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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/40792?offset=60</link>
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	<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>
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<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>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/31566/software-and-tools-to-detect-structure-variation-with-long-reads</guid>
	<pubDate>Wed, 15 Mar 2017 14:31:09 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/31566/software-and-tools-to-detect-structure-variation-with-long-reads</link>
	<title><![CDATA[Software and Tools to detect structure variation with long reads !!]]></title>
	<description><![CDATA[<p>Uncovering the connection between genetics and heritable diseases requires an approach that looks at all the variant bases and types in a genome. While a PacBio&nbsp;<em>de novo</em>&nbsp;assembly resolves the most novel SV variants. 8-10X PacBio coverage of single genomes or trios reveals triple the SVs detectable by short-read data.</p><p>With&nbsp;<span style="text-decoration: underline;"><a href="http://www.pacb.com/smrt-science/">Single Molecule, Real-Time (SMRT) Sequencing</a></span>, you can access structural variations having a broad range of sizes, types, and GC content with the ability to:</p><ul>
<li>Uncover missing heritability linked to structural variation</li>
<li>Unambiguously identify genomic context and variant breakpoints at the sequence level to unravel the genetic etiology of disease</li>
<li>Resolve structural variation across the complete size spectrum with basepair resolution</li>
</ul><p>Following are the SV tools, which can assist you to achieve your goal.</p><p><strong>Sniffles:</strong>&nbsp;Structural variation caller using third generation sequencing</p><p>Sniffles is a structural variation caller using third generation sequencing (PacBio or Oxford Nanopore). It detects all types of SVs using evidence from split-read alignments, high-mismatch regions, and coverage analysis. Please note the current version of Sniffles requires sorted output from BWA-MEM (use -M and -x parameter) or NGM-LR with the optional SAM attributes enabled!&nbsp;</p><p>More at&nbsp;https://github.com/fritzsedlazeck/Sniffles</p><p><strong style="font-size: 12.8px;"><br />MultiBreak-SV:</strong> It identifies structural variants from next-generation paired end data, third-generation long read data, or data from a combination of sequencing platforms.</p><p>There are two pieces of software in this release: (1) a pre-processor that takes machineformat (.m5) BLASR files, and (2) MultiBreak-SV. For installation and usage instructions, see doc/MultiBreakSV-Manual.txt.</p><p>More at&nbsp;https://github.com/raphael-group/multibreak-sv</p><p><strong style="font-size: 12.8px;"><br />Parliament:</strong>&nbsp;A Structural Variation Tool. Why ask a single sv-detection approach to find every variant when you can have a parliament of tools deciding?</p><p>Publication about the algorithm and &ldquo;&hellip;the first long-read characterization of structural variation in a diploid human personal genome&hellip;&rdquo; (HS1011) -&nbsp;<a href="http://www.biomedcentral.com/1471-2164/16/286">&ldquo;Assessing structural variation in a personal genome&mdash;towards a human reference diploid genome&rdquo;</a></p><p>More at&nbsp;https://sourceforge.net/projects/parliamentsv/</p><p>https://www.dnanexus.com/papers/Parliament_Info_Sheet.pdf</p><p><br /><strong>PBHoney:</strong>&nbsp;the structural variation discovery tool&nbsp;<br /><br />PBHoney is an implementation of two variant-identification approaches designed to exploit the high mappability of long reads (i.e., greater than 10,000 bp). PBHoney considers both intra-read discordance and soft-clipped tails of long reads to identify structural variants.</p><p>Read The Paper&nbsp;<a href="http://www.biomedcentral.com/1471-2105/15/180/abstract" target="_blank">http://www.biomedcentral.com/1471-2105/15/180/abstract</a></p><p>More at&nbsp;https://sourceforge.net/projects/pb-jelly/</p><p><strong><br />SMRT-SV:</strong> Structural variant and indel caller for PacBio reads</p><p>Structural variant (SV) and indel caller for PacBio reads based on methods from&nbsp;<a href="http://www.nature.com/nature/journal/vaop/ncurrent/full/nature13907.html">Chaisson et al. 2014</a>.</p><p>SMRT-SV provides an official software package for tools described in&nbsp;<a href="http://www.nature.com/nature/journal/vaop/ncurrent/full/nature13907.html">Chaisson et al. 2014</a>&nbsp;and adds several key features including the following.</p><ul>
<li>Unified variant calling user interface with built-in cluster compute support</li>
<li>Small indel calling (2-49 bp)</li>
<li>Improved inversion calling (<code>screenInversions</code>)</li>
<li>Quality metric for SV calls based on number of local assemblies supporting each call</li>
<li>Higher sensitivity for SV calls using tiled local assemblies across the entire genome instead of "signature" regions</li>
<li>Genotyping of SVs with Illumina paired-end reads from WGS samples</li>
</ul><p>More at&nbsp;https://github.com/EichlerLab/pacbio_variant_caller</p>]]></description>
	<dc:creator>Archana Malhotra</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27841/covcal-coverage-read-count-calculator</guid>
	<pubDate>Wed, 15 Jun 2016 18:08:13 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27841/covcal-coverage-read-count-calculator</link>
	<title><![CDATA[CovCal: Coverage / Read Count Calculator]]></title>
	<description><![CDATA[<h2>Coverage / Read Count Calculator</h2>
<h4>Calculate how much sequencing you need to hit a target depth of coverage (or vice versa).</h4>
<p><span>Instructions:</span> set the read length/configuration and genome size, then select what you want to calculate.</p>
<p>Written by <a href="http://stephenturner.us/" target="blank">Stephen Turner</a>, based on the <a href="http://www.ncbi.nlm.nih.gov/pubmed/3294162" target="_blank">Lander-Waterman formula</a>, inspired by <a href="http://core-genomics.blogspot.com/2016/05/how-many-reads-to-sequence-genome.html" target="_blank">a similar calculator</a> written by James Hadfield. Coverage is calculated as <em>C=LN/G</em> and reads as <em>N=CG/L</em> where <em>C</em> = Coverage (X),<em>L</em> = Read length (bp), <em>G</em> = Haploid genome size (bp), and <em>N</em> = Number of reads. Source code <a href="https://github.com/stephenturner/covcalc" target="_blank">on GitHub</a>.</p><p>Address of the bookmark: <a href="http://apps.bioconnector.virginia.edu/covcalc/" rel="nofollow">http://apps.bioconnector.virginia.edu/covcalc/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37496/gsearch-a-fast-and-flexible-general-search-tool-for-whole-genome-sequencing</guid>
	<pubDate>Mon, 06 Aug 2018 17:19:15 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37496/gsearch-a-fast-and-flexible-general-search-tool-for-whole-genome-sequencing</link>
	<title><![CDATA[gSearch: a fast and flexible general search tool for whole-genome sequencing]]></title>
	<description><![CDATA[<p><span>gSearch compares sequence variants in the Genome Variation Format (GVF) or Variant Call Format (VCF) with a pre-compiled annotation or with variants in other genomes. Its search algorithms are subsequently optimized and implemented in a multi-threaded manner.&nbsp;</span></p><p>Address of the bookmark: <a href="http://ml.ssu.ac.kr/gSearch/index.html" rel="nofollow">http://ml.ssu.ac.kr/gSearch/index.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<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>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34413/coursera-genome-assembly-tutorial</guid>
	<pubDate>Sat, 25 Nov 2017 08:57:25 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34413/coursera-genome-assembly-tutorial</link>
	<title><![CDATA[coursera genome assembly tutorial]]></title>
	<description><![CDATA[<p><span>Solutions to Coursera Genome Sequencing (Bioinformatics II)</span></p><p>Address of the bookmark: <a href="https://github.com/iansealy/coursera-assembly" rel="nofollow">https://github.com/iansealy/coursera-assembly</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37409/nanopolis-polish-a-genome-assembly</guid>
	<pubDate>Thu, 26 Jul 2018 04:51:28 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37409/nanopolis-polish-a-genome-assembly</link>
	<title><![CDATA[Nanopolis: polish a genome assembly]]></title>
	<description><![CDATA[<p><span>Software package for signal-level analysis of Oxford Nanopore sequencing data. Nanopolish can calculate an improved consensus sequence for a draft genome assembly, detect base modifications, call SNPs and indels with respect to a reference genome and more (see Nanopolish modules, below).</span></p>
<p>Quickstart</p>
<p>http://nanopolish.readthedocs.io/en/latest/quickstart_consensus.html</p>
<p>Algorithms</p>
<p>http://simpsonlab.github.io/2017/06/30/nanopolish-v0.7.0/</p><p>Address of the bookmark: <a href="https://github.com/jts/nanopolish" rel="nofollow">https://github.com/jts/nanopolish</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38008/quast-lg-versatile-genome-assembly-evaluation</guid>
	<pubDate>Thu, 25 Oct 2018 10:46:55 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38008/quast-lg-versatile-genome-assembly-evaluation</link>
	<title><![CDATA[QUAST-LG: Versatile genome assembly evaluation]]></title>
	<description><![CDATA[<p>QUAST-LG-a tool that compares large genomic de novo assemblies against reference sequences and computes relevant quality metrics. Since genomes generally cannot be reconstructed completely due to complex repeat patterns and low coverage regions, we introduce a concept of upper bound assembly for a given genome and set of reads, and compute theoretical limits on assembly correctness and completeness. Using QUAST-LG, we show how close the assemblies are to the theoretical optimum, and how far this optimum is from the finished reference.</p>
<h4>AVAILABILITY AND IMPLEMENTATION:</h4>
<p>http://cab.spbu.ru/software/quast-lg</p><p>Address of the bookmark: <a href="http://cab.spbu.ru/software/quast-lg/" rel="nofollow">http://cab.spbu.ru/software/quast-lg/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/38618/canu-genome-assembly-parameters</guid>
	<pubDate>Mon, 07 Jan 2019 08:40:37 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/38618/canu-genome-assembly-parameters</link>
	<title><![CDATA[CANU genome assembly parameters !]]></title>
	<description><![CDATA[<p>Choose the appropriate parameters to run Canu and run it. The assembly will take about an hour. You can use two cores (parameter&nbsp;<code>-maxThreads=2</code>) and you would like to disable cluster option, since we compute on a single Amazon server set off the option to compute on cluster&nbsp;<code>useGrid=false</code>. This specifications should be for your project discussed with a local computing guru. The parameters that are in square brackets&nbsp;<code>[]</code>&nbsp;are optional, symbol&nbsp;<code>|</code>&nbsp;stands for "or".</p><pre><code>usage:   canu [-correct | -trim | -assemble | -trim-assemble] \
              [-s ] \
               -p  \
               -d  \
               genomeSize=[g|m|k] \
               -maxThreads=2 \
               useGrid=false \
              [other-options] \
               read_file.fastq.gz
</code></pre><p>A default&nbsp;<code>Canu</code>&nbsp;run produces usually high quality assembly, example of a command that was used for testing can be found below. However, there are still a lot of parameters that are possible to tweak. For example if we desire to assemble haplotypes separately of if we want to smash them together, we can alternate the error correction process.</p><pre><code>canu -p test_asmbl \
     -d asm_test3 \
     genomeSize=2m \
     -maxThreads=2 useGrid=false \
     -pacbio-raw \ ~/pacbio/dna/sample_reads.fastq.gz</code></pre><p>There is a brilliant&nbsp;<a href="http://canu.readthedocs.io/en/latest/faq.html#what-parameters-can-i-tweak">section in documentation</a>&nbsp;about parameter tweaking.</p><p>The output directory contains will contain many files. The most interesting ones are:</p><ul>
<li><code>*.correctedReads.fasta.gz</code>&nbsp;: file containing the input sequences after correction, trim and split based on consensus evidence.</li>
<li><code>*.trimmedReads.fastq</code>&nbsp;: file containing the sequences after correction and final trimming</li>
<li><code>*.layout</code>&nbsp;: file containing informations about read inclusion in the final assembly</li>
<li><code>*.gfa</code>&nbsp;: file containing the assembly graph by Canu</li>
<li><code>*.contigs.fasta</code>&nbsp;: file containing everything that could be assembled and is part of the primary assembly</li>
</ul><p>The basic stats of assembly can be read from reports generated by the assembler, or calculated using standard UNIX command line tools.</p><p>More at&nbsp;https://canu.readthedocs.io/en/latest/faq.html</p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38792/nxrepair-error-correction-in-de-novo-assemblies-using-nextera-mate-pair-reads</guid>
	<pubDate>Thu, 24 Jan 2019 10:35:12 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38792/nxrepair-error-correction-in-de-novo-assemblies-using-nextera-mate-pair-reads</link>
	<title><![CDATA[NxRepair: error correction in de novo assemblies using Nextera Mate Pair Reads]]></title>
	<description><![CDATA[<p>NxRepair is a python module that automatically detects large structural errors in de novo assemblies using Nextera mate pair reads. The decector will break a contig at the site of an identified misassembly and will generate a new fasta file containing both the corrected contigs and the correct, unaffected contigs.</p>
<p>https://nxrepair.readthedocs.io/en/latest/tutorial.html</p>
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<pre>nxrepair aligned_matepairs.bam assemblyfasta.fasta error_locations.csv new_fasta.fasta</pre>
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<div>&nbsp;</div><p>Address of the bookmark: <a href="https://github.com/rebeccaroisin/nxrepair" rel="nofollow">https://github.com/rebeccaroisin/nxrepair</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
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