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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/43254?</link>
	<atom:link href="https://bioinformaticsonline.com/related/43254?" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41571/wego-simple-but-useful-tool-for-visualizing-comparing-and-plotting-go-gene-ontology-annotation-results</guid>
	<pubDate>Sun, 12 Apr 2020 10:02:22 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41571/wego-simple-but-useful-tool-for-visualizing-comparing-and-plotting-go-gene-ontology-annotation-results</link>
	<title><![CDATA[WEGO : simple but useful tool for visualizing, comparing and plotting GO (Gene Ontology) annotation results]]></title>
	<description><![CDATA[<p><span>WEGO (Web Gene Ontology Annotation Plot) is a simple but useful tool for visualizing, comparing and plotting GO (Gene Ontology) annotation results. As the GO vocabulary became more and more popular, WEGO was widely adopted and used in many researches. Therefore we have updated WEGO 2.0 in 2018. Here are some changes we&rsquo;ve made:</span><br><span>1. The limit of input file numbers was cancelled. Now the users could upload as many files as they want with one operation.</span><br><span>2. We have added the reference data of 9 species for users selection.</span><br><span>3. Besides the traditional WEGO histogram, WEGO 2.0 outputs an additional type of bar graph showing GO terms with significant gene number differences.</span></p><p>Address of the bookmark: <a href="http://wego.genomics.org.cn/" rel="nofollow">http://wego.genomics.org.cn/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36607/tarean-a-computational-tool-for-identification-and-characterization-of-satellite-dna-from-unassembled-short-reads</guid>
	<pubDate>Tue, 15 May 2018 02:53:11 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36607/tarean-a-computational-tool-for-identification-and-characterization-of-satellite-dna-from-unassembled-short-reads</link>
	<title><![CDATA[TAREAN: A computational tool for identification and characterization of satellite DNA from unassembled short reads]]></title>
	<description><![CDATA[<p><strong>TA</strong>ndem&nbsp;<strong>RE</strong>peat&nbsp;<strong>AN</strong>alyzer -TAREAN &ndash; is a computational pipeline for&nbsp;<strong>unsupervised identification of satellite repeats</strong>&nbsp;from unassembled sequence reads. The pipeline uses low-pass whole genome sequence reads and performs their graph-based clustering. Resulting clusters, representing all types of repeats, are then examined for the presence of circular structures and putative satellite repeats are reported.</p>
<p><em><strong>How to use TAREAN</strong></em>:</p>
<ul>
<li>Install a local instance of the pipeline using its source code available from&nbsp;<a href="https://bitbucket.org/petrnovak/repex_tarean" target="_blank" title="TAREAN source code">bitbucket repository</a>.</li>
<li>Use&nbsp; public Galaxy-based server at&nbsp;<a href="https://repeatexplorer-elixir.cerit-sc.cz/" target="_blank">https://repeatexplorer-elixir.cerit-sc.cz/</a>. The server is provided in frame of the&nbsp;<a href="https://www.elixir-czech.cz/" target="_blank">Elixir CZ project</a>&nbsp;and is maintained by&nbsp;<a href="https://www.cesnet.cz/" target="_blank">CESNET</a>&nbsp;and&nbsp;<a href="https://www.cerit-sc.cz/en/index.html" target="_blank">CERIT-SC</a>. Simple registration is required to use this service.</li>
</ul>
<p>Development of TAREAN was supported by&nbsp;<a href="https://www.elixir-czech.cz/" target="_blank" title="ELIXIR-CZ">ELIXIR CZ</a>&nbsp;research infrastructure project (MEYS Grant No: LM2015047).</p>
<p><strong><em>References</em></strong></p>
<p>Novak, P., Avila Robledillo, L., Koblizkova, A., Vrbova, I., Neumann, P., Macas, J. (2017) &ndash;&nbsp;<a href="https://academic.oup.com/nar/article/3574061/" target="_blank">TAREAN: a computational tool for identification and characterization of satellite DNA from unassembled short reads</a>.&nbsp;<em>Nucleic Acids Res.</em>, doi:10.1093/nar/gkx257</p><p>Address of the bookmark: <a href="https://bitbucket.org/petrnovak/repex_tarean" rel="nofollow">https://bitbucket.org/petrnovak/repex_tarean</a></p>]]></description>
	<dc:creator>Surabhi Chaudhary</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37563/colormap-correcting-long-reads-by-mapping-short-reads</guid>
	<pubDate>Mon, 20 Aug 2018 14:17:05 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37563/colormap-correcting-long-reads-by-mapping-short-reads</link>
	<title><![CDATA[CoLoRMap: Correcting Long Reads by Mapping short reads]]></title>
	<description><![CDATA[<p><span>Second generation sequencing technologies paved the way to an exceptional increase in the number of sequenced genomes, both prokaryotic and eukaryotic. However, short reads are difficult to assemble and often lead to highly fragmented assemblies. The recent developments in long reads sequencing methods offer a promising way to address this issue. However, so far long reads are characterized by a high error rate, and assembling from long reads require a high depth of coverage. This motivates the development of hybrid approaches that leverage the high quality of short reads to correct errors in long reads.We introduce CoLoRMap, a hybrid method for correcting noisy long reads, such as the ones produced by PacBio sequencing technology, using high-quality Illumina paired-end reads mapped onto the long reads. Our algorithm is based on two novel ideas: using a classical shortest path algorithm to find a sequence of overlapping short reads that minimizes the edit score to a long read and extending corrected regions by local assembly of unmapped mates of mapped short reads. Our results on bacterial, fungal and insect data sets show that CoLoRMap compares well with existing hybrid correction methods.The source code of CoLoRMap is freely available for non-commercial use at https://github.com/sfu-compbio/colormap</span></p>
<p><span>ehaghshe@sfu.ca or cedric.chauve@sfu.ca</span></p><p>Address of the bookmark: <a href="https://github.com/sfu-compbio/colormap" rel="nofollow">https://github.com/sfu-compbio/colormap</a></p>]]></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>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37554/finishersca-repeat-aware-tool-for-upgrading-de-novo-assembly-using-long-reads</guid>
	<pubDate>Mon, 20 Aug 2018 04:08:50 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37554/finishersca-repeat-aware-tool-for-upgrading-de-novo-assembly-using-long-reads</link>
	<title><![CDATA[FinisherSC:a repeat-aware tool for upgrading de novo assembly using long reads]]></title>
	<description><![CDATA[<p><br>Here is the command to run the tool:</p>
<pre><code>python finisherSC.py destinedFolder mummerPath
</code></pre>
<p>If you are running on server computer and would like to use multiple threads, then the following commands can generate 20 threads to run FinisherSC.</p>
<pre><code>python finisherSC.py -par 20 destinedFolder mummerPath
</code></pre>
<p>Sometimes, if the names of raw reads and contigs consists of special characters/formats, FinisherSC/MUMmer may not parse them correctly. In that case, you want to have a quick renaming of the names of contigs/reads in contigs.fasta or raw_reads.fasta using the following command.</p>
<pre><code>    perl -pe 's/&gt;[^\$]*$/"&gt;Seg" . ++$n ."\n"/ge' raw_reads.fasta &gt; newRaw_reads.fasta
    cp newRaw_reads.fasta raw_reads.fasta
    perl -pe 's/&gt;[^\$]*$/"&gt;Seg" . ++$n ."\n"/ge' contigs.fasta &gt; newContigs.fasta
    cp newContigs.fasta contigs.fasta</code></pre><p>Address of the bookmark: <a href="https://github.com/kakitone/finishingTool" rel="nofollow">https://github.com/kakitone/finishingTool</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37576/lrcstats-a-tool-for-evaluating-long-reads-correction-methods</guid>
	<pubDate>Wed, 22 Aug 2018 11:05:04 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37576/lrcstats-a-tool-for-evaluating-long-reads-correction-methods</link>
	<title><![CDATA[LRCstats: a tool for evaluating long reads correction methods]]></title>
	<description><![CDATA[<p><span>LRCstats is an open-source pipeline for benchmarking DNA long read correction algorithms for long reads outputted by third generation sequencing technology such as machines produced by Pacific Biosciences. The reads produced by third generation sequencing technology, as the name suggests, are longer in length than reads produced by next generation sequencing technologies, such as those produced by Illumina. However, long reads are plagued by high error rates, which can cause issues in downstream analysis. Long read correction algorithms reduce the error rate of long reads either through self-correcting methods or using accurate, short reads outputted by next generation sequencing technologies to correct long reads.</span></p><p>Address of the bookmark: <a href="https://github.com/cchauve/lrcstats" rel="nofollow">https://github.com/cchauve/lrcstats</a></p>]]></description>
	<dc:creator>Aaryan Lokwani</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38735/genome-assembly-tutorial-genome-assembly-for-short-and-long-reads</guid>
	<pubDate>Sat, 19 Jan 2019 17:29:53 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38735/genome-assembly-tutorial-genome-assembly-for-short-and-long-reads</link>
	<title><![CDATA[Genome assembly tutorial &quot;Genome Assembly for short and long reads&quot;]]></title>
	<description><![CDATA[<p>In this lab we will perform de novo genome assembly of a bacterial genome. You will be guided through the genome assembly starting with data quality control, through to building contigs and analysis of the results. At the end of the lab you will know:</p>
<ol>
<li>How to perform basic quality checks on the input data</li>
<li>How to run a short read assembler on Illumina data</li>
<li>How to run a long read assembler on Pacific Biosciences or Oxford Nanopore data</li>
<li>How to improve the accuracy of a long read assembly using short reads</li>
<li>How to assess the quality of an assembly</li>
</ol>
<p>https://bioinformaticsdotca.github.io/high-throughput_biology_2017</p><p>Address of the bookmark: <a href="https://bioinformaticsdotca.github.io/high-throughput_biology_2017_module6_lab" rel="nofollow">https://bioinformaticsdotca.github.io/high-throughput_biology_2017_module6_lab</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40792/haslr-a-tool-for-rapid-genome-assembly-of-long-sequencing-reads</guid>
	<pubDate>Fri, 31 Jan 2020 05:50:15 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40792/haslr-a-tool-for-rapid-genome-assembly-of-long-sequencing-reads</link>
	<title><![CDATA[HASLR: a tool for rapid genome assembly of long sequencing reads]]></title>
	<description><![CDATA[<p><span>HASLR is a tool for rapid genome assembly of long sequencing reads. HASLR is a hybrid tool which means it requires long reads generated by Third Generation Sequencing technologies (such as PacBio or Oxford Nanopore) together with Next Generation Sequencing reads (such as Illumina) from the same sample.&nbsp;</span></p><p>Address of the bookmark: <a href="https://github.com/vpc-ccg/haslr" rel="nofollow">https://github.com/vpc-ccg/haslr</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42139/mixtures-a-novel-tool-for-bacterial-strain-reconstruction-from-reads</guid>
	<pubDate>Fri, 21 Aug 2020 08:23:19 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42139/mixtures-a-novel-tool-for-bacterial-strain-reconstruction-from-reads</link>
	<title><![CDATA[mixtureS: a novel tool for bacterial strain reconstruction from reads]]></title>
	<description><![CDATA[<div>
<p>mixtureS that can de novo identify bacterial strains from shotgun reads of a clonal or metagenomic sample, without prior knowledge about the strains and their variations. Tested on 243 simulated datasets and 195 experimental datasets, mixtureS reliably identified the strains, their numbers and their abundance. Compared with three tools, mixtureS showed better performance in almost all simulated datasets and the vast majority of experimental datasets.</p>
</div>
<div>
<div>Availability</div>
<p>The source code and tool mixtureS is available at&nbsp;<a href="http://www.cs.ucf.edu/~xiaoman/mixtureS/" target="_blank">http://www.cs.ucf.edu/&tilde;xiaoman/mixtureS/</a>.</p>
</div><p>Address of the bookmark: <a href="http://www.cs.ucf.edu/~xiaoman/mixtureS/" rel="nofollow">http://www.cs.ucf.edu/~xiaoman/mixtureS/</a></p>]]></description>
	<dc:creator>BioStar</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>
</item>

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