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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/38735?offset=70</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43652/peregrine-shimmer-genome-assembly-toolkit</guid>
	<pubDate>Thu, 16 Dec 2021 02:50:19 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43652/peregrine-shimmer-genome-assembly-toolkit</link>
	<title><![CDATA[Peregrine &amp; SHIMMER Genome Assembly Toolkit]]></title>
	<description><![CDATA[<p><span>Peregrine is a fast genome assembler for accurate long reads (length &gt; 10kb, accuracy &gt; 99%). It can assemble a human genome from 30x reads within 20 cpu hours from reads to polished consensus. It uses Sparse HIereachical MimiMizER (SHIMMER) for fast read-to-read overlaping without quadratic comparisions used in other OLC assemblers.</span></p><p>Address of the bookmark: <a href="https://github.com/cschin/Peregrine" rel="nofollow">https://github.com/cschin/Peregrine</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36635/circlator-automated-circularization-of-genome-assemblies-using-long-sequencing-reads</guid>
	<pubDate>Tue, 15 May 2018 09:42:32 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36635/circlator-automated-circularization-of-genome-assemblies-using-long-sequencing-reads</link>
	<title><![CDATA[Circlator: automated circularization of genome assemblies using long sequencing reads]]></title>
	<description><![CDATA[A tool to circularize genome assemblies. The algorithm and benchmarks are described in the Genome Biology manuscript. 

Citation: "Circlator: automated circularization of genome assemblies using long sequencing reads", Hunt et al, Genome Biology 2015 Dec 29;16(1):294. doi: 10.1186/s13059-015-0849-0. PMID: 26714481.<p>Address of the bookmark: <a href="http://sanger-pathogens.github.io/circlator/" rel="nofollow">http://sanger-pathogens.github.io/circlator/</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40531/shasta-long-read-assembler</guid>
	<pubDate>Tue, 14 Jan 2020 06:47:07 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40531/shasta-long-read-assembler</link>
	<title><![CDATA[Shasta long read assembler]]></title>
	<description><![CDATA[<p>The goal of the Shasta long read assembler is to rapidly produce accurate assembled sequence using as input DNA reads generated by&nbsp;<a href="https://nanoporetech.com/">Oxford Nanopore</a>&nbsp;flow cells.</p>
<p>Computational methods used by the Shasta assembler include:</p>
<ul>
<li>Using a&nbsp;<a href="https://en.wikipedia.org/wiki/Run-length_encoding">run-length</a>&nbsp;representation of the read sequence. This makes the assembly process more resilient to errors in homopolymer repeat counts, which are the most common type of errors in Oxford Nanopore reads.</li>
<li>Using in some phases of the computation a representation of the read sequence based on&nbsp;<em>markers</em>, a fixed subset of short k-mers (k &asymp; 10).</li>
</ul>
<p>More at&nbsp;<a href="https://chanzuckerberg.github.io/shasta/index.html">https://chanzuckerberg.github.io/shasta/index.html</a></p><p>Address of the bookmark: <a href="https://github.com/chanzuckerberg/shasta" rel="nofollow">https://github.com/chanzuckerberg/shasta</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41896/kad-assessing-genome-assemblies-using-k-mer-copies-in-assemblies-and-k-mer-abundance-in-illumina-reads</guid>
	<pubDate>Fri, 19 Jun 2020 07:34:12 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41896/kad-assessing-genome-assemblies-using-k-mer-copies-in-assemblies-and-k-mer-abundance-in-illumina-reads</link>
	<title><![CDATA[KAD: Assessing genome assemblies using K-mer copies in assemblies and K-mer abundance in Illumina reads]]></title>
	<description><![CDATA[<p>KAD is designed for evaluating the accuracy of nucleotide base quality of genome assemblies. Briefly, abundance of k-mers are quantified for both sequencing reads and assembly sequences. Comparison of the two values results in a single value per k-mer, K-mer Abundance Difference (KAD), which indicates how well the assembly matches read data for each k-mer.</p>
<p><a href="https://render.githubusercontent.com/render/math?math=KAD=log_{2}\begin{pmatrix}\frac{c%2Bm}{m(n%2B1)}\end{pmatrix}" target="_blank"><img src="https://render.githubusercontent.com/render/math?math=KAD=log_{2}\begin{pmatrix}\frac{c%2Bm}{m(n%2B1)}\end{pmatrix}" alt="image" style="border: 0px;"></a></p>
<p>where,&nbsp;<em>c</em>&nbsp;is the count of a k-mer from reads,&nbsp;<em>m</em>&nbsp;is the mode of counts of read k-mers, and&nbsp;<em>n</em>&nbsp;is the copy of the k-mer in the assembly.</p><p>Address of the bookmark: <a href="https://github.com/liu3zhenlab/KAD" rel="nofollow">https://github.com/liu3zhenlab/KAD</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43112/calling-variants-in-non-diploid-systems</guid>
	<pubDate>Sat, 26 Jun 2021 15:37:49 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43112/calling-variants-in-non-diploid-systems</link>
	<title><![CDATA[Calling variants in non-diploid systems]]></title>
	<description><![CDATA[<p><span>The main challenge associated with non-diploid variant calling is the difficulty in distinguishing between the sequencing noise (abundant in all NGS platforms) and true low frequency variants. Some of the early attempts to do this well have been accomplished on human mitochondrial&nbsp;</span><span>DNA</span><span>&nbsp;although the same approaches will work equally good on viral and bacterial genomes (</span><a href="https://training.galaxyproject.org/training-material/topics/variant-analysis/tutorials/non-dip/tutorial.html#Rebolledo-Jaramillo2014">Rebolledo-Jaramillo&nbsp;<em>et al.</em>&nbsp;2014</a><span>,&nbsp;</span><a href="https://training.galaxyproject.org/training-material/topics/variant-analysis/tutorials/non-dip/tutorial.html#Li2015">Li&nbsp;<em>et al.</em>&nbsp;2015</a><span>).</span></p><p>Address of the bookmark: <a href="https://training.galaxyproject.org/training-material/topics/variant-analysis/tutorials/non-dip/tutorial.html" rel="nofollow">https://training.galaxyproject.org/training-material/topics/variant-analysis/tutorials/non-dip/tutorial.html</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26414/advanced-bash-scripting-guide</guid>
	<pubDate>Thu, 18 Feb 2016 04:50:51 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26414/advanced-bash-scripting-guide</link>
	<title><![CDATA[Advanced Bash-Scripting Guide]]></title>
	<description><![CDATA[<p>This tutorial assumes no previous knowledge of scripting or programming, yet progresses rapidly toward an intermediate/advanced level of instruction <em>. . . all the while sneaking in little nuggets of <span>UNIX</span>&reg; wisdom and lore</em>. It serves as a textbook, a manual for self-study, and as a reference and source of knowledge on shell scripting techniques. The exercises and heavily-commented examples invite active reader participation, under the premise that <tt><strong>the only way to really learn scripting is to write scripts</strong></tt>.</p>
<p>This book is suitable for classroom use as a general introduction to programming concepts.</p>
<p>More at http://tldp.org/LDP/abs/html/</p><p>Address of the bookmark: <a href="http://tldp.org/LDP/abs/html/" rel="nofollow">http://tldp.org/LDP/abs/html/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27967/linux-command-line-exercises-for-ngs-data-processing</guid>
	<pubDate>Wed, 22 Jun 2016 07:59:39 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27967/linux-command-line-exercises-for-ngs-data-processing</link>
	<title><![CDATA[Linux command line exercises for NGS data processing]]></title>
	<description><![CDATA[<p>The purpose of this tutorial is to introduce students to the frequently used tools for NGS analysis as well as giving experience in writing one-liners. Copy the required files to your current directory, change directory (<code>cd</code>) to the <code>linuxTutorial</code> folder, and do all the processing inside:</p>
<pre><span>[uzi@quince-srv2 ~/]$</span> cp -r /home/opt/MScBioinformatics/linuxTutorial .
<span>[uzi@quince-srv2 ~/]$</span> cd linuxTutorial
<span>[uzi@quince-srv2 ~/linuxTutorial]$</span>
</pre>
<p>I have deliberately chosen <code>Awk</code> in the exercises as it is a language in itself and is used more often to manipulate NGS data as compared to the other command line tools such as <code>grep</code>, <code>sed</code>, <code>perl</code> etc. Furthermore, having a command on <code>awk</code> will make it easier to understand advanced tutorials such as <a href="http://userweb.eng.gla.ac.uk/umer.ijaz/bioinformatics/Illumina_workflow.html">Illumina Amplicons Processing Workflow</a>. <br><br> In <code>Linux</code>, we use a shell that is a program that takes your commands from the keyboard and gives them to the operating system. Most Linux systems utilize Bourne Again SHell (<code>bash</code>), but there are several additional shell programs on a typical Linux system such as <code>ksh</code>, <code>tcsh</code>, and <code>zsh</code>. To see which shell you are using, type</p>
<pre><span>[uzi@quince-srv2 ~/linuxTutorial]$</span> echo $SHELL

<span>/bin/bash
</span></pre><p>Address of the bookmark: <a href="http://userweb.eng.gla.ac.uk/umer.ijaz/bioinformatics/linux.html" rel="nofollow">http://userweb.eng.gla.ac.uk/umer.ijaz/bioinformatics/linux.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29029/ngs-tutorial</guid>
	<pubDate>Mon, 05 Sep 2016 09:50:46 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29029/ngs-tutorial</link>
	<title><![CDATA[NGS Tutorial]]></title>
	<description><![CDATA[<p><span>These tutorials are written for hundreds of bioinformaticians trying to cope with large volume of next-generation sequencing (NGS) data. NGS technologies brought a dramatic shift in the world of sequencing. Merely five years back, genome sequencing of higher eukaryotes used to be very expensive endeavor. To get a genome of interest sequenced, hundreds of scientists had to raise funds together by writing a joint white-paper and petitioning to various government agencies. The tasks of sequencing and assembly were handled by dedicated sequencing facilities, of which only a few existed around the globe. Naturally, the capacities at those sequencing facilities were significantly constrained from high volume of requests</span></p><p>Address of the bookmark: <a href="http://www.homolog.us/Tutorials/index.php" rel="nofollow">http://www.homolog.us/Tutorials/index.php</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36880/jvarkit-java-utilities-for-bioinformatics</guid>
	<pubDate>Fri, 08 Jun 2018 09:31:55 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36880/jvarkit-java-utilities-for-bioinformatics</link>
	<title><![CDATA[Jvarkit : Java utilities for Bioinformatics]]></title>
	<description><![CDATA[Collection of Java tool kits for bioinformatics works:

Jvarkit : Java utilities for Bioinformatics<p>Address of the bookmark: <a href="http://lindenb.github.io/jvarkit/" rel="nofollow">http://lindenb.github.io/jvarkit/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37576/lrcstats-a-tool-for-evaluating-long-reads-correction-methods</guid>
	<pubDate>Wed, 22 Aug 2018 11:05:04 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37576/lrcstats-a-tool-for-evaluating-long-reads-correction-methods</link>
	<title><![CDATA[LRCstats: a tool for evaluating long reads correction methods]]></title>
	<description><![CDATA[<p><span>LRCstats is an open-source pipeline for benchmarking DNA long read correction algorithms for long reads outputted by third generation sequencing technology such as machines produced by Pacific Biosciences. The reads produced by third generation sequencing technology, as the name suggests, are longer in length than reads produced by next generation sequencing technologies, such as those produced by Illumina. However, long reads are plagued by high error rates, which can cause issues in downstream analysis. Long read correction algorithms reduce the error rate of long reads either through self-correcting methods or using accurate, short reads outputted by next generation sequencing technologies to correct long reads.</span></p><p>Address of the bookmark: <a href="https://github.com/cchauve/lrcstats" rel="nofollow">https://github.com/cchauve/lrcstats</a></p>]]></description>
	<dc:creator>Aaryan Lokwani</dc:creator>
</item>

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