/supernova-generates-phased-whole-genome-de-novo-assemblies-from-a-chromium-prepared-library</link>
	<title><![CDATA[Supernova: generates phased, whole-genome de novo assemblies from a Chromium-prepared library.]]></title>
	<description><![CDATA[<p>Supernova generates phased, whole-genome&nbsp;<em>de novo</em>&nbsp;assemblies from a Chromium-prepared library.</p>
<p>Please see&nbsp;<a href="https://support.10xgenomics.com/de-novo-assembly/guidance/doc/achieving-success-with-de-novo-assembly">Achieving Success with De Novo Assembly</a>&nbsp;and&nbsp;<a href="https://support.10xgenomics.com/de-novo-assembly/software/overview/system-requirements">System Requirements</a>&nbsp;<em>before</em>&nbsp;creating your Chromium libraries for assembly.</p>
<p>Supernova should be run using 38-56x coverage of the genome.<br>&bull; Somewhat higher coverage is&nbsp;<em>sometimes</em>&nbsp;advantageous.<br>&bull; Supernova will exit if it finds that coverage is far from the recommended range.<br>&bull; Note that at most 2.14 billion reads are allowed.<br>&bull; Please note that we have not extensively tested genomes larger than human, and any genome above approximately 4 GB should be considered experimental and is not supported.</p><p>Address of the bookmark: <a href="https://support.10xgenomics.com/de-novo-assembly/software/pipelines/latest/using/running" rel="nofollow">https://support.10xgenomics.com/de-novo-assembly/software/pipelines/latest/using/running</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34141/rami-a-tool-for-identification-and-characterization-of-phylogenetic-clusters-in-microbial-communities</guid>
	<pubDate>Mon, 07 Aug 2017 18:49:27 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34141/rami-a-tool-for-identification-and-characterization-of-phylogenetic-clusters-in-microbial-communities</link>
	<title><![CDATA[RAMI: a tool for identification and characterization of phylogenetic clusters in microbial communities]]></title>
	<description><![CDATA[<p>RAMI, which clusters related nodes in a phylogenetic tree based on the patristic distance. RAMI also produces indices of cluster properties and other indices used in population and community studies on-the-fly.</p>
<p><strong>Availability:</strong>&nbsp;RAMI is licensed under GNU GPL and can be run or downloaded from&nbsp;<a href="http://www.acgt.se/online.html" target="">http://www.acgt.se/online.html</a>.</p><p>Address of the bookmark: <a href="https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btp051" rel="nofollow">https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btp051</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</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>

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