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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/36533?offset=730</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/29614/art-set-of-simulation-tools</guid>
	<pubDate>Thu, 03 Nov 2016 08:28:25 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29614/art-set-of-simulation-tools</link>
	<title><![CDATA[ART: Set of Simulation Tools]]></title>
	<description><![CDATA[<p>ART is a set of simulation tools to generate synthetic next-generation sequencing reads. ART simulates sequencing reads by mimicking real sequencing process with empirical error models or quality profiles summarized from large recalibrated sequencing data. ART can also simulate reads using user own read error model or quality profiles. ART supports simulation of single-end, paired-end/mate-pair reads of three major commercial next-generation sequencing platforms: Illumina's Solexa, Roche's 454 and Applied Biosystems' SOLiD. ART can be used to test or benchmark a variety of method or tools for next-generation sequencing data analysis, including read alignment, de novo assembly, SNP and structure variation discovery. ART was used as a primary tool for the simulation study of the <span><a href="http://www.1000genomes.org/" target="_blank">1000 Genomes Project<span></span></a></span> . ART is implemented in C++ with optimized algorithms and is highly efficient in read simulation. ART outputs reads in the FASTQ format, and alignments in the ALN format. ART can also generate alignments in the SAM alignment or UCSC BED file format. ART can be used together with genome variants simulators (e.g. <span><a href="http://bioinform.github.io/varsim/" target="_blank">VarSim<span></span></a></span>) for evaluating variant calling tools or methods.</p><p>Address of the bookmark: <a href="http://www.niehs.nih.gov/research/resources/software/biostatistics/art/" rel="nofollow">http://www.niehs.nih.gov/research/resources/software/biostatistics/art/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32129/lordec-a-hybrid-error-correction-program-for-long-pacbio-reads</guid>
	<pubDate>Mon, 10 Apr 2017 04:16:09 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32129/lordec-a-hybrid-error-correction-program-for-long-pacbio-reads</link>
	<title><![CDATA[LoRDEC: a hybrid error correction program for long, PacBio reads]]></title>
	<description><![CDATA[<p>LoRDEC is a program to correct sequencing errors in long reads from 3rd generation sequencing with high error rate, and is especially intended for PacBio reads. It uses a hybrid strategy, meaning that it uses two sets of reads: the reference read set, whose error rate is assumed to be small, and the PacBio read set, which is then corrected using the reference set. Typically, the reference set contains Illumina reads.</p>
<p><br> Usually, errors in PacBio reads include many insertions and deletions, and comparatively less substitutions. LoRDEC can correct errors of all these types.<br> After correction, a larger portion of the sequence of PacBio reads is usable for detection of region of similarity with other sequences, for aligning them to the contigs of an assembly, etc.</p>
<p>Why is LoRDEC different?</p>
<ul>
<li>It is efficient and can process large read data sets, included from eukaryotic or vertebrate species, on a usual computing server, and even works on desktop/laptop computers.</li>
<li>It adopts a novel graph based approach: it builds a succinct De Bruijn Graph (DBG) representing the short reads, and seeks a corrective sequence for each erroneous region of a long read by traversing chosen paths in the graph.</li>
</ul><p>Address of the bookmark: <a href="http://www.atgc-montpellier.fr/lordec/" rel="nofollow">http://www.atgc-montpellier.fr/lordec/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38563/hecil-a-hybrid-error-correction-algorithm-for-long-reads-with-iterative-learning</guid>
	<pubDate>Tue, 01 Jan 2019 12:01:00 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38563/hecil-a-hybrid-error-correction-algorithm-for-long-reads-with-iterative-learning</link>
	<title><![CDATA[HECIL: A Hybrid Error Correction Algorithm for Long Reads with Iterative Learning]]></title>
	<description><![CDATA[<p><span>HECIL&mdash;Hybrid Error Correction with Iterative Learning&mdash;a hybrid error correction framework that determines a correction policy for erroneous long reads, based on optimal combinations of decision weights obtained from short read alignments.&nbsp;</span></p>
<p><span><span>HECIL&rsquo;s core algorithm by introducing an iterative learning paradigm that enhances the correction policy at each iteration by incorporating knowledge gathered from previous iterations via data-driven confidence metrics assigned to prior corrections.</span></span></p><p>Address of the bookmark: <a href="https://github.com/NDBL/HECIL" rel="nofollow">https://github.com/NDBL/HECIL</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44593/bear-better-emulation-for-artificial-reads</guid>
	<pubDate>Sat, 06 Jul 2024 04:27:53 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44593/bear-better-emulation-for-artificial-reads</link>
	<title><![CDATA[BEAR: Better Emulation for Artificial Reads]]></title>
	<description><![CDATA[<p dir="auto">Created by Stephen Johnson, Brett Trost, Dr. Jeffrey R. Long, Dr. Anthony Kusalik University of Saskatchewan, Department of Computer Science</p>
<p dir="auto">BEAR is intended to be an easy-to-use collection of scripts for generating simulated WGS metagenomic reads with read lengths, quality scores, error profiles, and species abundances derived from real user-supplied WGS data.</p><p>Address of the bookmark: <a href="https://github.com/sej917/BEAR" rel="nofollow">https://github.com/sej917/BEAR</a></p>]]></description>
	<dc:creator>BioStar</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/view/2044</guid>
	<pubDate>Mon, 12 Aug 2013 12:19:29 -0500</pubDate>
	<link>https://bioinformaticsonline.com/view/2044</link>
	<title><![CDATA[Does anyone have Nanopore latest updates?]]></title>
	<description><![CDATA[<p>There was a lot of buzz about&nbsp;<span>Oxford Nanopore Technologies&reg; is developing the GridION&trade; system and miniaturised MinION&trade; device. These are a new generation of electronic molecular analysis system for use in scientific research, personalised medicine, crop science, security/defence and more. The platform technology uses nanopores to analyse single molecules including DNA/RNA and proteins. With a broad patent portfolio, the Oxford Nanopore pipeline includes biological nanopores and solid-state nanopores.</span></p><p>Is this available, or still under trial mode?&nbsp;</p><p><a href="https://www.nanoporetech.com/">https://www.nanoporetech.com/</a></p><p><a href="https://www.nanoporetech.com/technology/the-minion-device-a-miniaturised-sensing-system/the-minion-device-a-miniaturised-sensing-system">https://www.nanoporetech.com/technology/the-minion-device-a-miniaturised-sensing-system/the-minion-device-a-miniaturised-sensing-system</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/4208/latest-paper-on-comparison-of-mapping-tools</guid>
	<pubDate>Tue, 03 Sep 2013 18:00:38 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/4208/latest-paper-on-comparison-of-mapping-tools</link>
	<title><![CDATA[Latest paper on comparison of mapping tools]]></title>
	<description><![CDATA[<p>A. Hatem, D. Bozdag, A. E. Toland, U. V. Catalyurek "Benchmarking short sequence mapping tools" BMC Bioinformatics, 14(1):184, 2013.</p>
<p>http://bmi.osu.edu/hpc/software/benchmark/</p>
<p><a href="http://bmi.osu.edu/hpc/software/pmap/pmap.html">http://bmi.osu.edu/hpc/software/pmap/pmap.html</a></p>
<p>Other similiar papers:</p>
<p><a href="http://online.liebertpub.com/doi/pdf/10.1089/cmb.2012.0022">http://online.liebertpub.com/doi/pdf/10.1089/cmb.2012.0022</a></p>
<p><a href="http://bioinformatics.oxfordjournals.org/content/28/24/3169">http://bioinformatics.oxfordjournals.org/content/28/24/3169</a></p>
<p>Some new Mapping tool links:<a href="http://bmi.osu.edu/hpc/software/benchmark/"></a></p>
<p><strong>GSNAP</strong></p>
<p><a href="http://research-pub.gene.com/gmap/"></a><a href="http://research-pub.gene.com/gmap/">http://research-pub.gene.com/gmap/</a></p>
<p><strong>RMAP</strong></p>
<p><a href="http://rulai.cshl.edu/rmap/"></a><a href="http://rulai.cshl.edu/rmap/">http://rulai.cshl.edu/rmap/</a></p>
<p><strong>mrsFAST</strong></p>
<p><a href="http://mrsfast.sourceforge.net/Home"></a><a href="http://mrsfast.sourceforge.net/Home">http://mrsfast.sourceforge.net/Home</a></p>
<p><a href="http://sourceforge.net/projects/mrsfast/files/mrsfast-ultra-3.1.0/">http://sourceforge.net/projects/mrsfast/files/mrsfast-ultra-3.1.0/</a></p>
<p><strong>BFAST</strong></p>
<p><a href="http://sourceforge.net/apps/mediawiki/bfast/index.php?title=Main_Page">http://sourceforge.net/apps/mediawiki/bfast/index.php?title=Main_Page</a></p>
<p><strong>SHRiMP (for&nbsp;AB SOLiD color-space reads)</strong></p>
<p><a href="http://compbio.cs.toronto.edu/shrimp/">http://compbio.cs.toronto.edu/shrimp/</a></p>
<p><strong>RazerA 3</strong></p>
<p><a href="http://www.seqan.de/projects/razers/">http://www.seqan.de/projects/razers/</a></p><p>Address of the bookmark: <a href="http://www.biomedcentral.com/1471-2105/14/184" rel="nofollow">http://www.biomedcentral.com/1471-2105/14/184</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/9400/largest-genome-sequenced</guid>
	<pubDate>Fri, 21 Mar 2014 13:57:19 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/9400/largest-genome-sequenced</link>
	<title><![CDATA[Largest Genome Sequenced]]></title>
	<description><![CDATA[<p>The enormous size of the <strong>loblolly pine genome</strong> having <strong>22 billion base pairs</strong> compared to only 3 billion in the human genome. In other words, it is&nbsp;<strong>seven times</strong> larger than a human&rsquo;s and also the largest and the most complete&nbsp;<strong>conifer<a href="http://en.wikipedia.org/wiki/Pinophyta" target="_blank"></a></strong>&nbsp;genome ever sequenced.</p>
<p><strong>Related Paper:</strong></p>
<p>http://genomebiology.com/2014/15/3/R59/abstract</p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="http://www.news.ucdavis.edu/search/news_detail.lasso?id=10859" rel="nofollow">http://www.news.ucdavis.edu/search/news_detail.lasso?id=10859</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/10243/new-rna-seq-tool</guid>
	<pubDate>Fri, 25 Apr 2014 10:59:04 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/10243/new-rna-seq-tool</link>
	<title><![CDATA[New RNA Seq tool]]></title>
	<description><![CDATA[<p>"<span>By removing the time-consuming step of read mapping, the authors reported, Sailfish able to provide quantification estimates 20&ndash;30 times faster than current methods without loss of accuracy."</span></p>
<p><span>Tool link:</span></p>
<p><span>http://www.cs.cmu.edu/~ckingsf/software/sailfish/</span></p>
<p><span></span></p><p>Address of the bookmark: <a href="http://www.genengnews.com/gen-news-highlights/lightweight-algorithms-sail-through-rna-sequencing-data/81249765/" rel="nofollow">http://www.genengnews.com/gen-news-highlights/lightweight-algorithms-sail-through-rna-sequencing-data/81249765/</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>

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