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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/39213?offset=650</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36806/manta-rapid-detection-of-structural-variants-and-indels-for-germline-and-cancer-sequencing-applications</guid>
	<pubDate>Mon, 28 May 2018 09:41:39 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36806/manta-rapid-detection-of-structural-variants-and-indels-for-germline-and-cancer-sequencing-applications</link>
	<title><![CDATA[Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications.]]></title>
	<description><![CDATA[Manta calls structural variants (SVs) and indels from mapped paired-end sequencing reads. It is optimized for analysis of germline variation in small sets of individuals and somatic variation in tumor/normal sample pairs. Manta discovers, assembles and scores large-scale SVs, medium-sized indels and large insertions within a single efficient workflow.<p>Address of the bookmark: <a href="https://github.com/Illumina/manta" rel="nofollow">https://github.com/Illumina/manta</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37527/nanopack-visualizing-and-processing-long-read-sequencing-data</guid>
	<pubDate>Fri, 10 Aug 2018 18:41:34 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37527/nanopack-visualizing-and-processing-long-read-sequencing-data</link>
	<title><![CDATA[NanoPack: visualizing and processing long-read sequencing data]]></title>
	<description><![CDATA[<p>The NanoPack tools are written in Python3 and released under the GNU GPL3.0 License. The source code can be found at&nbsp;<a href="https://github.com/wdecoster/nanopack" target="">https://github.com/wdecoster/nanopack</a>, together with links to separate scripts and their documentation. The scripts are compatible with Linux, Mac OS and the MS Windows 10 subsystem for Linux and are available as a graphical user interface, a web service at&nbsp;<a href="http://nanoplot.bioinf.be/" target="">http://nanoplot.bioinf.be</a>&nbsp;and command line tools.</p>
<p>&nbsp;https://academic.oup.com/bioinformatics/article/34/15/2666/4934939</p><p>Address of the bookmark: <a href="https://github.com/wdecoster/nanoQC" rel="nofollow">https://github.com/wdecoster/nanoQC</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/38649/ngs-platforms-launched-by-bgi%E2%80%99s-mgi-tech</guid>
	<pubDate>Thu, 10 Jan 2019 04:42:06 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/38649/ngs-platforms-launched-by-bgi%E2%80%99s-mgi-tech</link>
	<title><![CDATA[NGS Platforms launched by BGI’s MGI Tech]]></title>
	<description><![CDATA[<p>MGI Tech Co., Ltd. (MGI), a subsidiary of BGI Group, is committed to enabling effective and affordable healthcare solutions for all. Based on its proprietary technology, MGI produces sequencing devices, equipment, consumables and reagents to support life science research, medicine and healthcare. MGI's multi-omics platforms include genetic sequencing, mass spectrometry and medical imaging. Providing real-time, comprehensive, life-long solutions, its mission&nbsp;is to&nbsp;develop and promote advanced life science tools for future healthcare.</p><p>MGI, a subsidiary of global genomics leader BGI Group, announced pricing and its first early access customer for the new ultra high-throughput sequencer, MGISEQ-T7, saying it has driven down sequencing cost to&nbsp;$5&nbsp;per gigabyte, with exceptionally high accuracy. Such innovations are helping more people to realize the benefits of genomic information.</p><p>In October, MGI launched the MGISEQ-T7, a highly flexible production-scale platform that is the most powerful sequencer to date. It can produce as many as 60 whole human genomes in one day. The instrument sells for&nbsp;$1 million.</p><p>The T7 enables simultaneous but independent operation of up to four flow cells, which means different applications such as single-cell RNA sequencing, whole exome sequencing and whole genome sequencing can be run in different flow cells at the same time. This helps to reduce costs, allowing MGI to offer the most competitive sequencing price in the market.</p><p><span>Powered by DNBseq&trade;, MGISEQ delivers quality data with accuracy for SNP and Indel calling rate of 99.9% and 99%, respectively, along with decreased duplication rate down to less than 2 percent, and almost zero Index mis-assignment rate.</span></p><p><span><span>SOURCE MGI</span></span></p><p>https://www.bgi.com/global/company/news/bgis-mgi-tech-launches-two-new-ngs-platforms/</p><p>http://en.mgitech.cn/</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/41039/phd-position-in-translational-medicine</guid>
  <pubDate>Sat, 15 Feb 2020 06:07:19 -0600</pubDate>
  <link></link>
  <title><![CDATA[PhD position in Translational Medicine]]></title>
  <description><![CDATA[
<p>https://www.jobvector.de/jobs-stellenangebote/biologie-life-sciences/wissenschaftliche-r-mitarbeiter-in/phd-position-translational-medicine-129981.html?suid=1b510358c7578e8f75cf04a464fc21a404a574ca</p>

<p>Essential experience / qualifications:<br />Master / Diploma in Biology, Biochemistry, Molecular Medicine or similar; solid knowledge of molecular and cell biological techniques; good English knowledge</p>

<p>Applications:<br />Please send your application (including CV, letter of motivation, contact information of two references, and list of publication) by 13.03.2020 at the latest to:</p>

<p>Universitätsklinikum Erlangen<br />Chirurgische Klinik<br />Translational Research Center<br />Prof. Dr. rer. nat. Michael Stürzl<br />Schwabachanlage 12<br />91054 Erlangen<br />E-Mail: michael.stuerzl@uk-erlangen.de</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44659/figeno-tool-for-plotting-sequencing-data-along-genomic-coordinates</guid>
	<pubDate>Tue, 17 Sep 2024 02:28:15 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44659/figeno-tool-for-plotting-sequencing-data-along-genomic-coordinates</link>
	<title><![CDATA[Figeno: Tool for plotting sequencing data along genomic coordinates.]]></title>
	<description><![CDATA[<p><span>Tool for plotting sequencing data along genomic coordinates.</span></p>
<div>
<pre><code>FIGENO is a
  FIGure
    GENerator
for GENOmics</code></pre>
</div>
<p dir="auto">With figeno, you can plot various types of sequencing data along genomic coordinates. Video overview:&nbsp;<a href="https://www.youtube.com/watch?v=h1cBeXoSYTA">https://www.youtube.com/watch?v=h1cBeXoSYTA</a>.</p>
<p dir="auto"><a href="https://github.com/CompEpigen/figeno/blob/main/docs/content/images/figeno.png" target="_blank"><img src="https://github.com/CompEpigen/figeno/raw/main/docs/content/images/figeno.png" alt="figeno" style="border: 0px;"></a></p><p>Address of the bookmark: <a href="https://github.com/CompEpigen/figeno" rel="nofollow">https://github.com/CompEpigen/figeno</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33859/disco-multi-threaded-and-multiprocess-distributed-memory-overlap-layout-consensus-olc-metagenome-assembler</guid>
	<pubDate>Mon, 10 Jul 2017 10:09:27 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33859/disco-multi-threaded-and-multiprocess-distributed-memory-overlap-layout-consensus-olc-metagenome-assembler</link>
	<title><![CDATA[DISCO : multi threaded and multiprocess distributed memory overlap-layout-consensus (OLC) metagenome assembler]]></title>
	<description><![CDATA[<p><span>DISCO is a multi threaded and multiprocess distributed memory overlap-layout-consensus (OLC) metagenome assembler. Disco was developed as a&nbsp;scalable assembler to assemble large metagenomes from billions of Illumina sequencing reads of complex microbial communities. Disco was parallelized for computer clusters in a hybrid architecture that integrated shared-memory multi-threading, point-to-point message passing, and remote direct memory access. The assembly and scaffolding were performed using an iterative overlap graph approach.</span></p><p>Address of the bookmark: <a href="http://disco.omicsbio.org/" rel="nofollow">http://disco.omicsbio.org/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34398/ont-assembly-and-illumina-polishing-pipeline</guid>
	<pubDate>Thu, 23 Nov 2017 10:13:42 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34398/ont-assembly-and-illumina-polishing-pipeline</link>
	<title><![CDATA[ONT assembly and Illumina polishing pipeline]]></title>
	<description><![CDATA[<p>This pipeline performs the following steps:</p>
<ul>
<li>Assembly of nanopore reads using&nbsp;<a href="http://canu.readthedocs.io/">Canu</a>.</li>
<li>Polish canu contigs using&nbsp;<a href="https://github.com/isovic/racon">racon</a>&nbsp;(<em>optional</em>).</li>
<li>Map a paired-end Illumina dataset onto the contigs obtained in the previous steps using&nbsp;<a href="http://bio-bwa.sourceforge.net/">BWA</a>&nbsp;mem.</li>
<li>Perform correction of contigs using&nbsp;<a href="https://github.com/broadinstitute/pilon/wiki">pilon</a>&nbsp;and the Illumina dataset.</li>
</ul><p>Address of the bookmark: <a href="https://github.com/nanoporetech/ont-assembly-polish" rel="nofollow">https://github.com/nanoporetech/ont-assembly-polish</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34501/dnapipete-de-novo-assembly-annotation-pipeline-for-transposable-elements</guid>
	<pubDate>Sat, 02 Dec 2017 18:25:44 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34501/dnapipete-de-novo-assembly-annotation-pipeline-for-transposable-elements</link>
	<title><![CDATA[dnaPipeTE: de-novo assembly &amp; annotation Pipeline for Transposable Elements]]></title>
	<description><![CDATA[<p>dnaPipeTE (for de-novo assembly &amp; annotation Pipeline for Transposable Elements), is a pipeline designed to find, annotate and quantify Transposable Elements in small samples of NGS datasets. It is very useful to quantify the proportion of TEs in newly sequenced genomes since it does not require genome assembly and works on small datasets (&lt; 1X).</p>
<ul>
<li>
<p>dnaPipeTE is developped by Cl&eacute;ment Goubert, Laurent Modolo and the TREEP team of the LBBE:&nbsp;<a href="http://lbbe.univ-lyon1.fr/-Equipe-Elements-transposables-.html?lang=en">http://lbbe.univ-lyon1.fr/-Equipe-Elements-transposables-.html?lang=en</a></p>
</li>
<li>
<p>You can find the original publication in GBE here:&nbsp;<a href="https://academic.oup.com/gbe/article/7/4/1192/533768">https://academic.oup.com/gbe/article/7/4/1192/533768</a></p>
</li>
</ul>
<p><a href="https://github.com/clemgoub/dnaPipeTE/blob/dev/dnaPipefront.png" target="_blank"><img src="https://github.com/clemgoub/dnaPipeTE/raw/dev/dnaPipefront.png" alt="Front" style="border: 0px;"></a><em>output examples of quantification and TE landscape (relative age) produced by dnaPipeTE</em></p>
<p><em>&nbsp;</em></p><p>Address of the bookmark: <a href="https://github.com/clemgoub/dnaPipeTE" rel="nofollow">https://github.com/clemgoub/dnaPipeTE</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35345/rgfa-powerful-and-convenient-handling-of-assembly-graphs</guid>
	<pubDate>Thu, 25 Jan 2018 05:47:53 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35345/rgfa-powerful-and-convenient-handling-of-assembly-graphs</link>
	<title><![CDATA[RGFA: powerful and convenient handling of assembly graphs]]></title>
	<description><![CDATA[<p><span>RGFA, an implementation of the proposed GFA specification in Ruby. It allows the user to conveniently parse, edit and write GFA files. Complex operations such as the separation of the implicit instances of repeats and the merging of linear paths can be performed. A typical application of RGFA is the editing of a graph, to finish the assembly of a sequence, using information not available to the assembler. We illustrate a use case, in which the assembly of a repetitive metagenomic fosmid insert was completed using a script based on RGFA.</span></p>
<p><span>https://github.com/ggonnella/rgfa</span></p><p>Address of the bookmark: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5103826/" rel="nofollow">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5103826/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36985/swalo-scaffolding-with-assembly-likelihood-optimization</guid>
	<pubDate>Wed, 20 Jun 2018 02:45:16 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36985/swalo-scaffolding-with-assembly-likelihood-optimization</link>
	<title><![CDATA[SWALO: Scaffolding with assembly likelihood optimization]]></title>
	<description><![CDATA[SWALO (scaffolding with assembly likelihood optimization) is a method for scaffolding based on likelihood of genome assemblies computed using generative models for sequencing.

Please email your questions, comments, suggestions, and bug reports to atif.bd@gmail.com.<p>Address of the bookmark: <a href="https://atifrahman.github.io/SWALO/" rel="nofollow">https://atifrahman.github.io/SWALO/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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