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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/4590?offset=520</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27440/stampy</guid>
	<pubDate>Fri, 20 May 2016 19:13:32 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27440/stampy</link>
	<title><![CDATA[Stampy]]></title>
	<description><![CDATA[<p><strong>Stampy&nbsp;</strong><span>is a package for the mapping of short reads from illumina sequencing machines onto a reference genome. It's recommended for most workflows, including those for genomic resequencing, RNA-Seq and Chip-seq. Stampy excels in the mapping of reads containing that contain sequence variation relative to the reference, in particular for those containing insertions or deletions.</span></p><p>Address of the bookmark: <a href="http://www.well.ox.ac.uk/project-stampy" rel="nofollow">http://www.well.ox.ac.uk/project-stampy</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37545/ncbi-magic-blast</guid>
	<pubDate>Tue, 14 Aug 2018 18:11:11 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37545/ncbi-magic-blast</link>
	<title><![CDATA[NCBI Magic-BLAST]]></title>
	<description><![CDATA[<p>Magic-BLAST is a tool for mapping large next-generation RNA or DNA sequencing runs against a whole genome or transcriptome. Each alignment optimizes a composite score, taking into account simultaneously the two reads of a pair, and in case of RNA-seq, locating the candidate introns and adding up the score of all exons. This is very different from other versions of BLAST, where each exon is scored as a separate hit and read-pairing is ignored.</p>
<p>Magic-BLAST incorporates within the NCBI BLAST code framework ideas developed in the NCBI Magic pipeline, in particular hit extensions by local walk and jump&nbsp;<a href="http://www.ncbi.nlm.nih.gov/pubmed/26109056">(http://www.ncbi.nlm.nih.gov/pubmed/26109056)</a>, and recursive clipping of mismatches near the edges of the reads, which avoids accumulating artefactual mismatches near splice sites and is needed to distinguish short indels from substitutions near the edges.</p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="https://ncbi.github.io/magicblast/" rel="nofollow">https://ncbi.github.io/magicblast/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/933/world-of-omics</guid>
	<pubDate>Tue, 16 Jul 2013 17:11:48 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/933/world-of-omics</link>
	<title><![CDATA[World of Omics]]></title>
	<description><![CDATA[<p>How many variants of "omics" techniques presently in use ?</p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
<item>
	<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>
</item>
<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/news/view/11365/drawback-of-exome-sequencing</guid>
	<pubDate>Mon, 02 Jun 2014 05:46:43 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/11365/drawback-of-exome-sequencing</link>
	<title><![CDATA[Drawback of Exome Sequencing]]></title>
	<description><![CDATA[<p><span><span>Dr Eric Londin, Assistant Professor, Thomas Jefferson University, USA, stated that analysis of 44 exome datasets from four different testing kits showed that they missed a high proportion of clinically relevant regions in the 56 ACMG genes. "At least one gene in each exome method was missing more than 40 percent of disease-causing genetic variants, and we found that the worst-performing method missed more than 90 percent of such variants in four of the 56 genes," he says.</span><br /></span></p><p><span><strong>Source</strong>:&nbsp;http://www.eurekalert.org/pub_releases/2014-05/esoh-pco052914.php</span></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/17843/pathway-analysis</guid>
	<pubDate>Fri, 03 Oct 2014 08:51:13 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/17843/pathway-analysis</link>
	<title><![CDATA[Pathway Analysis]]></title>
	<description><![CDATA[<p>Pathway Analysis is usually performed with aim to enrich the genes with their functional information and reveal the underlying biological mechanisms pursue by genes. Pathway Analysis is not only limited to what biological pathways a particular set of expressed genes follow but also to disclose the relationships between these genes. With availability of more genomics, transcriptomics and proteomics data, interactions between genes involve in multiple pathways become more clear and also relationships between the genes, their transcripts, and their gene products. However, existing tools and dbs mainly based on knowledge driven approach in which pathways will be identified by finding the correlation between the&nbsp;<span>information in one of the pathway knowledge databases (KEGG,Reactome,Panther,BioCarta, Panther,GO,NCI,WikiPathways,etc) and gene expression result for a specific conditions for instance tumor, obesity , cold resistant crops/plants, etc.</span></p><p><span><strong>Introductory Articles/ppt/sources</strong>:</span></p><p><a href="http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002375"><span>http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002375</span></a></p><p><a href="http://bioinformatics.mdanderson.org/MicroarrayCourse/Lectures09/Pathway%20Analysis.pdf"><span>http://bioinformatics.mdanderson.org/MicroarrayCourse/Lectures09/Pathway%20Analysis.pdf</span></a></p><p><a href="http://gettinggeneticsdone.blogspot.de/2012/03/pathway-analysis-for-high-throughput.html"><span>http://gettinggeneticsdone.blogspot.de/2012/03/pathway-analysis-for-high-throughput.html</span></a></p><p><a href="http://davetang.org/muse/tag/pathway/"><span>http://davetang.org/muse/tag/pathway/</span></a></p><p><a href="https://www.biostars.org/p/42219/"><span>https://www.biostars.org/p/42219/</span></a></p><p><a href="http://bioinformatics.ca//files/public/Pathways_2014_Module4_v2.pdf"><span>http://bioinformatics.ca//files/public/Pathways_2014_Module4_v2.pdf</span></a></p><p><a href="http://bioinformatics.ca//files/public/Pathways_2014_Module2.pdf"><span>http://bioinformatics.ca//files/public/Pathways_2014_Module2.pdf</span></a></p><p><span><strong>Impotant Database and Tools</strong>:</span></p><p>GeneMANIA, Cytoscape,&nbsp;<a href="http://www.ingenuity.com/products/ipa">IPA</a>&nbsp;and <a href="http://thomsonreuters.com/metacore/">Metacore</a> (Commerical ),&nbsp;<span>Pathway Commons, Reactome ,Panther, BioCyc, WikiPathways, Pathvisio, KEGG, NCI, Stringdb, Amigo,&nbsp;<span>WebGestalt ,<span>ConsensusPathDB ,GSEA,Blast2go</span></span></span></p><p><span><strong>Popular R based tools</strong>:</span></p><p><span>Reactome.db, ReactomePA, ClusterProfiler, Gage, SPIA, topGO, Pathview,DOSE,GOStat</span></p><p><span><strong>More</strong>:</span></p><p><a href="http://www.bioconductor.org/help/search/index.html?q=Enrichment+analysis+"><span>http://www.bioconductor.org/help/search/index.html?q=Enrichment+analysis+</span></a></p><p>&nbsp;</p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27839/lorma-a-tool-for-correcting-sequencing-errors-in-long-reads-such-those-produced-by-pacific-biosciences-sequencing-machines</guid>
	<pubDate>Wed, 15 Jun 2016 17:18:36 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27839/lorma-a-tool-for-correcting-sequencing-errors-in-long-reads-such-those-produced-by-pacific-biosciences-sequencing-machines</link>
	<title><![CDATA[LoRMA: a tool for correcting sequencing errors in long reads such those produced by Pacific Biosciences sequencing machines]]></title>
	<description><![CDATA[<p>LoRMA is a tool for correcting sequencing errors in long reads such those produced by Pacific Biosciences sequencing machines.</p>
<p>Publication:</p>
<ul>
<li>L. Salmela, R. Walve, E. Rivals, and E. Ukkonen: Accurate selfcorrection of errors in long reads using de Bruijn graphs. Accepted to RECOMB-Seq 2016.</li>
</ul>
<p>Download:</p>
<ul>
<li><a href="https://www.cs.helsinki.fi/u/lmsalmel/LoRMA/LoRMA-0.3.tar.gz">LoRMA 0.3 source files</a></li>
<li><a href="https://www.cs.helsinki.fi/u/lmsalmel/LoRMA/README.txt">README</a></li>
</ul><p>Address of the bookmark: <a href="https://www.cs.helsinki.fi/u/lmsalmel/LoRMA/" rel="nofollow">https://www.cs.helsinki.fi/u/lmsalmel/LoRMA/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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