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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/40711?offset=120</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41146/lofreq-a-sequence-quality-aware-ultra-sensitive-variant-caller-for-ngs-data</guid>
	<pubDate>Tue, 18 Feb 2020 03:24:22 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41146/lofreq-a-sequence-quality-aware-ultra-sensitive-variant-caller-for-ngs-data</link>
	<title><![CDATA[LoFreq*: A sequence-quality aware, ultra-sensitive variant caller for NGS data]]></title>
	<description><![CDATA[<p>LoFreq* (i.e. LoFreq version 2) is a fast and sensitive variant-caller for inferring SNVs and indels from next-generation sequencing data. It makes full use of base-call qualities and other sources of errors inherent in sequencing (e.g. mapping or base/indel alignment uncertainty), which are usually ignored by other methods or only used for filtering.</p>
<p>https://github.com/CSB5/lofreq</p>
<p>http://csb5.github.io/lofreq/installation/</p>
<p>https://github.com/CSB5/lofreq/tree/master/dist</p><p>Address of the bookmark: <a href="http://csb5.github.io/lofreq/" rel="nofollow">http://csb5.github.io/lofreq/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43850/merfin-improved-variant-filtering-assembly-evaluation-and-polishing-via-k-mer-validation</guid>
	<pubDate>Sun, 03 Apr 2022 20:35:19 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43850/merfin-improved-variant-filtering-assembly-evaluation-and-polishing-via-k-mer-validation</link>
	<title><![CDATA[Merfin: improved variant filtering, assembly evaluation and polishing via k-mer validation]]></title>
	<description><![CDATA[<p><span>Merfin, a&nbsp;</span><em>k</em><span>-mer based variant-filtering algorithm for improved accuracy in genotyping and genome assembly polishing. Merfin evaluates each variant based on the expected&nbsp;</span><em>k</em><span>-mer multiplicity in the reads, independently of the quality of the read alignment and variant caller&rsquo;s internal score. Merfin increased the precision of genotyped calls in several benchmarks, improved consensus accuracy and reduced frameshift errors when applied to human and nonhuman assemblies built from Pacific Biosciences HiFi and continuous long reads or Oxford Nanopore reads, including the first complete human genome. Moreover, we introduce assembly quality and completeness metrics that account for the expected genomic copy numbers.</span></p>
<p><span>More at&nbsp;https://www.nature.com/articles/s41592-022-01445-y</span></p>
<p><img src="https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41592-022-01445-y/MediaObjects/41592_2022_1445_Fig1_HTML.png" alt="image" style="border: 0px; border: 0px;"></p><p>Address of the bookmark: <a href="https://github.com/arangrhie/merfin" rel="nofollow">https://github.com/arangrhie/merfin</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44311/jbrowse-2-a-modular-genome-browser-with-views-of-synteny-and-structural-variation</guid>
	<pubDate>Tue, 25 Apr 2023 20:58:52 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44311/jbrowse-2-a-modular-genome-browser-with-views-of-synteny-and-structural-variation</link>
	<title><![CDATA[JBrowse 2: a modular genome browser with views of synteny and structural variation]]></title>
	<description><![CDATA[<ul dir="auto">
<li>igvjs - a create-react-app with igv package from npm installed. the igv.js is instrumented to output "DONE" to the console when finished, and to have an increased fetchSizeLimit (which is otherwise git in CRAM longread tests)</li>
<li>jb2-web - stock instance of jbrowse-web v1.7.5</li>
<li>jb1 - stock instance of jbrowse 1 v1.16.11</li>
<li>jb2 embedded - a create-react-app with @jbrowse/react-linear-genome-view</li>
</ul><p>Address of the bookmark: <a href="https://github.com/GMOD/jb2profile" rel="nofollow">https://github.com/GMOD/jb2profile</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29103/genome-strip</guid>
	<pubDate>Tue, 06 Sep 2016 03:58:19 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29103/genome-strip</link>
	<title><![CDATA[Genome STRiP]]></title>
	<description><![CDATA[<p><strong>Genome STRiP</strong><span>&nbsp;(Genome STRucture In Populations) is a suite of tools for discovering and genotyping structural variations using sequencing data. The methods are designed to detect shared variation using data from multiple individuals.</span><br><br><span>Genome STRiP looks both across and within a set of sequenced genomes to detect variation. The methods are adaptive and support heterogeneous data sets, including variations in sequencing depth, read lengths and mixtures of paired and single-end reads. A minimum of 20 to 30 genomes are required to get acceptable results, but the method gains power across genomes and processing more genomes provide better results.</span><br><br><span>To run discovery or genotyping on a single sequenced genome or a small set of genomes, you need to call your data against a background population, such as a set of genomes from the 1000 Genomes Project.&nbsp; The background population does not need to be matched to the target individuals.</span></p><p>Address of the bookmark: <a href="http://software.broadinstitute.org/software/genomestrip/" rel="nofollow">http://software.broadinstitute.org/software/genomestrip/</a></p>]]></description>
	<dc:creator>Neel</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>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27094/smash-an-alignment-free-method-to-find-and-visualise-rearrangements-between-pairs-of-dna-sequences</guid>
	<pubDate>Tue, 26 Apr 2016 12:18:49 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27094/smash-an-alignment-free-method-to-find-and-visualise-rearrangements-between-pairs-of-dna-sequences</link>
	<title><![CDATA[Smash: An alignment-free method to find and visualise rearrangements between pairs of DNA sequences]]></title>
	<description><![CDATA[<p><strong>Smash is a completely alignment-free method/tool to find and visualise genomic rearrangements</strong><span>. The detection is based on&nbsp;</span><strong>conditional exclusive compression</strong><span>, namely using a FCM (Markov model), of high context order (typically 20). For visualisation, Smash outputs a&nbsp;</span><strong>SVG image</strong><span>, with an&nbsp;</span><strong>ideogram</strong><span>output architecture, where the patterns are represented with several&nbsp;</span><strong>HSV values</strong><span>&nbsp;(only value varies). The method can perform both in small- and large-scale. Nevertheless is more directed to large-scale since that the main aim of the research is to&nbsp;</span><strong>know where the large-scale [chromosomal by chromosome] of several primates was equal/different, having at a glance a map of the entire genomes</strong><span>.</span></p><p>Address of the bookmark: <a href="http://bioinformatics.ua.pt/software/smash/" rel="nofollow">http://bioinformatics.ua.pt/software/smash/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29992/spines</guid>
	<pubDate>Mon, 28 Nov 2016 05:33:26 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29992/spines</link>
	<title><![CDATA[Spines]]></title>
	<description><![CDATA[<p><a href="https://www.broadinstitute.org/ftp/distribution/software/spines/"><em>Spines</em></a>&nbsp;is a collection of software tools, developed and used by the Vertebrate Genome Biology Group at the Broad Institute. It provides basic data structures for efficient data manipulation (mostly genomic sequences, alignments, variation etc.), as well as specialized tool sets for various analyses. It also features three sequence alignment packages:&nbsp;<em>Satsuma,</em>&nbsp;a highly parallelized program for high-sensitivity, genome-wide synteny;&nbsp;<em>Papaya,</em>&nbsp;an all-purpose alignment tool for less diverged sequences; and&nbsp;<em>SLAP,</em>&nbsp;a context-sensitive local aligner for diverged sequences with large gaps.</p>
<p>Access&nbsp;<em>Spines</em>&nbsp;<a href="https://www.broadinstitute.org/ftp/distribution/software/spines/">here</a>.</p><p>Address of the bookmark: <a href="https://www.broadinstitute.org/genome-sequencing-and-analysis/spines" rel="nofollow">https://www.broadinstitute.org/genome-sequencing-and-analysis/spines</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30701/harvest</guid>
	<pubDate>Tue, 31 Jan 2017 10:57:56 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30701/harvest</link>
	<title><![CDATA[Harvest]]></title>
	<description><![CDATA[<p>Harvest is a suite of core-genome alignment and visualization tools for quickly analyzing thousands of intraspecific microbial genomes, including variant calls, recombination detection, and phylogenetic trees.</p>
<p><a href="http://harvest.readthedocs.io/en/latest/_images/screen.png"><img src="http://harvest.readthedocs.io/en/latest/_images/screen.png" alt="_images/screen.png" style="border: 0px;"></a><span></span></p>
<p><strong>Tools</strong></p>
<ul>
<li><a href="http://harvest.readthedocs.io/en/latest/content/parsnp.html">Parsnp</a>&nbsp;- Core-genome alignment and analysis</li>
<li><a href="http://harvest.readthedocs.io/en/latest/content/gingr.html">Gingr</a>&nbsp;- Interactive visualization of alignments, trees and variants</li>
<li><a href="http://harvest.readthedocs.io/en/latest/content/harvest-tools.html">HarvestTools</a>&nbsp;- Archiving and postprocessing</li>
</ul>
<p><strong>Citation</strong></p>
<blockquote>
<div>Treangen TJ, Ondov BD, Koren S, Phillippy AM. The Harvest suite for rapid core-genome alignment and visualization of thousands of intraspecific microbial genomes. Genome Biology, 15 (11), 1-15 [<a href="http://www.biomedcentral.com/content/pdf/s13059-014-0524-x.pdf">PDF</a>]</div>
</blockquote><p>Address of the bookmark: <a href="http://harvest.readthedocs.io/en/latest/index.html" rel="nofollow">http://harvest.readthedocs.io/en/latest/index.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31018/j-circos</guid>
	<pubDate>Fri, 17 Feb 2017 09:06:54 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31018/j-circos</link>
	<title><![CDATA[J-Circos]]></title>
	<description><![CDATA[<p>Circos plot tool (J-Circos) that is an interactive visualization tool that can plot Circos figures, as well as being able to dynamically add data to the figure, and providing information for specific data points using mouse hover display and zoom in/out functions. J-Circos uses the Java computer language to enable it to be used on most operating systems (Windows, MacOS, Linux). Users can input data into J-Circos using flat data formats, as well as from the GUI. J-Circos will enable biologists to better study more complex chromosomal interactions and fusion transcripts that are otherwise difficult to visualize from next-generation sequencing data.</p><p>Address of the bookmark: <a href="http://www.australianprostatecentre.org/research/software/jcircos" rel="nofollow">http://www.australianprostatecentre.org/research/software/jcircos</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32376/diamond</guid>
	<pubDate>Thu, 27 Apr 2017 04:21:54 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32376/diamond</link>
	<title><![CDATA[DIAMOND]]></title>
	<description><![CDATA[<p><span>DIAMOND is a sequence aligner for protein and translated DNA searches and functions as a drop-in replacement for the NCBI BLAST software tools. It is suitable for protein-protein search as well as DNA-protein search on short reads and longer sequences including contigs and assemblies, providing a speedup of BLAST ranging up to x20,000.</span></p>
<p><span>More at&nbsp;file:///home/urbe/Downloads/diamond_manual.pdf</span></p>
<p><span>http://www.nature.com/nmeth/journal/v12/n1/full/nmeth.3176.html</span></p><p>Address of the bookmark: <a href="https://github.com/bbuchfink/diamond" rel="nofollow">https://github.com/bbuchfink/diamond</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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