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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/32154?offset=110</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/19555/a-3d-map-of-the-human-genome</guid>
	<pubDate>Fri, 12 Dec 2014 22:27:55 -0600</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/19555/a-3d-map-of-the-human-genome</link>
	<title><![CDATA[A 3D Map of the Human Genome]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/dES-ozV65u4" frameborder="0" allowfullscreen></iframe>Suhas Rao and Miriam Huntley (of the Aiden Lab) describe a 3D map of the human genome at kilobase resolution, revealing the principles of chromatin looping. Guest Origami Folding: Sarah Nyquist.

Suhas S.P. Rao*, Miriam H. Huntley*, Neva C. Durand, Elena K. Stamenova, Ivan D. Bochkov, James T. Robinson, Adrian L. Sanborn, Ido Machol, Arina D. Omer, Eric S. Lander, Erez Lieberman Aiden. (2014). A 3D Map of the Human Genome at Kilobase Resolution Reveals Principles of Chromatin Looping. Cell.]]></description>
	
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40465/airlift-a-methodology-and-tool-for-comprehensively-moving-mappings-and-annotations-from-one-genome-to-another-similar-genome</guid>
	<pubDate>Mon, 23 Dec 2019 10:20:13 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40465/airlift-a-methodology-and-tool-for-comprehensively-moving-mappings-and-annotations-from-one-genome-to-another-similar-genome</link>
	<title><![CDATA[AirLift, a methodology and tool for comprehensively moving mappings and annotations from one genome to another similar genome]]></title>
	<description><![CDATA[<p>We propose AirLift, a methodology and tool for comprehensively moving mappings and annotations from one genome to another similar genome while maintaining the accuracy of a full mapper.</p><p>Address of the bookmark: <a href="https://github.com/CMU-SAFARI/AirLift" rel="nofollow">https://github.com/CMU-SAFARI/AirLift</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43711/vcf-compare</guid>
	<pubDate>Wed, 19 Jan 2022 10:30:14 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43711/vcf-compare</link>
	<title><![CDATA[VCF Compare !]]></title>
	<description><![CDATA[<h2><span>compare two&nbsp;<strong>BWA</strong>&nbsp;mapping methods with the online hg18-mapped data</span></h2>
<p>We first operate a rapid inspection of the different BAM files using&nbsp;<strong>samtools flagstat</strong>. Illumina provided chr21 read mapping obtained with their&nbsp;<strong>GA IIx</strong>&nbsp;deep sequencing platform &lt;<a href="ftp://webdata:webdata@ussd-ftp.illumina.com/Data/SequencingRuns/NA18507_GAIIx_100_chr21.bam" target="_blank">ftp://webdata:webdata@ussd-ftp.illumina.com/Data/SequencingRuns/NA18507_GAIIx_100_chr21.bam</a>&gt;, aligned to the b36/hg18 reference genome)</p><p>Address of the bookmark: <a href="https://wiki.bits.vib.be/index.php/NGS_Exercise.6#compare_aln_.26_mem_results_with_vcf-compare" rel="nofollow">https://wiki.bits.vib.be/index.php/NGS_Exercise.6#compare_aln_.26_mem_results_with_vcf-compare</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/22793/sequencing-by-xpansion</guid>
	<pubDate>Wed, 17 Jun 2015 20:58:11 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/22793/sequencing-by-xpansion</link>
	<title><![CDATA[Sequencing By Xpansion]]></title>
	<description><![CDATA[<p>Sequencing By Xpansion (SBX) is a DNA sequencing method that uses a simple biochemical reaction to encode the sequence of a DNA molecule into a highly measurable surrogate called an Xpandomer. This single molecule approach produces enough Xpandomer in a single drop reaction to sequence an entire human genome 1000X over. To achieve this, an Xpandomer replaces each DNA sequence with a sequence of large, high signal reporter molecules using the SBX molecular expansion technology. The DNA sequence is then read out as the Xpandomer reporters pass sequentially through a nanopore detector. SBX is a molecular engineering platform that benefits from core design principles that separate the multiple molecular functions. This systems approach enables efficient development and incorporation of improvements to SBX and is key to reconfiguring and optimizing Xpandomer measurement for different detection platforms.</p><p>http://www.stratosgenomics.com/stratos-genomics-technology</p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37982/raven-a-software-suite-for-matlab-that-allows-for-semi-automated-reconstruction-of-genome-scale-models</guid>
	<pubDate>Wed, 24 Oct 2018 22:38:05 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37982/raven-a-software-suite-for-matlab-that-allows-for-semi-automated-reconstruction-of-genome-scale-models</link>
	<title><![CDATA[RAVEN: a software suite for Matlab that allows for semi-automated reconstruction of genome-scale models]]></title>
	<description><![CDATA[<p><span>The RAVEN (Reconstruction, Analysis and Visualization of Metabolic Networks) Toolbox 2 is a software suite for Matlab that allows for semi-automated reconstruction of genome-scale models (GEMs). It makes use of published models and/or KEGG, MetaCyc databases, coupled with extensive gap-filling and quality control features. The software suite also contains methods for visualizing simulation results and omics data, as well as a range of methods for performing simulations and analyzing the results. The software is a useful tool for system-wide data analysis in a metabolic context and for streamlined reconstruction of metabolic networks based on protein homology.</span></p><p>Address of the bookmark: <a href="https://github.com/SysBioChalmers/RAVEN" rel="nofollow">https://github.com/SysBioChalmers/RAVEN</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26303/maker</guid>
	<pubDate>Sun, 07 Feb 2016 15:59:24 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26303/maker</link>
	<title><![CDATA[MAKER]]></title>
	<description><![CDATA[<p>MAKER is a portable and easily configurable genome annotation pipeline.Its purpose is to allow smaller eukaryotic and prokaryotic genome projects to independently annotate their genomes and to create genome databases. MAKER identifies repeats, aligns ESTs and proteins to a genome, produces ab-initio gene predictions and automatically synthesizes these data into gene annotations having evidence-based quality values.</p>
<p>More at http://www.yandell-lab.org/software/maker.html</p><p>Address of the bookmark: <a href="http://www.yandell-lab.org/software/maker.html" rel="nofollow">http://www.yandell-lab.org/software/maker.html</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26426/genome-browser-gbrowse</guid>
	<pubDate>Fri, 19 Feb 2016 09:22:43 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26426/genome-browser-gbrowse</link>
	<title><![CDATA[Genome Browser : GBrowse]]></title>
	<description><![CDATA[<p>Generic Genome Browser Version 2: A Tutorial for Administrators</p>
<p>This is an extensive tutorial to take you through the main features and gotchas of configuring GBrowse as a server. This tutorial assumes that you have successfully set up Perl, GD, BioPerl and the other GBrowse dependencies. If you haven't, please see the <a href="http://gmod.org/wiki/GBrowse_2.0_HOWTO">GBrowse HOWTO</a> During most of the tutorial, we will be using the "in-memory" GBrowse database (no relational database required!) Later we will show how to set up a genome size database using the berkeleydb and MySQL adaptors.</p>
<p>More at http://elp.ucdavis.edu/tutorial/tutorial.html</p><p>Address of the bookmark: <a href="http://elp.ucdavis.edu/tutorial/tutorial.html" rel="nofollow">http://elp.ucdavis.edu/tutorial/tutorial.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<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>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29103/genome-strip</guid>
	<pubDate>Tue, 06 Sep 2016 03:58:19 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29103/genome-strip</link>
	<title><![CDATA[Genome STRiP]]></title>
	<description><![CDATA[<p><strong>Genome STRiP</strong><span>&nbsp;(Genome STRucture In Populations) is a suite of tools for discovering and genotyping structural variations using sequencing data. The methods are designed to detect shared variation using data from multiple individuals.</span><br><br><span>Genome STRiP looks both across and within a set of sequenced genomes to detect variation. The methods are adaptive and support heterogeneous data sets, including variations in sequencing depth, read lengths and mixtures of paired and single-end reads. A minimum of 20 to 30 genomes are required to get acceptable results, but the method gains power across genomes and processing more genomes provide better results.</span><br><br><span>To run discovery or genotyping on a single sequenced genome or a small set of genomes, you need to call your data against a background population, such as a set of genomes from the 1000 Genomes Project.&nbsp; The background population does not need to be matched to the target individuals.</span></p><p>Address of the bookmark: <a href="http://software.broadinstitute.org/software/genomestrip/" rel="nofollow">http://software.broadinstitute.org/software/genomestrip/</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29018/crossmap</guid>
	<pubDate>Mon, 05 Sep 2016 04:07:38 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29018/crossmap</link>
	<title><![CDATA[CrossMap]]></title>
	<description><![CDATA[<ul>
<li>CrossMap is a program for convenient conversion of genome coordinates (or annotation files) between&nbsp;<em>different assemblies</em>&nbsp;(such as Human&nbsp;<a href="http://www.ncbi.nlm.nih.gov/assembly/2928/">hg18 (NCBI36)</a>&nbsp;&lt;&gt;&nbsp;<a href="http://www.ncbi.nlm.nih.gov/assembly/2758/">hg19 (GRCh37)</a>, Mouse&nbsp;<a href="http://www.ncbi.nlm.nih.gov/assembly/165668/">mm9 (MGSCv37)</a>&nbsp;&lt;&gt;&nbsp;<a href="http://www.ncbi.nlm.nih.gov/assembly/327618/">mm10 (GRCm38)</a>).</li>
<li>It supports most commonly used file formats including SAM/BAM, Wiggle/BigWig, BED, GFF/GTF, VCF.</li>
<li>CrossMap is designed to liftover genome coordinates between assemblies. It&rsquo;s&nbsp;<em>not</em>&nbsp;a program for aligning sequences to reference genome.</li>
<li>We&nbsp;<em>do not</em>&nbsp;recommend using CrossMap to convert genome coordinates between species.</li>
</ul><p>Address of the bookmark: <a href="http://crossmap.sourceforge.net/" rel="nofollow">http://crossmap.sourceforge.net/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
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