<?xml version='1.0'?><rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:georss="http://www.georss.org/georss" xmlns:atom="http://www.w3.org/2005/Atom" >
<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/36456?offset=260</link>
	<atom:link href="https://bioinformaticsonline.com/related/36456?offset=260" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/5388/biggest-human-brain-project-hbp-launched</guid>
	<pubDate>Mon, 07 Oct 2013 19:50:55 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/5388/biggest-human-brain-project-hbp-launched</link>
	<title><![CDATA[Biggest Human Brain Project (HBP) launched!!!]]></title>
	<description><![CDATA[<p><img src="http://s1.ibtimes.com/sites/www.ibtimes.com/files/styles/v2_article_large/public/2013/10/07/human-brain-project.jpg" width="500" height="500" alt="image" style="border: 0px;"></p><p>"In neuroscience, the project will use neuroinformatics and brain simulation to collect and integrate experimental data, identifying and filling gaps in our knowledge, and prioritising future experiments.</p><p>In medicine, the HBP will use medical informatics to identify biological signatures of brain disease, allowing diagnosis at an early stage, before the disease has done irreversible damage, and enabling personalized treatment, adapted to the needs of individual patients. Better diagnosis, combined with disease and drug simulation, will accelerate the discovery of new treatments, drastically lowering the cost of drug discovery.<br /><br />In computing, new techniques of interactive supercomputing, driven by the needs of brain simulation, will impact a vast range of industries. Devices and systems, modelled after the brain, will overcome fundamental limits on the energy-efficiency, reliability and programmability of current technologies, clearing the road for systems with brain-like intelligence."</p><p>Source:&nbsp;<a href="http://www.forbes.com/sites/jenniferhicks/2013/10/07/the-human-brain-project-begins/">http://www.forbes.com/sites/jenniferhicks/2013/10/07/the-human-brain-project-begins/</a>&nbsp;</p><p>(<a href="https://www.facebook.com/humanbrainproj/info">https://www.facebook.com/humanbrainproj/info</a>)</p><p>Home Page:</p><p><a href="https://www.humanbrainproject.eu/">https://www.humanbrainproject.eu/</a></p><p>Jobs:</p><p><a href="https://www.humanbrainproject.eu/participate/jobs">https://www.humanbrainproject.eu/participate/jobs</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/9586/list-of-bioinformatics-companies-and-genomics-service-providers</guid>
	<pubDate>Wed, 02 Apr 2014 06:52:28 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/9586/list-of-bioinformatics-companies-and-genomics-service-providers</link>
	<title><![CDATA[List of bioinformatics companies and genomics service providers]]></title>
	<description><![CDATA[<p>Plz check out link for bioinformatics and genomics companies.&nbsp;</p><p>Address of the bookmark: <a href="http://grouthbio.com/Genome_Software_Service.php" rel="nofollow">http://grouthbio.com/Genome_Software_Service.php</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/11107/the-minerva-research-group-for-bioinformatics</guid>
  <pubDate>Tue, 27 May 2014 15:48:14 -0500</pubDate>
  <link></link>
  <title><![CDATA[The Minerva Research Group for Bioinformatics]]></title>
  <description><![CDATA[
<p>The focus of the bioinformatics group is to use computational approaches to gain an insight into genome evolution in primates.</p>

<p>http://www.eva.mpg.de/genetics/bioinformatics/overview.html?Fsize=0%2C%20%40%2F%27</p>

<p>Kelso Group<br />Department of Evolutionary Genetics<br />Max Planck Institute for Evolutionary Anthropology<br />Deutscher Platz 6<br />04103 Leipzig<br />Germany<br />Phone: +49 341 3550 500</p>

<p>Job: <br />http://www.eva.mpg.de/genetics/bioinformatics/jobs.html?Fsize=0%2C%2B%40</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33976/goldgenomes-online-database</guid>
	<pubDate>Wed, 26 Jul 2017 07:49:29 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33976/goldgenomes-online-database</link>
	<title><![CDATA[GOLD:Genomes Online Database]]></title>
	<description><![CDATA[<p><span>GOLD</span><span>:Genomes Online Database, is a World Wide Web resource for comprehensive access to information regarding genome and metagenome sequencing projects, and their associated metadata, around the world.</span></p>
<p>https://gold.jgi.doe.gov/</p><p>Address of the bookmark: <a href="https://gold.jgi.doe.gov/" rel="nofollow">https://gold.jgi.doe.gov/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34748/airvf-a-filtering-toolbox-for-precise-variant-calling-in-ion-torrent-sequencing</guid>
	<pubDate>Fri, 22 Dec 2017 00:31:06 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34748/airvf-a-filtering-toolbox-for-precise-variant-calling-in-ion-torrent-sequencing</link>
	<title><![CDATA[AIRVF: a filtering toolbox for precise variant calling in Ion Torrent sequencing]]></title>
	<description><![CDATA[<p><span>AIRVF that works on flowgram, raw and mapped reads and called variants to reduce artifact-driven false variant calls. Tests on sequencing data of standard reference material showed up to &sim;98% reduction of false variants when combined to conventional public pipelines and &sim;48% to the in-house commercial solution, with a minimal loss of sensitivity</span></p>
<p><span><span>The program with a detailed manual is available at&nbsp;</span><a href="https://sourceforge.net/projects/airvf/" target="">https://sourceforge.net/projects/airvf/</a></span></p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/airvf/" rel="nofollow">https://sourceforge.net/projects/airvf/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37512/purecn-copy-number-calling-and-snv-classification-using-targeted-short-read-sequencing</guid>
	<pubDate>Thu, 09 Aug 2018 04:09:37 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37512/purecn-copy-number-calling-and-snv-classification-using-targeted-short-read-sequencing</link>
	<title><![CDATA[PureCN: copy number calling and SNV classification using targeted short read sequencing]]></title>
	<description><![CDATA[<p>This package estimates tumor purity, copy number, and loss of heterozygosity (LOH), and classifies single nucleotide variants (SNVs) by somatic status and clonality. PureCN is designed for targeted short read sequencing data, integrates well with standard somatic variant detection and copy number pipelines, and has support for tumor samples without matching normal samples.</p>
<p>Author: Markus Riester [aut, cre], Angad P. Singh [aut]</p>
<p>Maintainer: Markus Riester &lt;markus.riester at novartis.com&gt;</p>
<div id="bioc_citation_outer">
<p>Citation (from within R, enter&nbsp;<code>citation("PureCN")</code>):</p>
<div id="bioc_citation">
<p>Riester M, Singh A, Brannon A, Yu K, Campbell C, Chiang D, Morrissey M (2016). &ldquo;PureCN: Copy number calling and SNV classification using targeted short read sequencing.&rdquo;&nbsp;<em>Source Code for Biology and Medicine</em>,&nbsp;<strong>11</strong>, 13. doi:&nbsp;<a href="http://doi.org/10.1186/s13029-016-0060-z">10.1186/s13029-016-0060-z</a>.</p>
</div>
</div><p>Address of the bookmark: <a href="http://bioconductor.org/packages/release/bioc/html/PureCN.html" rel="nofollow">http://bioconductor.org/packages/release/bioc/html/PureCN.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37749/d2tools-the-toolbox-for-counting-the-frequency-of-k-tuple-from-sequencing-datasets-and-calculate-the-dissimilarity</guid>
	<pubDate>Thu, 20 Sep 2018 08:38:29 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37749/d2tools-the-toolbox-for-counting-the-frequency-of-k-tuple-from-sequencing-datasets-and-calculate-the-dissimilarity</link>
	<title><![CDATA[d2Tools: The toolbox for counting the frequency of k-tuple from sequencing datasets and calculate the dissimilarity]]></title>
	<description><![CDATA[<p><code>d2Tools</code>&nbsp;are the toolbox for counting the frequency of K-tuple from sequencing datasets and then calculating the pairwise dissimilarity matrix between samples with the&nbsp;<strong>d2-style</strong>(d2/d2<code>*</code>/d2S representing d2/d2Star/d2shepp, respectively) measures. Hao, Dai, Eucliean, Mahattan, and Chebyshev distance measures are also included in d2Tools.</p>
<p>Manual at&nbsp;https://code.google.com/archive/p/d2-tools/wikis/d2ToolMannual.wiki</p><p>Address of the bookmark: <a href="https://code.google.com/archive/p/d2-tools/" rel="nofollow">https://code.google.com/archive/p/d2-tools/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38205/sim3c-read-pair-simulation-of-3c-based-sequencing-methodologies-hic-meta3c-dnase-hic</guid>
	<pubDate>Tue, 13 Nov 2018 07:25:38 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38205/sim3c-read-pair-simulation-of-3c-based-sequencing-methodologies-hic-meta3c-dnase-hic</link>
	<title><![CDATA[sim3C: Read-pair simulation of 3C-based sequencing methodologies (HiC, Meta3C, DNase-HiC)]]></title>
	<description><![CDATA[<p><strong>Required python modules</strong></p>
<ul>
<li>biopython</li>
<li>intervaltree</li>
<li>numpy</li>
<li>scipy</li>
<li>tqdm</li>
<li>PyYAML</li>
</ul><p>Address of the bookmark: <a href="https://github.com/cerebis/sim3C" rel="nofollow">https://github.com/cerebis/sim3C</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/bookmarks/view/40251/mosdepth-fast-bamcram-depth-calculation-for-wgs-exome-or-targeted-sequencing</guid>
	<pubDate>Wed, 13 Nov 2019 22:20:19 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40251/mosdepth-fast-bamcram-depth-calculation-for-wgs-exome-or-targeted-sequencing</link>
	<title><![CDATA[mosdepth: fast BAM/CRAM depth calculation for WGS, exome, or targeted sequencing]]></title>
	<description><![CDATA[<p>mosdepth can output:</p>
<p>per-base depth about 2x as fast samtools depth--about 25 minutes of CPU time for a 30X genome.<br>mean per-window depth given a window size--as would be used for CNV calling.<br>the mean per-region given a BED file of regions.<br>a distribution of proportion of bases covered at or above a given threshold for each chromosome and genome-wide.<br>quantized output that merges adjacent bases as long as they fall in the same coverage bins e.g. (10-20)<br>threshold output to indicate how many bases in each region are covered at the given thresholds.<br>A summary of mean depths per chromosome and within specified regions per chromosome.</p><p>Address of the bookmark: <a href="https://github.com/brentp/mosdepth" rel="nofollow">https://github.com/brentp/mosdepth</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

</channel>
</rss>