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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/30214?offset=40</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29008/circos-visualize</guid>
	<pubDate>Fri, 02 Sep 2016 08:29:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29008/circos-visualize</link>
	<title><![CDATA[CIRCOS Visualize !!]]></title>
	<description><![CDATA[<p>Before uploading a data file, check the&nbsp;<a href="http://mkweb.bcgsc.ca/tableviewer/samples">samples gallery</a>&nbsp;to make sure that your data format is compatible.</p>
<ul>
<li>Your file must be&nbsp;<strong>plain text</strong>.</li>
<li>Your data values must be&nbsp;<strong>non-negative integers</strong>.</li>
<li>Data must be space-separated (<strong>one or more</strong>&nbsp;tab or space, which will be collapsed).</li>
<li>No two rows or columns may have the same name.</li>
<li>Column and row names must&nbsp;<strong>begin with a letter</strong>&nbsp;(e.g. 'A', 'A0', 'A-0') and can only contain letters, numbers and _. No punctuation!</li>
<li>Maximum row + column total is 150 &mdash; if exceeded, rows and columns are limited to 75.</li>
<li>If you are using order, size and color rows/columns in combination they must appear in that order.</li>
</ul>
<p>Need help? Post questions to the&nbsp;<a href="https://groups.google.com/forum/#!forum/circos-data-visualization">Circos Google Group</a>.</p>
<p>http://mkweb.bcgsc.ca/tableviewer/visualize/</p><p>Address of the bookmark: <a href="http://mkweb.bcgsc.ca/tableviewer/visualize/" rel="nofollow">http://mkweb.bcgsc.ca/tableviewer/visualize/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29112/sybil</guid>
	<pubDate>Wed, 07 Sep 2016 03:20:44 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29112/sybil</link>
	<title><![CDATA[Sybil]]></title>
	<description><![CDATA[<p><span>The Sybil software package provides a primarily web-based front-end to comparative genome datasets warehoused in a chado relational database. It was developed by the bioinformatics department at The Institute for Genomic Research (</span><a href="http://www.tigr.org/">TIGR</a><span>) and development continues at the J. Craig Venter Institute (</span><a href="http://jcvi.org/">JCVI</a><span>) and the Institute for Genome Sciences (</span><a href="http://igs.umaryland.edu/">IGS</a><span>) at the University of Maryland: Baltimore. Sybil has been used at TIGR/JCVI, IGS, NYU, New York Medical College, Novartis Vaccines and University of Maryland: College Park to support a number of research projects that involve comparative genome analysis. The following sections provide some high-level technical details about the overall architecture and external dependencies of the Sybil package.</span></p><p>Address of the bookmark: <a href="http://sybil.sourceforge.net/" rel="nofollow">http://sybil.sourceforge.net/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29382/virmet</guid>
	<pubDate>Mon, 10 Oct 2016 08:27:19 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29382/virmet</link>
	<title><![CDATA[VirMet]]></title>
	<description><![CDATA[<p>Watch out: only a few files are counted in coverage statistics.</p>
<p>Full documentation on&nbsp;<a href="http://virmet.rtfd.org/en/latest/">Read the Docs</a>.</p>
<p>A set of tools for viral metagenomics.</p>
<p>virmet is called with a command subcommand syntax:&nbsp;<code>virmet fetch --viral n</code>, for example, downloads the bacterial database. Other available subcommands so far are</p>
<ul>
<li><code>fetch</code>&nbsp;download genomes</li>
<li><code>update</code>&nbsp;update viral/bacterial database</li>
<li><code>index</code>&nbsp;index genomes</li>
<li><code>wolfpack</code>&nbsp;analyze a Miseq run</li>
<li><code>covplot</code>&nbsp;plot coverage for a specific organism</li>
</ul>
<p>A short help is obtained with&nbsp;<code>virmet subcommand -h</code>.</p>
<p>More at&nbsp;https://github.com/ozagordi/VirMet</p><p>Address of the bookmark: <a href="https://github.com/ozagordi/VirMet" rel="nofollow">https://github.com/ozagordi/VirMet</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29500/genomescope-open-source-web-tool-to-rapidly-estimate-the-overall-characteristics-of-a-genome-including-genome-size-heterozygosity-rate-and-repeat-content-from-unprocessed-short-reads</guid>
	<pubDate>Fri, 21 Oct 2016 05:46:43 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29500/genomescope-open-source-web-tool-to-rapidly-estimate-the-overall-characteristics-of-a-genome-including-genome-size-heterozygosity-rate-and-repeat-content-from-unprocessed-short-reads</link>
	<title><![CDATA[GenomeScope: open-source web tool to rapidly estimate the overall characteristics of a genome, including genome size, heterozygosity rate, and repeat content from unprocessed short reads]]></title>
	<description><![CDATA[<div>
<div>
<div>
<div id="content-block-markup">
<div>
<div id="abstract-1">
<p id="p-2">Summary: GenomeScope is an open-source web tool to rapidly estimate the overall characteristics of a genome, including genome size, heterozygosity rate, and repeat content from unprocessed short reads. These features are essential for studying genome evolution, and help to choose parameters for downstream analysis. We demonstrate its accuracy on 324 simulated and 16 real datasets with a wide range in genome sizes, heterozygosity levels, and error rates. Availability and Implementation: http://qb.cshl.edu/genomescope/, https://github.com/schatzlab/genomescope.git</p>
</div>
<span></span></div>
<span></span></div>
</div>
</div>
</div><p>Address of the bookmark: <a href="http://qb.cshl.edu/genomescope/" rel="nofollow">http://qb.cshl.edu/genomescope/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29628/links</guid>
	<pubDate>Fri, 04 Nov 2016 06:19:01 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29628/links</link>
	<title><![CDATA[LINKS]]></title>
	<description><![CDATA[<p>LINKS is a genomics application for scaffolding genome assemblies with long reads, such as those produced by Oxford Nanopore Technologies Ltd. It can be used to scaffold high-quality draft genome assemblies with any long sequences (eg. ONT reads, PacBio reads, another draft genomes, etc)</p>
<p>Paper at&nbsp;https://gigascience.biomedcentral.com/articles/10.1186/s13742-015-0076-3</p><p>Address of the bookmark: <a href="https://github.com/warrenlr/LINKS/" rel="nofollow">https://github.com/warrenlr/LINKS/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30249/genome-assembly-tutorial</guid>
	<pubDate>Tue, 20 Dec 2016 07:56:01 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30249/genome-assembly-tutorial</link>
	<title><![CDATA[Genome Assembly Tutorial]]></title>
	<description><![CDATA[<p><span>If genomes were completely random sequences in a statistical sense, 'overlap-consensus-layout' method would have been enough to assemble large genomes from Sanger reads. In contrast, real genomes often have long repetitive regions, and they are hard to assemble using overlap-consensus-layout approach. De Bruijn graph-based assembly approach was originally proposed to handle the assembly of repetitive regions better.</span></p>
<p><span>More at&nbsp;http://www.homolog.us/Tutorials/index.php?p=1.4&amp;s=1</span></p><p>Address of the bookmark: <a href="http://www.homolog.us/Tutorials/index.php?p=1.4&amp;s=1" rel="nofollow">http://www.homolog.us/Tutorials/index.php?p=1.4&amp;s=1</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31343/metabat-an-efficient-tool-for-accurately-reconstructing-single-genomes-from-complex-microbial-communities</guid>
	<pubDate>Mon, 06 Mar 2017 03:44:34 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31343/metabat-an-efficient-tool-for-accurately-reconstructing-single-genomes-from-complex-microbial-communities</link>
	<title><![CDATA[MetaBAT:  An Efficient Tool for Accurately Reconstructing Single Genomes from Complex Microbial Communities]]></title>
	<description><![CDATA[<p>MetaBAT, An Efficient Tool for Accurately Reconstructing Single Genomes from Complex Microbial Communities</p>
<p>Grouping large genomic fragments assembled from shotgun metagenomic sequences to deconvolute complex microbial communities, or metagenome binning, enables the study of individual organisms and their interactions. Here we developed an automated metagenome binning software, called MetaBAT, which integrates empirical probabilistic distances of genome abundance and tetranucleotide frequency. Tested on both synthetic and real metagenome datasets, MetaBAT outperforms alternative methods in both accuracy and computational efficiency. Applying MetaBAT to an assembly from 1,704 human gut samples formed 1,634 genome bins (&gt;200kb) in 3 hours, where 621 genome bins are &gt;50% complete with &lt;5% contamination from other species. Further analysis shows that the quality of these genome bins approaches manually curated genomes.</p><p>Address of the bookmark: <a href="https://bitbucket.org/berkeleylab/metabat" rel="nofollow">https://bitbucket.org/berkeleylab/metabat</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31105/understanding-pacbio</guid>
	<pubDate>Fri, 24 Feb 2017 10:17:36 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31105/understanding-pacbio</link>
	<title><![CDATA[Understanding PacBio]]></title>
	<description><![CDATA[<p>This tutorial includes resources for learning more about PacBio data and bioinformatics analysis, and includes content suitable for both beginners and experts. Below are links to training modules (webinars and PowerPoint presentations) to help you get started with your data processing, as well as information for specialized applications.</p>
<p>Training Resources:</p>
<ul>
<li><a href="https://github.com/PacificBiosciences/Bioinformatics-Training/wiki/Bioinformatics-Workshop">Bioinformatics Workshop (Webinars)</a></li>
<li><a href="https://github.com/PacificBiosciences/Bioinformatics-Training/wiki/Bioinformatics-Training-Slides">Bioinformatics Training Slides</a></li>
</ul>
<p>Specialized Applications:</p>
<ul>
<li><a href="https://github.com/PacificBiosciences/Bioinformatics-Training/wiki/De-Novo-Assembly">De Novo Assembly</a></li>
<li><a href="https://github.com/PacificBiosciences/cDNA_primer/wiki">Transcriptome analysis</a></li>
<li><a href="https://github.com/PacificBiosciences/Bioinformatics-Training/wiki/Base-modification-analysis">Base Modification Analysis</a></li>
<li><a href="https://github.com/PacificBiosciences/Bioinformatics-Training/wiki/Barcoding">Barcoding</a></li>
<li><a href="https://github.com/PacificBiosciences/Bioinformatics-Training/wiki/Data-Analysis-Tools">Data Analysis Tools</a></li>
<li><a href="https://github.com/PacificBiosciences/Bioinformatics-Training/wiki/Minor-Variants-and-Phasing-Analysis">Minor Variants and Phasing Analysis</a></li>
</ul><p>Address of the bookmark: <a href="https://github.com/PacificBiosciences/Bioinformatics-Training/wiki" rel="nofollow">https://github.com/PacificBiosciences/Bioinformatics-Training/wiki</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30973/abacas</guid>
	<pubDate>Thu, 16 Feb 2017 12:15:55 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30973/abacas</link>
	<title><![CDATA[ABACAS]]></title>
	<description><![CDATA[<p><span>ABACAS is intended to rapidly contiguate (align, order, orientate) , visualize and design primers to close gaps on shotgun assembled contigs based on a reference sequence. It uses MUMmer to find alignment positions and identify syntenies of assembly contigs against the reference. The output is then processed to generate a pseudomolecule taking overlaping contigs and gaps in to account. MUMmer's alignment generating programs, Nucmer and Promer are used followed by the 'delta-filter' utility function. Users could also run tblastx on contigs that are not used to generate the pseudomolecule.&nbsp;</span></p><p>Address of the bookmark: <a href="http://abacas.sourceforge.net/Manual.html#9._Colour_code" rel="nofollow">http://abacas.sourceforge.net/Manual.html#9._Colour_code</a></p>]]></description>
	<dc:creator>Surabhi Chaudhary</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31137/finishersc-a-repeat-aware-and-scalable-tool-for-upgrading-de-novo-assembly-using-long-reads</guid>
	<pubDate>Mon, 27 Feb 2017 09:49:45 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31137/finishersc-a-repeat-aware-and-scalable-tool-for-upgrading-de-novo-assembly-using-long-reads</link>
	<title><![CDATA[FinisherSC: a repeat-aware and scalable tool for upgrading de novo assembly using long reads]]></title>
	<description><![CDATA[<p><span>FinisherSC, a repeat-aware and scalable tool for upgrading&nbsp;</span><em>de novo</em><span>&nbsp;assembly using long reads. Experiments with real data suggest that FinisherSC can provide longer and higher quality contigs than existing tools while maintaining high concordance.</span></p><p>Address of the bookmark: <a href="http://kakitone.github.io/finishingTool/" rel="nofollow">http://kakitone.github.io/finishingTool/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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