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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/43057?offset=230</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34931/3d-dna-3d-de-novo-assembly-3d-dna-pipeline</guid>
	<pubDate>Thu, 28 Dec 2017 10:09:37 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34931/3d-dna-3d-de-novo-assembly-3d-dna-pipeline</link>
	<title><![CDATA[3d-dna: 3D de novo assembly (3D DNA) pipeline]]></title>
	<description><![CDATA[<p>This code is designed to enable anyone to reproduce the Hs2-HiC and the AaegL4 genomes reported in:&nbsp;<a href="http://science.sciencemag.org/content/early/2017/03/22/science.aal3327.full">Dudchenko et al., De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds. Science, 2017.</a></p>
<p>Unless otherwise noted, all terminology below is consistent with this paper, and all references to figures and tables in this readme refer to this paper. Specifically, some of the terminology used below is outlined in&nbsp;<code>Figure S2</code>. The assembly procedure is described in detail in the&nbsp;<a href="http://science.sciencemag.org/content/suppl/2017/03/22/science.aal3327.DC1?_ga=1.9816115.760837492.1490574064">Supporting Online Materials</a>, specifically in the section labelled &ldquo;Pipeline description&rdquo;.</p>
<p>In addition, the pipeline uses tools and methods from&nbsp;<a href="http://www.cell.com/cell-systems/abstract/S2405-4712(16)30219-8">Juicer (Durand &amp; Shamim et al., Cell Systems, 2016)</a>&nbsp;and&nbsp;<a href="http://www.cell.com/cell-systems/abstract/S2405-4712(15)00054-X">Juicebox (Durand &amp; Robinson et al., Cell Systems, 2016)</a>, as well as additional dependencies noted below.</p>
<p>Feel free to post your questions and comments at:&nbsp;<a href="http://www.aidenlab.org/forum.html">http://www.aidenlab.org/forum.html</a></p>
<p>http://aidenlab.org/documentation.html</p><p>Address of the bookmark: <a href="https://github.com/theaidenlab/3d-dna" rel="nofollow">https://github.com/theaidenlab/3d-dna</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36456/alpaca-a-hybrid-strategy-for-assembly-of-genomic-dna-shotgun-sequencing-reads</guid>
	<pubDate>Mon, 30 Apr 2018 04:38:40 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36456/alpaca-a-hybrid-strategy-for-assembly-of-genomic-dna-shotgun-sequencing-reads</link>
	<title><![CDATA[ALPACA: A hybrid strategy for assembly of genomic DNA shotgun sequencing reads.]]></title>
	<description><![CDATA[<p><span>ALPACA requires Celera Assembler 8.3 or later. It is recommended to build Celera Assembler from source. (Why? The pre-built binaries CA_8.3rc1 and CA8.3rc2 will work for any large data set.&nbsp;</span></p>
<p><span>Detail paper at&nbsp;https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-017-3927-8</span></p><p>Address of the bookmark: <a href="https://github.com/VicugnaPacos/ALPACA" rel="nofollow">https://github.com/VicugnaPacos/ALPACA</a></p>]]></description>
	<dc:creator>Seema Singh</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/40516/nextdenovo-string-graph-based-de-novo-assembler-for-tgs-long-reads</guid>
	<pubDate>Sun, 05 Jan 2020 04:08:29 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40516/nextdenovo-string-graph-based-de-novo-assembler-for-tgs-long-reads</link>
	<title><![CDATA[NextDenovo: string graph-based de novo assembler for TGS long reads]]></title>
	<description><![CDATA[<p>NextDenovo is a string graph-based<span>&nbsp;</span><em>de novo</em><span>&nbsp;</span>assembler for TGS long reads. It uses a "correct-then-assemble" strategy similar to canu, but requires significantly less computing resources and storages. After assembly, the per-base error rate is about 97-98%, to further improve single base accuracy, please use<span>&nbsp;</span><a href="https://github.com/Nextomics/NextPolish">NextPolish</a>.</p>
<p>NextDenovo contains two core modules: NextCorrect and NextGraph. NextCorrect can be used to correct TGS long reads with approximately 15% sequencing errors, and NextGraph can be used to construct a string graph with corrected reads. It also contains a modified version of<span>&nbsp;</span><a href="https://github.com/lh3/minimap2">minimap2</a><span>&nbsp;</span>for adapting input and output and producing more sensitive and accurate dovetail overlaps, and some useful utilities (see<span>&nbsp;</span><a href="https://github.com/Nextomics/NextDenovo/blob/master/doc/UTILITY.md">here</a><span>&nbsp;</span>for more details).</p><p>Address of the bookmark: <a href="https://github.com/Nextomics/NextDenovo" rel="nofollow">https://github.com/Nextomics/NextDenovo</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40897/mec-contig-misassembly-correction</guid>
	<pubDate>Tue, 04 Feb 2020 23:40:49 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40897/mec-contig-misassembly-correction</link>
	<title><![CDATA[MEC: Contig Misassembly Correction]]></title>
	<description><![CDATA[<p><span>MEC, to identify and correct misassemblies in contigs. Firstly, MEC takes fragment coverage as the feature to detect the candidate misassemblies. Then, it can distinguish a large number of false positives from the candidate misassemblies based on the distribution of paired-end reads and the statistical analysis of GC-contents. We apply MEC to four real contig datasets, and carry out experiments to analyze the influence of MEC on scaffolding results, which shows that MEC can reduce misassemblies effectively and result in quantitative improvements in scaffolding quality. MEC is publicly available for download at https://github.com/bioinfomaticsCSU/MEC.</span></p><p>Address of the bookmark: <a href="https://github.com/bioinfomaticsCSU/MEC" rel="nofollow">https://github.com/bioinfomaticsCSU/MEC</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41397/svaba-structural-variation-and-indel-detection-by-local-assembly</guid>
	<pubDate>Tue, 10 Mar 2020 07:52:15 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41397/svaba-structural-variation-and-indel-detection-by-local-assembly</link>
	<title><![CDATA[SvABA: Structural variation and indel detection by local assembly]]></title>
	<description><![CDATA[<p><span>SvABA is a method for detecting structural variants in sequencing data using genome-wide local assembly. Under the hood, SvABA uses a custom implementation of&nbsp;</span><a href="https://github.com/jts/sga">SGA</a><span>&nbsp;(String Graph Assembler) by Jared Simpson, and&nbsp;</span><a href="https://github.com/lh3/bwa">BWA-MEM</a><span>&nbsp;by Heng Li. Contigs are assembled for every 25kb window (with some small overlap) for every region in the genome. The default is to use only clipped, discordant, unmapped and indel reads, although this can be customized to any set of reads at the command line using&nbsp;</span><a href="https://github.com/walaj/VariantBam">VariantBam</a><span>&nbsp;rules. These contigs are then immediately aligned to the reference with BWA-MEM and parsed to identify variants. Sequencing reads are then realigned to the contigs with BWA-MEM, and variants are scored by their read support.</span></p><p>Address of the bookmark: <a href="https://github.com/walaj/svaba" rel="nofollow">https://github.com/walaj/svaba</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43926/aun-a-new-metric-to-measure-assembly-contiguity</guid>
	<pubDate>Tue, 02 Aug 2022 01:18:47 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43926/aun-a-new-metric-to-measure-assembly-contiguity</link>
	<title><![CDATA[auN: a new metric to measure assembly contiguity]]></title>
	<description><![CDATA[<p><span>Given a de novo assembly, we often measure the &ldquo;average&rdquo; contig length by N50.&nbsp;</span><a href="https://en.wikipedia.org/wiki/N50,_L50,_and_related_statistics">N50</a><span>&nbsp;is neither the real average nor median. It is the length of the contig such that this and longer contigs cover at least 50% of the assembly. A longer N50 indicates better contiguity. We can similarly define N</span><em>x</em><span>&nbsp;such that contigs no shorter than N</span><em>x</em><span>&nbsp;covers&nbsp;</span><em>x</em><span>% of the assembly. The N</span><em>x</em><span>&nbsp;curve plots N</span><em>x</em><span>&nbsp;as a function of&nbsp;</span><em>x</em><span>, where&nbsp;</span><em>x</em><span>&nbsp;is ranged from 0 to 100.</span></p>
<p><span><img src="http://lh3.github.io/images/NGx_plot.png" alt="image" style="border: 0px;"></span></p><p>Address of the bookmark: <a href="https://lh3.github.io/2020/04/08/a-new-metric-on-assembly-contiguity" rel="nofollow">https://lh3.github.io/2020/04/08/a-new-metric-on-assembly-contiguity</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26325/crossmap</guid>
	<pubDate>Mon, 08 Feb 2016 15:47:00 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26325/crossmap</link>
	<title><![CDATA[CrossMap]]></title>
	<description><![CDATA[<p>CrossMap is a program for convenient conversion of genome coordinates (or annotation files) between <em>different assemblies</em> (such as Human <a href="http://www.ncbi.nlm.nih.gov/assembly/2928/">hg18 (NCBI36)</a> &lt;&gt; <a href="http://www.ncbi.nlm.nih.gov/assembly/2758/">hg19 (GRCh37)</a>, Mouse <a href="http://www.ncbi.nlm.nih.gov/assembly/165668/">mm9 (MGSCv37)</a> &lt;&gt; <a href="http://www.ncbi.nlm.nih.gov/assembly/327618/">mm10 (GRCm38)</a>).</p>
<p>It supports most commonly used file formats including SAM/BAM, Wiggle/BigWig, BED, GFF/GTF, VCF.</p>
<p>CrossMap is designed to liftover genome coordinates between assemblies. It&rsquo;s <em>not</em> a program for aligning sequences to reference genome.</p>
<p>We <em>do not</em> recommend using CrossMap to convert genome coordinates between species.</p>
<p>More at http://crossmap.sourceforge.net/</p><p>Address of the bookmark: <a href="http://crossmap.sourceforge.net/" rel="nofollow">http://crossmap.sourceforge.net/</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27110/easyfig</guid>
	<pubDate>Fri, 29 Apr 2016 05:49:39 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27110/easyfig</link>
	<title><![CDATA[Easyfig]]></title>
	<description><![CDATA[<p>Easyfig has moved to github, for newer releases of Easyfig please visit our new webpage - https://mjsull.github.io/Easyfig.&nbsp; Easyfig is a Python application for creating linear comparison figures of multiple genomic loci with an easy-to-use graphical user interface (GUI).</p>
<p>More at http://easyfig.sourceforge.net/</p><p>Address of the bookmark: <a href="http://easyfig.sourceforge.net/" rel="nofollow">http://easyfig.sourceforge.net/</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>

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