<?xml version='1.0'?><rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:georss="http://www.georss.org/georss" xmlns:atom="http://www.w3.org/2005/Atom" >
<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/36478?offset=220</link>
	<atom:link href="https://bioinformaticsonline.com/related/36478?offset=220" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/2726/comparison-of-short-read-de-novo-alignment-algorithms</guid>
	<pubDate>Wed, 21 Aug 2013 07:56:01 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/2726/comparison-of-short-read-de-novo-alignment-algorithms</link>
	<title><![CDATA[Comparison of Short Read De Novo Alignment Algorithms]]></title>
	<description><![CDATA[<p>Excellent article to introduce different sequencing methods along with tools for de novo assembly of sequencing reads and their relevant references.</p>
<p>Title:&nbsp;<strong>Comparison of Short Read De Novo Alignment Algorithms&nbsp;</strong></p>
<p>Author<strong>: Nikhil Gopal</strong></p><p>Address of the bookmark: <a href="http://biochem218.stanford.edu/Projects%202011/Gopal%202011.pdf" rel="nofollow">http://biochem218.stanford.edu/Projects%202011/Gopal%202011.pdf</a></p>]]></description>
	<dc:creator>Rahul Agarwal</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/bookmarks/view/37291/transrate-understanding-your-transcriptome-assembly</guid>
	<pubDate>Fri, 13 Jul 2018 07:49:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37291/transrate-understanding-your-transcriptome-assembly</link>
	<title><![CDATA[transrate: Understanding your transcriptome assembly]]></title>
	<description><![CDATA[<p><span>Transrate is software for&nbsp;</span><em>de-novo</em><span>&nbsp;transcriptome assembly quality analysis. It examines your assembly in detail and compares it to experimental evidence such as the sequencing reads, reporting quality scores for contigs and assemblies. This allows you to choose between assemblers and parameters, filter out the bad contigs from an assembly, and help decide when to stop trying to improve the assembly.</span></p><p>Address of the bookmark: <a href="http://hibberdlab.com/transrate/index.html" rel="nofollow">http://hibberdlab.com/transrate/index.html</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38505/allhic-phasing-and-scaffolding-polyploid-genomes-based-on-hi-c-data</guid>
	<pubDate>Thu, 20 Dec 2018 12:03:32 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38505/allhic-phasing-and-scaffolding-polyploid-genomes-based-on-hi-c-data</link>
	<title><![CDATA[ALLHiC: Phasing and scaffolding polyploid genomes based on Hi-C data]]></title>
	<description><![CDATA[<p><span>The major problem of scaffolding polyploid genome is that Hi-C signals are frequently detected between allelic haplotypes and any existing stat of art Hi-C scaffolding program links the allelic haplotypes together. To solve the problem, we developed a new Hi-C scaffolding pipeline, called ALLHIC, specifically tailored to the polyploid genomes. ALLHIC pipeline contains a total of 5 steps:&nbsp;</span><em>prune</em><span>,&nbsp;</span><em>partition</em><span>,&nbsp;</span><em>rescue</em><span>,&nbsp;</span><em>optimize</em><span>&nbsp;and&nbsp;</span><em>build</em><span>.</span></p><p>Address of the bookmark: <a href="https://github.com/tangerzhang/ALLHiC/wiki" rel="nofollow">https://github.com/tangerzhang/ALLHiC/wiki</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38755/svaba-genome-wide-detection-of-structural-variants-and-indels-by-local-assembly</guid>
	<pubDate>Mon, 21 Jan 2019 17:58:56 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38755/svaba-genome-wide-detection-of-structural-variants-and-indels-by-local-assembly</link>
	<title><![CDATA[SvABA: Genome-wide detection of structural variants and indels 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>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39856/tritex-sequence-assembly-pipeline-for-triticeae-genomes</guid>
	<pubDate>Tue, 20 Aug 2019 09:47:14 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39856/tritex-sequence-assembly-pipeline-for-triticeae-genomes</link>
	<title><![CDATA[TRITEX sequence assembly pipeline for Triticeae genomes]]></title>
	<description><![CDATA[<div>
<p>The pipeline is open-source and hosted in a public Bitbucket&nbsp;<a href="https://bitbucket.org/tritexassembly/tritexassembly.bitbucket.io/src/master/">repository</a>.</p>
</div>
<div>
<p>TRITEX has been run on highly inbred genotypes of barley (<em>Hordeum vulgare</em>), tetraploid wheat (<em>Triticum turgidum</em>) and hexaploid wheat (<em>T. aestivum</em>) with reasonable results: super-scaffold N50 values in the range of dozens of Mb and pseudomolecules with better gene space representation than a BAC-by-BAC assembly. It has never been tested and is not expected to work on heterozygous or autopolyploid genomes.</p>
</div>
<div>
<p>A protocol for generating chromosome-conformation capture sequencing (Hi-C) data suitable for use with the pipeline is described in&nbsp;<a href="https://bio-protocol.org/e2955">Himmelbach et al. 2018</a>. Refer to the&nbsp;<a href="https://www.10xgenomics.com/resources/technical-notes/">technical notes</a>&nbsp;of 10X Genomics on how to generate Chromium data.</p>
</div><p>Address of the bookmark: <a href="https://tritexassembly.bitbucket.io/" rel="nofollow">https://tritexassembly.bitbucket.io/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41734/supernova-generates-phased-whole-genome-de-novo-assemblies-from-a-chromium-prepared-library</guid>
	<pubDate>Sun, 31 May 2020 01:59:30 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41734/supernova-generates-phased-whole-genome-de-novo-assemblies-from-a-chromium-prepared-library</link>
	<title><![CDATA[Supernova: generates phased, whole-genome de novo assemblies from a Chromium-prepared library.]]></title>
	<description><![CDATA[<p>Supernova generates phased, whole-genome&nbsp;<em>de novo</em>&nbsp;assemblies from a Chromium-prepared library.</p>
<p>Please see&nbsp;<a href="https://support.10xgenomics.com/de-novo-assembly/guidance/doc/achieving-success-with-de-novo-assembly">Achieving Success with De Novo Assembly</a>&nbsp;and&nbsp;<a href="https://support.10xgenomics.com/de-novo-assembly/software/overview/system-requirements">System Requirements</a>&nbsp;<em>before</em>&nbsp;creating your Chromium libraries for assembly.</p>
<p>Supernova should be run using 38-56x coverage of the genome.<br>&bull; Somewhat higher coverage is&nbsp;<em>sometimes</em>&nbsp;advantageous.<br>&bull; Supernova will exit if it finds that coverage is far from the recommended range.<br>&bull; Note that at most 2.14 billion reads are allowed.<br>&bull; Please note that we have not extensively tested genomes larger than human, and any genome above approximately 4 GB should be considered experimental and is not supported.</p><p>Address of the bookmark: <a href="https://support.10xgenomics.com/de-novo-assembly/software/pipelines/latest/using/running" rel="nofollow">https://support.10xgenomics.com/de-novo-assembly/software/pipelines/latest/using/running</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/26332/pilon</guid>
	<pubDate>Mon, 08 Feb 2016 15:56:18 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26332/pilon</link>
	<title><![CDATA[Pilon]]></title>
	<description><![CDATA[<p>Pilon is a software tool which can be used to:</p>
<ul>
<li>Automatically improve draft assemblies</li>
<li>Find variation among strains, including large event detection</li>
</ul>
<p>Pilon requires as input a FASTA file of the genome along with one or more BAM files of reads aligned to the input FASTA file. Pilon uses read alignment analysis to identify inconsistencies between the input genome and the evidence in the reads. It then attempts to make improvements to the input genome, including:</p>
<ul>
<li>Single base differences</li>
<li>Small indels</li>
<li>Larger indel or block substitution events</li>
<li>Gap filling</li>
<li>Identification of local misassemblies, including optional opening of new gaps</li>
</ul>
<p>More at https://github.com/broadinstitute/pilon/wiki</p><p>Address of the bookmark: <a href="https://github.com/broadinstitute/pilon/wiki" rel="nofollow">https://github.com/broadinstitute/pilon/wiki</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26906/paired-end-assembler-for-dna-sequences</guid>
	<pubDate>Wed, 06 Apr 2016 05:25:34 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26906/paired-end-assembler-for-dna-sequences</link>
	<title><![CDATA[PAired-eND Assembler for DNA sequences]]></title>
	<description><![CDATA[<p>PANDASEQ is a program to align Illumina reads, optionally with PCR primers embedded in the sequence, and reconstruct an overlapping sequence.</p>
<p>&nbsp;</p>
<p>More at https://github.com/neufeld/pandaseq</p><p>Address of the bookmark: <a href="https://github.com/neufeld/pandaseq" rel="nofollow">https://github.com/neufeld/pandaseq</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>

</channel>
</rss>