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<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/31351?offset=1270</link>
	<atom:link href="https://bioinformaticsonline.com/related/31351?offset=1270" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33847/omega2-metagenome-assembly-pipeline</guid>
	<pubDate>Mon, 10 Jul 2017 05:56:07 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33847/omega2-metagenome-assembly-pipeline</link>
	<title><![CDATA[Omega2: metagenome assembly pipeline]]></title>
	<description><![CDATA[<p><span>Omega found overlaps between reads using a prefix/suffix hash table. The overlap graph of reads was simplified by removing transitive edges and trimming short branches. Unitigs were generated based on minimum cost flow analysis of the overlap graph and then merged to contigs and scaffolds using mate-pair information. In comparison with three de Bruijn graph assemblers (SOAPdenovo, IDBA-UD and MetaVelvet), Omega provided comparable overall performance on a HiSeq 100-bp dataset and superior performance on a MiSeq 300-bp dataset. In comparison with Celera on the MiSeq dataset, Omega provided more continuous assemblies overall using a fraction of the computing time of existing overlap-layout-consensus assemblers. This indicates Omega can more efficiently assemble longer Illumina reads, and at deeper coverage, for metagenomic datasets.</span></p><p>Address of the bookmark: <a href="http://omega.omicsbio.org/" rel="nofollow">http://omega.omicsbio.org/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/30364/bioinformatics-contest-2017</guid>
	<pubDate>Fri, 23 Dec 2016 14:03:37 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/30364/bioinformatics-contest-2017</link>
	<title><![CDATA[Bioinformatics Contest 2017!]]></title>
	<description><![CDATA[<p><a href="http://contest.bioinf.me" target="_blank">Bioinformatics Contest 2017</a>! Rosalind is co-organizer.<br /> Compete with thousands of people worldwide on bioinformatics problem solving.<br /> Everything is online. Qualification round starts on <strong>January 23, 2017</strong>. Final is on <span><span>Feb 18</span></span>.</p><p>You will need to solve bioinformatics problems using programming. The goal is to correctly solve as many problems as possible within 24 hours. Some of them will be approximation problems and will have partial grades. All rounds will be held online, submissions will be auto-graded in real time.</p><p>Check more at http://contest.bioinf.me/</p><p>Good luck!</p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34416/miniasm-very-fast-olc-based-de-novo-assembler-for-noisy-long-reads</guid>
	<pubDate>Mon, 27 Nov 2017 07:58:49 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34416/miniasm-very-fast-olc-based-de-novo-assembler-for-noisy-long-reads</link>
	<title><![CDATA[miniasm: very fast OLC-based de novo assembler for noisy long reads]]></title>
	<description><![CDATA[<p>Miniasm is a very fast OLC-based&nbsp;<em>de novo</em>&nbsp;assembler for noisy long reads. It takes all-vs-all read self-mappings (typically by&nbsp;<a href="https://github.com/lh3/minimap">minimap</a>) as input and outputs an assembly graph in the&nbsp;<a href="https://github.com/pmelsted/GFA-spec/blob/master/GFA-spec.md">GFA</a>&nbsp;format. Different from mainstream assemblers, miniasm does not have a consensus step. It simply concatenates pieces of read sequences to generate the final&nbsp;<a href="http://wgs-assembler.sourceforge.net/wiki/index.php/Celera_Assembler_Terminology">unitig</a>&nbsp;sequences. Thus the per-base error rate is similar to the raw input reads.</p>
<p>So far miniasm is in early development stage. It has only been tested on a dozen of PacBio and Oxford Nanopore (ONT) bacterial data sets. Including the mapping step, it takes about 3 minutes to assemble a bacterial genome. Under the default setting, miniasm assembles 9 out of 12 PacBio datasets and 3 out of 4 ONT datasets into a single contig. The 12 PacBio data sets are&nbsp;<a href="https://github.com/PacificBiosciences/DevNet/wiki/E.-coli-Bacterial-Assembly">PacBio E. coli sample</a>,&nbsp;<a href="http://www.ebi.ac.uk/ena/data/view/ERS473430">ERS473430</a>,&nbsp;<a href="http://www.ebi.ac.uk/ena/data/view/ERS544009">ERS544009</a>,&nbsp;<a href="http://www.ebi.ac.uk/ena/data/view/ERS554120">ERS554120</a>,&nbsp;<a href="http://www.ebi.ac.uk/ena/data/view/ERS605484">ERS605484</a>,&nbsp;<a href="http://www.ebi.ac.uk/ena/data/view/ERS617393">ERS617393</a>,&nbsp;<a href="http://www.ebi.ac.uk/ena/data/view/ERS646601">ERS646601</a>,&nbsp;<a href="http://www.ebi.ac.uk/ena/data/view/ERS659581">ERS659581</a>,&nbsp;<a href="http://www.ebi.ac.uk/ena/data/view/ERS670327">ERS670327</a>,&nbsp;<a href="http://www.ebi.ac.uk/ena/data/view/ERS685285">ERS685285</a>,&nbsp;<a href="http://www.ebi.ac.uk/ena/data/view/ERS743109">ERS743109</a>&nbsp;and a&nbsp;<a href="https://github.com/PacificBiosciences/DevNet/wiki/E.-coli-20kb-Size-Selected-Library-with-P6-C4/ce0533c1d2a957488594f0b29da61ffa3e4627e8">deprecated PacBio E. coli data set</a>. ONT data are acquired from the&nbsp;<a href="http://lab.loman.net/2015/09/24/first-sqk-map-006-experiment/">Loman Lab</a>.</p>
<p>For a&nbsp;<em>C. elegans</em>&nbsp;<a href="https://github.com/PacificBiosciences/DevNet/wiki/C.-elegans-data-set">PacBio data set</a>&nbsp;(only 40X are used, not the whole dataset), miniasm finishes the assembly, including reads overlapping, in ~10 minutes with 16 CPUs. The total assembly size is 105Mb; the N50 is 1.94Mb. In comparison, the&nbsp;<a href="https://github.com/PacificBiosciences/Bioinformatics-Training/wiki/HGAP">HGAP3</a>produces a 104Mb assembly with N50 1.61Mb.&nbsp;<a href="http://lh3lh3.users.sourceforge.net/download/ce-miniasm.png">This dotter plot</a>&nbsp;gives a global view of the miniasm assembly (on the X axis) and the HGAP3 assembly (on Y). They are broadly comparable. Of course, the HGAP3 consensus sequences are much more accurate. In addition, on the whole data set (assembled in ~30 min), the miniasm N50 is reduced to 1.79Mb. Miniasm still needs improvements.</p>
<p>Miniasm confirms that at least for high-coverage bacterial genomes, it is possible to generate long contigs from raw PacBio or ONT reads without error correction. It also shows that&nbsp;<a href="https://github.com/lh3/minimap">minimap</a>&nbsp;can be used as a read overlapper, even though it is probably not as sensitive as the more sophisticated overlapers such as&nbsp;<a href="https://github.com/marbl/MHAP">MHAP</a>&nbsp;and&nbsp;<a href="https://github.com/thegenemyers/DALIGNER">DALIGNER</a>. Coupled with long-read error correctors and consensus tools, miniasm may also be useful to produce high-quality assemblies.</p>
<p>Minimap and miniasm are ultrafast tools for (i) mapping and (ii) assembly. Designed for long, noisy reads, they do not have a correction or consensus step, and therefore the resulting assemblies are contiguous (i.e. long) but very noisy (i.e. full of errors)</p>
<p>We start with an all against all comparison:</p>
<div>
<pre><code>minimap -Sw5 -L100 -m0 -t8 reads.fq reads.fq | gzip -1 &gt; reads.paf.gz
</code></pre>
</div>
<p>Then we can assemble</p>
<div>
<pre><code>miniasm -f reads.fq reads.paf.gz &gt; reads.gfa
</code></pre>
</div>
<p>Convert GFA to FASTA:</p>
<div>
<pre><code>awk <span>'/^S/{print "&gt;"$2"\n"$3}'</span> reads.gfa | fold &gt; reads.fa
</code></pre>
</div>
<p>And then count how many contigs:</p>
<div>
<pre><code>grep <span>"&gt;"</span> reads.fa | wc -l</code></pre>
</div>
<p>&nbsp;</p>
<pre><span><span>#</span> Download sample PacBio from the PBcR website</span>
wget -O- http://www.cbcb.umd.edu/software/PBcR/data/selfSampleData.tar.gz <span>|</span> tar zxf -
ln -s selfSampleData/pacbio_filtered.fastq reads.fq
<span><span>#</span> Install minimap and miniasm (requiring gcc and zlib)</span>
git clone https://github.com/lh3/minimap <span>&amp;&amp;</span> (cd minimap <span>&amp;&amp;</span> make)
git clone https://github.com/lh3/miniasm <span>&amp;&amp;</span> (cd miniasm <span>&amp;&amp;</span> make)
<span><span>#</span> Overlap</span>
minimap/minimap -Sw5 -L100 -m0 -t8 reads.fq reads.fq <span>|</span> gzip -1 <span>&gt;</span> reads.paf.gz
<span><span>#</span> Layout</span>
miniasm/miniasm -f reads.fq reads.paf.gz <span>&gt;</span> reads.gfa</pre><p>Address of the bookmark: <a href="https://github.com/lh3/miniasm" rel="nofollow">https://github.com/lh3/miniasm</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30557/speedseq</guid>
	<pubDate>Fri, 20 Jan 2017 06:05:43 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30557/speedseq</link>
	<title><![CDATA[SpeedSeq]]></title>
	<description><![CDATA[<p>A flexible framework for rapid genome analysis and interpretation</p>
<p>C Chiang, R M Layer, G G Faust, M R Lindberg, D B Rose, E P Garrison, G T Marth, A R Quinlan, and I M Hall. SpeedSeq: ultra-fast personal genome analysis and interpretation. Nat Meth (2015). doi:10.1038/nmeth.3505.</p>
<p><a href="http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.3505.html">http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.3505.html</a></p><p>Address of the bookmark: <a href="https://github.com/hall-lab/speedseq" rel="nofollow">https://github.com/hall-lab/speedseq</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34618/mashmap-a-fast-and-approximate-software-for-mapping-long-reads-pacbioont-or-assembly-to-reference-genomes</guid>
	<pubDate>Tue, 12 Dec 2017 17:23:31 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34618/mashmap-a-fast-and-approximate-software-for-mapping-long-reads-pacbioont-or-assembly-to-reference-genomes</link>
	<title><![CDATA[MashMap: a fast and approximate software for mapping long reads (PacBio/ONT) or assembly to reference genome(s)]]></title>
	<description><![CDATA[<p><span>MashMap is a fast and approximate software for mapping long reads (PacBio/ONT) or assembly to reference genome(s). It maps a query sequence against a reference region if and only if its estimated alignment identity is above a specified threshold. It does not compute the alignments explicitly, but rather estimates a&nbsp;</span><em>k</em><span>-mer based&nbsp;</span><a href="https://en.wikipedia.org/wiki/Jaccard_index">Jaccard similarity</a><span>&nbsp;using a combination of&nbsp;</span><a href="http://www.cs.princeton.edu/courses/archive/spr05/cos598E/bib/p76-schleimer.pdf">Winnowing</a><span>&nbsp;and&nbsp;</span><a href="https://en.wikipedia.org/wiki/MinHash">MinHash</a><span>. This is then converted to an estimate of sequence identity using the&nbsp;</span><a href="http://mash.readthedocs.org/">Mash</a><span>&nbsp;distance. An appropriate&nbsp;</span><em>k</em><span>-mer sampling rate is automatically determined given minimum local alignment length and identity thresholds. The efficiency of the algorithm improves as both of these thresholds are increased.</span></p><p>Address of the bookmark: <a href="https://github.com/marbl/MashMap" rel="nofollow">https://github.com/marbl/MashMap</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30680/easybuild</guid>
	<pubDate>Fri, 27 Jan 2017 16:00:43 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30680/easybuild</link>
	<title><![CDATA[EasyBuild]]></title>
	<description><![CDATA[<p><a href="https://github.com/hpcugent/easybuild">EasyBuild</a><span>&nbsp;is a software build and installation framework that allows you to manage (scientific) software on High Performance Computing (HPC) systems in an efficient way.</span><br><span>A full list of supported software packages is available&nbsp;</span><a href="http://easybuild.readthedocs.io/en/latest/version-specific/Supported_software.html">here</a><span>.</span></p><p>Address of the bookmark: <a href="https://hpcugent.github.io/easybuild/" rel="nofollow">https://hpcugent.github.io/easybuild/</a></p>]]></description>
	<dc:creator>Jit</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/news/view/30744/binc-2017</guid>
	<pubDate>Wed, 01 Feb 2017 09:36:22 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/30744/binc-2017</link>
	<title><![CDATA[BINC 2017]]></title>
	<description><![CDATA[<p><span>Pondicherry University,Puducherry,on behalf of Department of Biotechnology, Government of India, conducted the BINC examination in&nbsp;</span><span style="color: blue;">2015 and 2016.&nbsp;</span><span>The objective of this examination is to certify bioinformatics professionals, trained formally as well as self-trained.</span><span style="color: blue;">Registration for BINC 2017 examination will open from January 29,2017 to February 28,2017.</span><span>&nbsp;</span></p><p><span>Pondicherry University, Puducherry has been identified as a nodal agency by the Department of Biotechnology, Govt. of India to coordinate this examination along with nine centres namely, </span></p><p><span>Pune University, Pune; </span></p><p><span>Anna University, Chennai; </span></p><p><span>Bose Institute, Kolkata; </span></p><p><span>Institute of Bioinformatics &amp; Applied Biotechnology, Bangalore; </span></p><p><span>North-Eastern Hill University, Shillong, University of Hyderabad, Hyderabad; </span></p><p><span>University of Kerala, Thiruvananthapuram; </span></p><p><span>Jawaharlal Nehru University, New Delhi and </span></p><p><span>Assam Agricultural University, Guwahati.</span><span style="color: blue;"><strong>&nbsp;</strong></span></p><p><span style="color: blue;"><strong>In the BINC 2015 and 2016 examination, 23 candidates and five candidates were certified respectively.</strong></span><span>&nbsp;DBT has agreed to fund Research fellowships for all the BINC qualified Indian nationals to pursue Ph.D. in Indian Institutes/Universities. </span></p><p><span>Note that the candidate must possess a postgraduate degree(or equivalent) &amp; meet the criteria of the institutes/universities in order to avail research fellowship. </span></p><p><span>In addition, cash prize of Rs. 10,000/- will be awarded to the top 10 BINC qualifiers.</span></p><p><span>More at&nbsp;http://www.pondiuni.edu.in/exams/binc/</span></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/30874/important-journals-blogs-and-forums-for-bioinformaticians</guid>
	<pubDate>Wed, 08 Feb 2017 09:15:31 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/30874/important-journals-blogs-and-forums-for-bioinformaticians</link>
	<title><![CDATA[Important Journals, Blogs and Forums for Bioinformaticians]]></title>
	<description><![CDATA[<p><em>Journals</em>. Most journals have RSS feeds for their current updates.</p><ul>
<li><a href="http://bioinformatics.oxfordjournals.org/rss/" target="_blank">Bioinformatics - RSS feed of current and advance online publications</a></li>
<li><a href="http://genome.cshlp.org/rss/" target="_blank">Genome Research - current &amp; advance</a></li>
<li><a href="http://genomebiology.com/" target="_blank">Genome Biology - editors picks, latest, most viewed, most forwarded</a>. (Hit the RSS icon under each tab).</li>
<li><a href="http://www.plosgenetics.org/static/rssFeeds.action" target="_blank">PLoS Genetics - new articles</a></li>
<li><a href="http://www.ploscompbiol.org/static/rssFeeds.action" target="_blank">PLoS Computational Biology - new articles</a></li>
<li><a href="http://www.nature.com/ng/newsfeeds.html" target="_blank">Nature Genetics - current TOC and AOP</a></li>
<li><a href="http://www.nature.com/nrg/info/newsfeeds.html" target="_blank">Nature Reviews Genetics - current TOC and AOP</a></li>
</ul><ul>
<li><a href="https://academic.oup.com/bioinformatics" target="_blank">Bioinformatics</a></li>
<li><a href="https://bmcbioinformatics.biomedcentral.com/" target="_blank">BMC Bioinformatics</a></li>
<li><a href="https://academic.oup.com/bib" target="_blank">Briefings in Bioinformatics</a></li>
<li><a href="http://genomebiology.biomedcentral.com/" target="_blank">Genome Biology</a></li>
<li><a href="http://genome.cshlp.org/rss/" target="_blank">Genome Research: current and AOP</a></li>
<li><a href="http://microbiomejournal.biomedcentral.com/" target="_blank">Microbiome</a></li>
<li><a href="http://www.nature.com/ng/newsfeeds.html" target="_blank">Nature Genetics, current &amp; AOP</a></li>
<li><a href="http://www.nature.com/nrg/info/newsfeeds.html" target="_blank">Nature Reviews Genetics, current &amp; AOP</a></li>
<li><a href="https://academic.oup.com/nar" target="_blank">Nucleic Acids Research</a></li>
<li><a href="http://journals.plos.org/ploscompbiol/s/help-using-this-site#loc-article-feeds" target="_blank">PLOS Computational Biology</a></li>
<li><a href="http://journals.plos.org/plosgenetics/s/help-using-this-site#loc-article-feeds" target="_blank">PLOS Genetics</a></li>
</ul><p><em>Blogs</em><span>. Some of these blogs are very relevant to bioinfo jobs. Others are more personal interest.</span></p><ul>
<li><a href="http://blog.openhelix.eu/" target="_blank">The OpenHelix Blog</a></li>
<li><a href="http://www.ensembl.info/" target="_blank">Ensembl blog</a></li>
<li><a href="http://wiki.g2.bx.psu.edu/News" target="_blank">Galaxy News</a></li>
<li><a href="http://bcbio.wordpress.com/" target="_blank">Blue Collar Bioinformatics</a></li>
<li><a href="http://www.homolog.us/blogs/" target="_blank">Homologus</a></li>
<li><a href="http://blog.goldenhelix.com/" target="_blank">Golden Helix - our 2 SNPs</a></li>
<li><a href="http://genomicslawreport.com/" target="_blank">Genomics Law Report</a></li>
<li><a href="http://www.r-bloggers.com/" target="_blank">R-bloggers</a>&nbsp;(aggregates feeds from &gt;350 blogs about R)</li>
<li><a href="http://genomesunzipped.org/" target="_blank">Genomes Unzipped</a></li>
<li><a href="http://compgen.blogspot.com/" target="_blank">Jason Moore's Epistasis Blog</a></li>
<li><a href="http://spittoon.23andme.com/" target="_blank">23andMe - the Spitoon</a></li>
</ul><ul>
<li><a href="http://varianceexplained.org/" target="_blank">Variance Explained</a>: David Robinson&rsquo;s blog (Data Scientist at Stack Overflow, works in R and Python).</li>
<li><a href="https://globalbiodefense.com/" target="_blank">Global Biodefense</a>: News on pathogens, outbreaks, and preparedness, with periodic posts on genomics and bioinformatics-related developments and funding opportunities.</li>
<li><a href="https://flxlexblog.wordpress.com/" target="_blank">In between lines of code</a>: Lex Nederbragt&rsquo;s blog on biology, sequencing, bioinformatics, &hellip;</li>
<li><a href="http://simplystatistics.org/" target="_blank">Simply Statistics</a>: A statistics blog by Rafa Irizarry, Roger Peng, and Jeff Leek.</li>
<li><a href="https://liorpachter.wordpress.com/" target="_blank">Bits of DNA</a>: Reviews and commentary on computational biology by Lior Pachter (fair warning: dialogue here can get a bit heated!).</li>
<li><a href="http://bcb.io/articles/" target="_blank">Blue Collar Bioinformatics</a>: articles related tool validation and the open source bioinformatics community.</li>
<li><a href="https://microbiomedigest.com/" target="_blank">Microbiome Digest &ndash; Bik&rsquo;s Picks</a>: A daily digest of scientific microbiome papers, by Elisabeth Bik, Science Editor at uBiome.</li>
<li><a href="http://ivory.idyll.org/blog/" target="_blank">Living in an Ivory Basement</a>: Titus Brown&rsquo;s blog on metagenomics, open science, testing, reproducibility, and programming.</li>
<li><a href="http://enseqlopedia.com/" target="_blank">Enseqlopedia</a>: James Hadfield&rsquo;s blog on all things NGS.</li>
<li><a href="http://www.epistasisblog.org/" target="_blank">Epistasis Blog</a>: Jason Moore&rsquo;s computational biology blog.</li>
<li><a href="https://blog.rstudio.org/" target="_blank">RStudio Blog</a>: announcements about new RStudio functionality, updates about the&nbsp;<a href="http://tidyverse.org/" target="_blank">tidyverse</a>, and more.</li>
<li><a href="http://nextgenseek.com/" target="_blank">nextgenseek.com</a>: Next-Gen Sequencing Blog covering new developments in NGS data &amp; analysis.</li>
<li><a href="http://www.rna-seqblog.com/" target="_blank">RNA-Seq Blog</a>: Transcriptome Research &amp; Industry News.</li>
<li><a href="http://www.theallium.com/" target="_blank">The Allium</a>: We all need a little humor in our lives. Like&nbsp;<em>The Onion</em>, but for science.</li>
</ul><p><em>Forums.</em></p><ul>
<li><a href="http://seqanswers.com/forums/forumdisplay.php?f=18" target="_blank">Seqanswers - bioinformatics forum</a></li>
<li><a href="http://seqanswers.com/forums/forumdisplay.php?f=26" target="_blank">Seqanswers - RNA-Seq forum</a></li>
<li><a href="http://www.biostars.org/rss/" target="_blank">BioStar</a></li>
<li><a href="http://bioinformaticsonline.com/">BOL</a></li>
</ul>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36867/cerulean-a-hybrid-assembly-using-high-throughput-short-and-long-reads</guid>
	<pubDate>Tue, 05 Jun 2018 10:10:15 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36867/cerulean-a-hybrid-assembly-using-high-throughput-short-and-long-reads</link>
	<title><![CDATA[Cerulean: A hybrid assembly using high throughput short and long reads]]></title>
	<description><![CDATA[Cerulean extends contigs assembled using short read datasets like Illumina paired-end reads using long reads like PacBio RS long reads.

Cerulean v0.1 has been implemented with bacterial genomes in mind.

The method is fully described in Deshpande, V., Fung, E. D., Pham, S., &amp; Bafna, V. (2013). Cerulean: A hybrid assembly using high throughput short and long reads. arXiv preprint arXiv:1307.7933.
http://arxiv.org/abs/1307.7933<p>Address of the bookmark: <a href="https://sourceforge.net/projects/ceruleanassembler/" rel="nofollow">https://sourceforge.net/projects/ceruleanassembler/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>

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