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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/43888?offset=60</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36518/mix-combining-multiple-assemblies-from-ngs-data</guid>
	<pubDate>Tue, 08 May 2018 04:58:05 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36518/mix-combining-multiple-assemblies-from-ngs-data</link>
	<title><![CDATA[MIX: Combining multiple assemblies from NGS data]]></title>
	<description><![CDATA[<p>Mix is a tool that combines two or more draft assemblies, without relying on a reference genome and has the goal to reduce contig fragmentation and thus speed-up genome finishing. The proposed algorithm builds an extension graph where vertices represent extremities of contigs and edges represent existing alignments between these extremities. These alignment edges are used for contig extension. The resulting output assembly corresponds to a path in the extension graph that maximizes the cumulative contig length.</p>
<p>The Mix algorithm, approach and results were published in BMC bioinformatics :&nbsp;<a href="http://www.biomedcentral.com/1471-2105/14/S15/S16">http://www.biomedcentral.com/1471-2105/14/S15/S16</a>.</p><p>Address of the bookmark: <a href="https://github.com/cbib/MIX" rel="nofollow">https://github.com/cbib/MIX</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37414/arc-pipeline-which-facilitates-iterative-reference-guided-de-novo-assemblies</guid>
	<pubDate>Thu, 26 Jul 2018 09:20:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37414/arc-pipeline-which-facilitates-iterative-reference-guided-de-novo-assemblies</link>
	<title><![CDATA[ARC: pipeline which facilitates iterative, reference guided de novo assemblies]]></title>
	<description><![CDATA[<p>ARC is a pipeline which facilitates iterative, reference guided&nbsp;<em>de novo</em>&nbsp;assemblies with the intent of:</p>
<ol>
<li>Reducing time in analysis and increasing accuracy of results by only considering those reads which should assemble together.</li>
<li>Reducing/removing reference bias as compared to mapping based approaches.</li>
</ol>
<p><span>The software is designed to work in situations where a whole-genome assembly is not the objective, but rather when the researcher wishes to assemble discreet 'targets' contained within next-generation shotgun sequence data. ARC decomplexifies the traditionally difficult problem of assembly by breaking the reads into small, manageable subsets which can then be assembled quickly and efficiently in parallel. Applications include those in which the researcher wishes to&nbsp;</span><em>de novo</em><span>&nbsp;assemble specific content and a set of semi-similar reference targets is available to initialize the assembly process.</span></p>
<p>https://ibest.github.io/ARC/</p><p>Address of the bookmark: <a href="https://ibest.github.io/ARC/" rel="nofollow">https://ibest.github.io/ARC/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38210/skesa-strategic-k-mer-extension-for-scrupulous-assemblies</guid>
	<pubDate>Wed, 14 Nov 2018 04:45:41 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38210/skesa-strategic-k-mer-extension-for-scrupulous-assemblies</link>
	<title><![CDATA[SKESA: strategic k-mer extension for scrupulous assemblies]]></title>
	<description><![CDATA[<p><span>SKESA is a DeBruijn graph-based de-novo assembler designed for assembling reads of microbial genomes sequenced using Illumina. Comparison with SPAdes and MegaHit shows that SKESA produces assemblies that have high sequence quality and contiguity, handles low-level contamination in reads, is fast, and produces an identical assembly for the same input when assembled multiple times with the same or different compute resources. </span></p>
<p><span>Source code for SKESA is freely available at&nbsp;</span><span><a href="https://github.com/ncbi/SKESA/releases"><span>https://github.com/ncbi/SKESA/releases</span></a></span><span>.</span></p>
<p>Research Paper&nbsp;@ <a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-018-1540-z">Link</a></p>
<p><span><span>SKESA algorithm are as follows:</span><br></span></p>
<p><span><img src="https://media.springernature.com/lw785/springer-static/image/art%3A10.1186%2Fs13059-018-1540-z/MediaObjects/13059_2018_1540_Fig4_HTML.png" alt="image" width="785" height="984" style="border: 0px; border: 0px;"></span></p><p>Address of the bookmark: <a href="https://github.com/ncbi/SKESA/releases" rel="nofollow">https://github.com/ncbi/SKESA/releases</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38561/hawkeye-an-interactive-visual-analytics-tool-for-genome-assemblies</guid>
	<pubDate>Tue, 01 Jan 2019 11:56:17 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38561/hawkeye-an-interactive-visual-analytics-tool-for-genome-assemblies</link>
	<title><![CDATA[Hawkeye: an interactive visual analytics tool for genome assemblies]]></title>
	<description><![CDATA[<p><span>Genome sequencing remains an inexact science, and genome sequences can contain significant errors if they are not carefully examined. Hawkeye is our new visual analytics tool for genome assemblies, designed to aid in identifying and correcting assembly errors. Users can analyze all levels of an assembly along with summary statistics and assembly metrics, and are guided by a ranking component towards likely mis-assemblies. Hawkeye is freely available and released as part of the open source AMOS project&nbsp;</span><span><a href="http://amos.sourceforge.net/hawkeye"><span>http://amos.sourceforge.net/hawkeye</span></a></span><span>.</span></p>
<p>https://genomebiology.biomedcentral.com/articles/10.1186/gb-2007-8-3-r34</p><p>Address of the bookmark: <a href="http://amos.sourceforge.net/wiki/index.php?title=Hawkeye" rel="nofollow">http://amos.sourceforge.net/wiki/index.php?title=Hawkeye</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43722/crossmap-program-for-genome-coordinates-conversion-between-different-assemblies</guid>
	<pubDate>Tue, 25 Jan 2022 17:59:32 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43722/crossmap-program-for-genome-coordinates-conversion-between-different-assemblies</link>
	<title><![CDATA[CrossMap: program for genome coordinates conversion between different assemblies]]></title>
	<description><![CDATA[<p><span>CrossMap is a program for genome coordinates conversion between&nbsp;</span><em>different assemblies</em><span>&nbsp;(such as&nbsp;</span><a href="http://www.ncbi.nlm.nih.gov/assembly/2928/">hg18 (NCBI36)</a><span>&nbsp;&lt;=&gt;&nbsp;</span><a href="http://www.ncbi.nlm.nih.gov/assembly/2758/">hg19 (GRCh37)</a><span>). It supports commonly used file formats including&nbsp;</span><a href="https://samtools.github.io/hts-specs/SAMv1.pdf">BAM</a><span>,&nbsp;</span><a href="https://en.wikipedia.org/wiki/CRAM_(file_format)">CRAM</a><span>,&nbsp;</span><a href="https://en.wikipedia.org/wiki/SAM_(file_format)">SAM</a><span>,&nbsp;</span><a href="https://genome.ucsc.edu/goldenPath/help/wiggle.html">Wiggle</a><span>,&nbsp;</span><a href="https://genome.ucsc.edu/goldenPath/help/bigWig.html">BigWig</a><span>,&nbsp;</span><a href="https://genome.ucsc.edu/FAQ/FAQformat.html#format1">BED</a><span>,&nbsp;</span><a href="https://genome.ucsc.edu/FAQ/FAQformat.html#format3">GFF</a><span>,&nbsp;</span><a href="https://genome.ucsc.edu/FAQ/FAQformat.html#format4">GTF</a><span>,&nbsp;</span><a href="https://docs.gdc.cancer.gov/Data/File_Formats/MAF_Format/">MAF</a><span>&nbsp;</span><a href="https://samtools.github.io/hts-specs/VCFv4.2.pdf">VCF</a><span>, and&nbsp;</span><a href="https://sites.google.com/site/gvcftools/home/about-gvcf">gVCF</a><span>.</span></p><p>Address of the bookmark: <a href="http://crossmap.sourceforge.net/" rel="nofollow">http://crossmap.sourceforge.net/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44414/reconplot-an-r-package-for-the-visualization-and-interpretation-of-genomic-rearrangements</guid>
	<pubDate>Thu, 14 Dec 2023 12:33:19 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44414/reconplot-an-r-package-for-the-visualization-and-interpretation-of-genomic-rearrangements</link>
	<title><![CDATA[ReConPlot: an R package for the visualization and interpretation of genomic rearrangements]]></title>
	<description><![CDATA[<p>ReConPlot (REarrangement and COpy Number PLOT), an R package that provides functionalities for the joint visualization of SCNAs and SVs across one or multiple chromosomes. ReConPlot is based on the popular ggplot2 package, thus allowing customization of plots and the generation of publication-quality figures with minimal effort.</p><p>Address of the bookmark: <a href="https://academic.oup.com/bioinformatics/article/39/12/btad719/7460198?login=false" rel="nofollow">https://academic.oup.com/bioinformatics/article/39/12/btad719/7460198?login=false</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37498/nextsv-a-meta-caller-for-structural-variants-from-low-coverage-long-read-sequencing-data</guid>
	<pubDate>Mon, 06 Aug 2018 17:24:53 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37498/nextsv-a-meta-caller-for-structural-variants-from-low-coverage-long-read-sequencing-data</link>
	<title><![CDATA[NextSV: a meta-caller for structural variants from low-coverage long-read sequencing data]]></title>
	<description><![CDATA[<p>NextSV, a meta SV caller and a computational pipeline to perform SV calling from low coverage long-read sequencing data. NextSV integrates three aligners and three SV callers and generates two integrated call sets (sensitive/stringent) for different analysis purpose. The output of NextSV is in ANNOVAR-compatible bed format. Users can easily perform downstream annotation using ANNOVAR and disease gene discovery using Phenolyzer.</p>
<p>&nbsp;</p>
<h2>&nbsp;</h2><p>Address of the bookmark: <a href="https://github.com/Nextomics/NextSV" rel="nofollow">https://github.com/Nextomics/NextSV</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39253/gmass-a-novel-measure-for-genomeassembly-structural-similarity</guid>
	<pubDate>Sun, 14 Apr 2019 20:35:40 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39253/gmass-a-novel-measure-for-genomeassembly-structural-similarity</link>
	<title><![CDATA[GMASS: a novel measure for genomeassembly structural similarity]]></title>
	<description><![CDATA[<div id="Abstract">
<div id="ASec3">
<p id="Par3">The GMASS score is a novel measure for representing structural similarity between two assemblies. It will contribute to the understanding of assembly output and developing de novo assemblers.</p>
<p><a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2710-z">https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2710-z</a></p>
</div>
</div><p>Address of the bookmark: <a href="http://bioinfo.konkuk.ac.kr/GMASS/htdocs/syncircos.php" rel="nofollow">http://bioinfo.konkuk.ac.kr/GMASS/htdocs/syncircos.php</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42354/vsfilt-a-tool-to-improve-virtual-screening-by-structural-filtration-of-docking-poses</guid>
	<pubDate>Wed, 25 Nov 2020 02:39:16 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42354/vsfilt-a-tool-to-improve-virtual-screening-by-structural-filtration-of-docking-poses</link>
	<title><![CDATA[vsFilt: A tool to improve virtual screening by structural filtration of docking poses]]></title>
	<description><![CDATA[<p><span>The vsFilt is the first open application for post-docking structural filtration, available as a web-server. The new tool is easy to use and configure to detect a wide range of interaction types that are known to be involved in molecular recognition, including hydrogen and halogen bonds, ionic interactions, hydrophobic contacts, &pi;-stacking, and cation-&pi; interactions. The web-server can process large libraries of up to 150&rsquo;000 docked ligand poses. The results are web-based and can be operated on-line using the built-in HTML5 interactive analysis tools, or can be downloaded for a local use. The vsFilt is freely available on-line, no login required.</span></p><p>Address of the bookmark: <a href="https://biokinet.belozersky.msu.ru/vsfilt" rel="nofollow">https://biokinet.belozersky.msu.ru/vsfilt</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44501/minda-a-tool-for-evaluating-structural-variant-sv-callers</guid>
	<pubDate>Sun, 31 Mar 2024 02:43:50 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44501/minda-a-tool-for-evaluating-structural-variant-sv-callers</link>
	<title><![CDATA[Minda: a tool for evaluating structural variant (SV) callers]]></title>
	<description><![CDATA[<p dir="auto">Minda is a tool for evaluating structural variant (SV) callers that</p>
<ul dir="auto">
<li>standardizes VCF records for compatibility with both germline and somatic SV callers,</li>
<li>benchmarks against a single VCF input file, or</li>
<li>benchmarks against an ensemble call set created from multiple VCF input files.</li>
</ul><p>Address of the bookmark: <a href="https://github.com/KolmogorovLab/minda" rel="nofollow">https://github.com/KolmogorovLab/minda</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>

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