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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/39372?offset=130</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37473/lsc-a-long-read-error-correction-tool</guid>
	<pubDate>Thu, 02 Aug 2018 07:39:46 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37473/lsc-a-long-read-error-correction-tool</link>
	<title><![CDATA[LSC :a long read error correction tool]]></title>
	<description><![CDATA[<h2>Getting Started</h2>
<p>These simple steps will help you integrate LSC into your transcriptomics analysis pipeline.</p>
<ul>
<li>Read the&nbsp;<a href="https://www.healthcare.uiowa.edu/labs/au/LSC/LSC_requirements.asp">LSC_requirements</a>&nbsp;for running LSC.</li>
<li><a href="https://www.healthcare.uiowa.edu/labs/au/LSC/LSC_download.asp">Download</a>&nbsp;and set-up the LSC package.</li>
<li>Follow the&nbsp;<a href="https://www.healthcare.uiowa.edu/labs/au/LSC/LSC_tutorial.asp">tutorial</a>&nbsp;to see how LSC works on some example data.</li>
<li>Read the&nbsp;<a href="https://www.healthcare.uiowa.edu/labs/au/LSC/LSC_manual.asp">manual</a>&nbsp;if anything is unclear.</li>
<li>You're ready, Happy LSCing!</li>
</ul>
<h2>Latest publication</h2>
<p><span>Kin Fai Au, Jason Underwood, Lawrence Lee and Wing Hung Wong&nbsp;</span><br><strong>Improving PacBio Long Read Accuracy by Short Read Alignment&nbsp;</strong><span>[</span><a href="http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0046679">Manuscript</a><span>]&nbsp;</span><br><em>PLoS ONE</em><span>&nbsp;2012. 7(10): e46679. doi:10.1371/journal.pone.0046679</span></p><p>Address of the bookmark: <a href="https://www.healthcare.uiowa.edu/labs/au/LSC/" rel="nofollow">https://www.healthcare.uiowa.edu/labs/au/LSC/</a></p>]]></description>
	<dc:creator>Jit</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/37796/grsr-a-tool-for-deriving-genome-rearrangement-scenarios-from-multiple-unichromosomal-genome-sequences</guid>
	<pubDate>Fri, 28 Sep 2018 09:35:10 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37796/grsr-a-tool-for-deriving-genome-rearrangement-scenarios-from-multiple-unichromosomal-genome-sequences</link>
	<title><![CDATA[GRSR: a tool for deriving genome rearrangement scenarios from multiple unichromosomal genome sequences]]></title>
	<description><![CDATA[<p>GRSR is a Tool for Deriving Genome Rearrangement Scenarios for Multiple Uni-chromosomal Genomes. This tool will do the following steps:</p>
<ul>
<li>Step 1. Run mugsy to get multiple sequence alignment results.</li>
<li>Step 2 &amp; 3. Extraction of the Coordinates of Core Blocks, Construction of Synteny Blocks and Generating Signed Permutations.</li>
<li>Step 4. Generate pairwise genome rearrangement scenarios and find repeats at the breakpoints of each rearrangement events.</li>
<li></li>
<li></li>
</ul>
<p>https://github.com/DanwangJessica/GRSR</p><p>Address of the bookmark: <a href="https://github.com/DanwangJessica/GRSR" rel="nofollow">https://github.com/DanwangJessica/GRSR</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38208/anitools-web-a-web-tool-for-fast-genome-comparison-within-multiple-bacterial-strains</guid>
	<pubDate>Wed, 14 Nov 2018 04:34:23 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38208/anitools-web-a-web-tool-for-fast-genome-comparison-within-multiple-bacterial-strains</link>
	<title><![CDATA[ANItools web: a web tool for fast genome comparison within multiple bacterial strains]]></title>
	<description><![CDATA[<p><span>ANItools is a software package written by PERL scripts that can be run in a Linux/Unix system. If you want to compare bacterial genomes and calculate their average nucleotide identity (ANI), you could download and run this program directly. Or you could send us the genome sequence by email. Then we will do the analysis work for you.</span></p>
<p><span>https://academic.oup.com/database/article/doi/10.1093/database/baw084/2630454</span></p><p>Address of the bookmark: <a href="http://ani.mypathogen.cn/" rel="nofollow">http://ani.mypathogen.cn/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39624/cogent-a-tool-for-reconstructing-the-coding-genome-using-high-quality-full-length-transcriptome-sequences</guid>
	<pubDate>Tue, 18 Jun 2019 05:33:04 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39624/cogent-a-tool-for-reconstructing-the-coding-genome-using-high-quality-full-length-transcriptome-sequences</link>
	<title><![CDATA[Cogent: a tool for reconstructing the coding genome using high-quality full-length transcriptome sequences.]]></title>
	<description><![CDATA[<div id="yui_3_14_1_1_1560853173251_3865">Cogent is a tool that identifies gene&nbsp;families and reconstructs the coding genome using high-quality transcriptome data without a reference genome, and can be used to check&nbsp;assemblies&nbsp;for the presence of&nbsp;these known coding sequences.</div>
<div>&nbsp;</div>
<div>
<p>Cogent is a tool for reconstructing the coding genome using high-quality full-length transcriptome sequences. It is designed to be used on&nbsp;<a href="https://github.com/PacificBiosciences/cDNA_primer/wiki">Iso-Seq data</a>&nbsp;and in cases where there is no reference genome or the ref genome is highly incomplete.</p>
<p>See a&nbsp;<a href="https://www.dropbox.com/s/mn6hwhguh0pqceu/20160106_Cogent_developers_conference_slides_Cuttlefish.pdf?dl=0">recent presentation</a>&nbsp;on Cogent being applied to the Cuttlefish Iso-Seq data.</p>
<p><a href="https://www.dropbox.com/s/kz0gi7qg0w82k9a/20161026_Cogent_manuscript_forGitHub.pdf?dl=0">Cogent preliminary draft paper (updated 2016Dec version)</a>,&nbsp;<a href="https://www.dropbox.com/s/37412o8glvnfhf9/20161026_Cogent_ManuscriptPlusSupplement_forGitHub.pdf?dl=0">Supplementary</a></p>
<p>Please see&nbsp;<a href="https://github.com/Magdoll/Cogent/wiki">wiki</a>&nbsp;for details on usage.</p>
</div><p>Address of the bookmark: <a href="https://github.com/Magdoll/Cogent" rel="nofollow">https://github.com/Magdoll/Cogent</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40707/vt-a-variant-tool-set-that-discovers-short-variants-from-next-generation-sequencing-data</guid>
	<pubDate>Tue, 28 Jan 2020 03:44:43 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40707/vt-a-variant-tool-set-that-discovers-short-variants-from-next-generation-sequencing-data</link>
	<title><![CDATA[vt: a variant tool set that discovers short variants from Next Generation Sequencing data.]]></title>
	<description><![CDATA[<p><span>vt is a variant tool set that discovers short variants from Next Generation Sequencing data.</span></p>
<p><span><a href="https://genome.sph.umich.edu/wiki/Vt">https://genome.sph.umich.edu/wiki/Vt</a></span></p>
<p><a href="https://github.com/atks/vt">https://github.com/atks/vt</a></p><p>Address of the bookmark: <a href="https://genome.sph.umich.edu/wiki/Vt" rel="nofollow">https://genome.sph.umich.edu/wiki/Vt</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41669/filtlong-quality-filtering-tool-for-long-reads</guid>
	<pubDate>Wed, 13 May 2020 10:23:55 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41669/filtlong-quality-filtering-tool-for-long-reads</link>
	<title><![CDATA[Filtlong: quality filtering tool for long reads]]></title>
	<description><![CDATA[<p>Filtlong is a tool for filtering long reads by quality. It can take a set of long reads and produce a smaller, better subset. It uses both read length (longer is better) and read identity (higher is better) when choosing which reads pass the filter.</p>
<p>Filtlong builds into a stand-alone executable:</p>
<pre><code>git clone https://github.com/rrwick/Filtlong.git
cd Filtlong
make -j
bin/filtlong -h
</code></pre><p>Address of the bookmark: <a href="https://github.com/rrwick/Filtlong" rel="nofollow">https://github.com/rrwick/Filtlong</a></p>]]></description>
	<dc:creator>Radha Agarkar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41916/truvari-structural-variant-comparison-tool-for-vcfs</guid>
	<pubDate>Tue, 30 Jun 2020 21:30:44 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41916/truvari-structural-variant-comparison-tool-for-vcfs</link>
	<title><![CDATA[truvari: Structural variant comparison tool for VCFs]]></title>
	<description><![CDATA[<p>Structural variant comparison tool for VCFs</p>
<p>Given benchmark and comparsion sets of SVs, calculate the recall, precision, and f-measure.</p>
<p><a href="https://github.com/spiralgenetics/www.spiralgenetics.com">Spiral Genetics</a></p>
<p><a href="https://docs.google.com/presentation/d/17mvC1XOpOm7khAbZwF3SgtG2Rl4M9Mro37yF2nN7GhE/edit">Motivation</a></p><p>Address of the bookmark: <a href="https://github.com/spiralgenetics/truvari" rel="nofollow">https://github.com/spiralgenetics/truvari</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42155/clustergrammer-is-a-web-based-tool-for-visualizing-high-dimensional-data-as-an-interactive-and-shareable-hierarchically-clustered-heatmap</guid>
	<pubDate>Sun, 23 Aug 2020 19:30:17 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42155/clustergrammer-is-a-web-based-tool-for-visualizing-high-dimensional-data-as-an-interactive-and-shareable-hierarchically-clustered-heatmap</link>
	<title><![CDATA[Clustergrammer is a web-based tool for visualizing high-dimensional data as an interactive and shareable hierarchically clustered heatmap]]></title>
	<description><![CDATA[<p><span>Clustergrammer is a web-based tool for visualizing high-dimensional data (e.g. a matrix) as an interactive and shareable hierarchically clustered heatmap. Clustergrammer's front end (</span><a href="http://clustergrammer.readthedocs.io/clustergrammer_js.html#clustergrammer-js">Clustergrammer-JS</a><span>) is built using&nbsp;</span><a href="https://d3js.org/">D3.js</a><span>&nbsp;and its back-end (</span><a href="http://clustergrammer.readthedocs.io/clustergrammer_py.html#clustergrammer-py">Clustergrammer-PY</a><span>) is built using Python. Clustergrammer produces highly interactive visualizations that enable intuitive exploration of high-dimensional data and has several biology-specific features (e.g. enrichment analysis, see&nbsp;</span><a href="http://clustergrammer.readthedocs.io/biology_specific_features.html#biology-specific-features">Biology-Specific Features</a><span>) to facilitate the exploration of gene-level biological data.&nbsp;</span></p><p>Address of the bookmark: <a href="https://github.com/MaayanLab/clustergrammer" rel="nofollow">https://github.com/MaayanLab/clustergrammer</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43263/jumbodb-tool-for-de-bruijn-graph-construction</guid>
	<pubDate>Tue, 17 Aug 2021 13:33:46 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43263/jumbodb-tool-for-de-bruijn-graph-construction</link>
	<title><![CDATA[JumboDB: tool for de Bruijn graph construction]]></title>
	<description><![CDATA[<p><span>jumboDB tool for fast de Bruijn graph construction from long sequences (reads or genomes) with very low error rate. JumboDB is not a genome assembler by itself but rather a subroutine that translates a set of reads into compressed de Bruijn graph.</span></p>
<p><span>More at&nbsp;https://github.com/AntonBankevich/jumboDB</span></p><p>Address of the bookmark: <a href="https://github.com/AntonBankevich/jumboDB" rel="nofollow">https://github.com/AntonBankevich/jumboDB</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>

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