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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/36833?offset=110</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/4100/should-you-get-sequenced-not-all-bad-genes-predict-disease</guid>
	<pubDate>Thu, 29 Aug 2013 15:10:53 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/4100/should-you-get-sequenced-not-all-bad-genes-predict-disease</link>
	<title><![CDATA[Should you get sequenced? Not all bad genes predict disease]]></title>
	<description><![CDATA[<p><span>&ldquo;What we really don&rsquo;t know yet is whether the predictive aspects of the genome are going to turn out to be beneficial or potentially harmful&rdquo;</span></p>
<p><span><span>&ldquo;As we roll out genomic medicine we are fighting against this society-wide misconception that having the bad gene means you&rsquo;re going to get the disease. That&rsquo;s only true in a very few cases.&rdquo;</span></span></p>
<p><span><span><strong>Source</strong>:Today Health</span></span></p><p>Address of the bookmark: <a href="http://www.today.com/health/should-you-get-sequenced-not-all-bad-genes-predict-disease-8C11017154" rel="nofollow">http://www.today.com/health/should-you-get-sequenced-not-all-bad-genes-predict-disease-8C11017154</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/2726/comparison-of-short-read-de-novo-alignment-algorithms</guid>
	<pubDate>Wed, 21 Aug 2013 07:56:01 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/2726/comparison-of-short-read-de-novo-alignment-algorithms</link>
	<title><![CDATA[Comparison of Short Read De Novo Alignment Algorithms]]></title>
	<description><![CDATA[<p>Excellent article to introduce different sequencing methods along with tools for de novo assembly of sequencing reads and their relevant references.</p>
<p>Title:&nbsp;<strong>Comparison of Short Read De Novo Alignment Algorithms&nbsp;</strong></p>
<p>Author<strong>: Nikhil Gopal</strong></p><p>Address of the bookmark: <a href="http://biochem218.stanford.edu/Projects%202011/Gopal%202011.pdf" rel="nofollow">http://biochem218.stanford.edu/Projects%202011/Gopal%202011.pdf</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/6130/rna-bioinformatics-and-high-throughput-analysis-jena</guid>
  <pubDate>Sat, 09 Nov 2013 20:03:56 -0600</pubDate>
  <link></link>
  <title><![CDATA[RNA Bioinformatics and High Throughput Analysis Jena]]></title>
  <description><![CDATA[
<p>Research Topics:</p>

<p>High Throughput Sequencing Analysis<br />Comparative Genomics<br />Identification and Annotation of Non-coding RNAs<br />Bioinformatic Analysis and System Biology of Viruses<br />Coevolution of Proteins and RNAs<br />Algorithmic Bioinformatics<br />Phylogenetic Analysis</p>

<p>http://www.rna.uni-jena.de/index.php</p>
]]></description>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/10093/bio-rad-acquires-gnubio</guid>
	<pubDate>Sat, 19 Apr 2014 10:36:36 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/10093/bio-rad-acquires-gnubio</link>
	<title><![CDATA[Bio-Rad Acquires GnuBIO]]></title>
	<description><![CDATA[<p>http://www.businesswire.com/news/home/20140411005331/en/Bio-Rad-Acquires-GnuBIO-Developer-Droplet-Based-DNA-Sequencing#.U1KXnPm1b8o</p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/10246/deadly-human-pathogen-cryptococcus-sequenced</guid>
	<pubDate>Fri, 25 Apr 2014 11:02:21 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/10246/deadly-human-pathogen-cryptococcus-sequenced</link>
	<title><![CDATA[Deadly Human Pathogen Cryptococcus  Sequenced]]></title>
	<description><![CDATA[<p><span>"Now, researchers have sequenced the entire genome and all the RNA products of the most important pathogenic lineage of Cryptococcus neoformans, a strain called H99. The results, which appear in&nbsp;</span><em>PLOS Genetics</em><span>, also describe a number of genetic changes that can occur after laboratory handling of H99 that make it more susceptible to stress, hamper its ability to sexually reproduce and render it less virulent."</span></p><p><span><strong>Source</strong>:</span></p><p><span>http://www.biosciencetechnology.com/news/2014/04/deadly-human-pathogen-cryptococcus-fully-sequenced</span></p><p><span><strong>Paper</strong>:</span></p><p><span>http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1004292</span></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/11249/how-to-sequence-the-human-genome-mark-j-kiel</guid>
	<pubDate>Fri, 30 May 2014 13:24:11 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/11249/how-to-sequence-the-human-genome-mark-j-kiel</link>
	<title><![CDATA[How to sequence the human genome - Mark J. Kiel]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/MvuYATh7Y74" frameborder="0" allowfullscreen></iframe>View full lesson: http://ed.ted.com/lessons/how-to-sequence-the-human-genome-mark-j-kiel

Your genome, every human's genome, consists of a unique DNA sequence of A's, T's, C's and G's that tell your cells how to operate. Thanks to technological advances, scientists are now able to know the sequence of letters that makes up an individual genome relatively quickly and inexpensively. Mark J. Kiel takes an in-depth look at the science behind the sequence.

Lesson by Mark J. Kiel, animation by Marc Christoforidis.]]></description>
	
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/file/view/13415/genomics-and-sequencing-approach-for-identification-of-biomarkers-to-assess-the-efficacy-of-tgf-%CE%B2ri-inhibitors-of-liver-cancer-in-vivo</guid>
	<pubDate>Tue, 05 Aug 2014 13:55:32 -0500</pubDate>
	<link>https://bioinformaticsonline.com/file/view/13415/genomics-and-sequencing-approach-for-identification-of-biomarkers-to-assess-the-efficacy-of-tgf-%CE%B2ri-inhibitors-of-liver-cancer-in-vivo</link>
	<title><![CDATA[Genomics and sequencing approach for identification of biomarkers to assess the efficacy of TGF-βRI inhibitors (of liver cancer) in vivo]]></title>
	<description><![CDATA[<p>Liver cancer is third leading cause of deaths and fourth most frequent occuring cancer worldwide. There are multiple signaling pathways responsible for causing cancer amongst which TGFb is most important cytokine whose signaling pathway promote cancer. However, main problem is to cure this cancer at late stage where we still have no treatment strategy to tackle this deadly cancer. &nbsp;Hence we need to find out new therapeutic target. One way is to look the relationships between mRNA, methylation and miRNA data of patients with different pathological conditions (cancer vs control either with inhibitor/not). MiRNA is small RNA molecules known to inhibit mRNA expression of particular gene by binding improperly to 3'UTR region of a gene and hence block binding of TF /translation of gene. CpG regions is known to located at promoter region of gene (5' UTR) and usually hypomethylated which allow to gene to transcribe and translate however sometime this region become hyper-methylated thats prevent expression of host gene. Thus , integration of these three data reveal new targets and pathways important for causing or preventing cancer and also reveal biomarker thats check the effects of inhibitor on signaling pathway underlying liver cancer.</p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/23174/scaffolding-of-a-bacterial-genome-using-minion-nanopore-sequencing</guid>
	<pubDate>Tue, 07 Jul 2015 16:59:25 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/23174/scaffolding-of-a-bacterial-genome-using-minion-nanopore-sequencing</link>
	<title><![CDATA[Scaffolding of a bacterial genome using MinION nanopore sequencing]]></title>
	<description><![CDATA[<p><span>Second generation sequencing has revolutionized genomic studies. However, most genomes contain repeated DNA elements that are longer than the read lengths achievable with typical sequencers, so the genomic order of several generated contigs cannot be easily resolved. A new generation of sequencers offering substantially longer reads is emerging, notably the Pacific Biosciences (PacBio) RS II system and the MinION system, released in early 2014 by Oxford Nanopore Technologies through an early access program.</span></p><p>Address of the bookmark: <a href="http://www.nature.com/srep/2015/150707/srep11996/full/srep11996.html" rel="nofollow">http://www.nature.com/srep/2015/150707/srep11996/full/srep11996.html</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32862/gam-ngs-genomic-assemblies-merger-for-next-generation-sequencing</guid>
	<pubDate>Fri, 19 May 2017 07:44:14 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32862/gam-ngs-genomic-assemblies-merger-for-next-generation-sequencing</link>
	<title><![CDATA[GAM-NGS: genomic assemblies merger for next generation sequencing]]></title>
	<description><![CDATA[<p><span>GAM-NGS is a tool able to merge two or more assemblies in order to improve contiguity and correctness. It can be used on all NGS-based assembly projects and it shows its full potential with multi-library Illumina-based projects. With more than 20 available assemblers it is hard to select the best tool. In this context we propose a tool that improves assemblies (and, as a by-product, perhaps even assemblers) by merging them and selecting the generating that is most likely to be correct.</span></p><p>Address of the bookmark: <a href="https://github.com/vice87/gam-ngs" rel="nofollow">https://github.com/vice87/gam-ngs</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34470/simngs-and-simlibrary-%E2%80%93-software-for-simulating-next-gen-sequencing-data</guid>
	<pubDate>Tue, 28 Nov 2017 06:49:11 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34470/simngs-and-simlibrary-%E2%80%93-software-for-simulating-next-gen-sequencing-data</link>
	<title><![CDATA[simNGS and simLibrary – Software for Simulating Next-Gen Sequencing Data]]></title>
	<description><![CDATA[<p>simNGS is software for simulating observations from Illumina sequencing machines using the statistical models behind the AYB base-calling software. By default, observations only incorporate noise due to sequencing and do not incorporate effects from more esoteric sources of noise that may be present in real data ("dust", bubbles, merged clusters, sequence-heterogeneous clusters, etc). Many of these additional sources may optionally applied.</p>
<p>simNGS takes fasta format sequences and a file describing the covariance of noise between bases and cycles observed in an actual run of the machine, randomly generates noisy intensities representing the signals for the sequence at each cycle and calculates likelihoods for all possible base calls.</p><p>Address of the bookmark: <a href="https://www.ebi.ac.uk/goldman-srv/simNGS/" rel="nofollow">https://www.ebi.ac.uk/goldman-srv/simNGS/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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