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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/44659?offset=230</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41916/truvari-structural-variant-comparison-tool-for-vcfs</guid>
	<pubDate>Tue, 30 Jun 2020 21:30:44 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41916/truvari-structural-variant-comparison-tool-for-vcfs</link>
	<title><![CDATA[truvari: Structural variant comparison tool for VCFs]]></title>
	<description><![CDATA[<p>Structural variant comparison tool for VCFs</p>
<p>Given benchmark and comparsion sets of SVs, calculate the recall, precision, and f-measure.</p>
<p><a href="https://github.com/spiralgenetics/www.spiralgenetics.com">Spiral Genetics</a></p>
<p><a href="https://docs.google.com/presentation/d/17mvC1XOpOm7khAbZwF3SgtG2Rl4M9Mro37yF2nN7GhE/edit">Motivation</a></p><p>Address of the bookmark: <a href="https://github.com/spiralgenetics/truvari" rel="nofollow">https://github.com/spiralgenetics/truvari</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42143/sibelia-a-comparative-genomics-tool</guid>
	<pubDate>Sat, 22 Aug 2020 02:49:00 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42143/sibelia-a-comparative-genomics-tool</link>
	<title><![CDATA[Sibelia: A comparative genomics tool]]></title>
	<description><![CDATA[<p><strong>Sibelia</strong>: A comparative genomics tool: It assists biologists in analysing the genomic variations that correlate with pathogens, or the genomic changes that help microorganisms adapt in different environments. Sibelia will also be helpful for the evolutionary and genome rearrangement studies for multiple strains of microorganisms.&nbsp;</p>
<p><strong>Sibelia</strong>&nbsp;is useful in finding: (1) shared regions, (2) regions that present in one group of genomes but not in others, (3) rearrangements that transform one genome to other genomes.</p>
<p>More at&nbsp;<a href="http://bioinf.spbau.ru/sibelia">http://bioinf.spbau.ru/sibelia</a></p>
<p>Sibelia docs&nbsp;<a href="http://gensoft.pasteur.fr/docs/Sibelia/3.0.7/SIBELIA.md">http://gensoft.pasteur.fr/docs/Sibelia/3.0.7/SIBELIA.md</a></p><p>Address of the bookmark: <a href="https://github.com/bioinf/Sibelia" rel="nofollow">https://github.com/bioinf/Sibelia</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43120/ventoy-an-open-source-tool-to-create-bootable-usb-drive</guid>
	<pubDate>Tue, 29 Jun 2021 10:16:19 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43120/ventoy-an-open-source-tool-to-create-bootable-usb-drive</link>
	<title><![CDATA[Ventoy: an open source tool to create bootable USB drive]]></title>
	<description><![CDATA[<p>Ventoy is an open source tool to create bootable USB drive for ISO/WIM/IMG/VHD(x)/EFI files. With ventoy, you don't need to format the disk over and over, you just need to copy the image files to the USB drive and boot it. You can copy many image files at a time and ventoy will give you a boot menu to select them. x86 Legacy BIOS, IA32 UEFI, x86_64 UEFI, ARM64 UEFI and MIPS64EL UEFI are supported in the same way. Both MBR and GPT partition style are supported in the same way. Most type of OS supported(Windows/WinPE/Linux/Unix/Vmware/Xen...) 700+ ISO files are tested.&nbsp;</p><p>Address of the bookmark: <a href="https://github.com/ventoy/Ventoy" rel="nofollow">https://github.com/ventoy/Ventoy</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43902/interactivenn-a-web-based-tool-for-the-analysis-of-sets-through-venn-diagrams</guid>
	<pubDate>Wed, 29 Jun 2022 03:22:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43902/interactivenn-a-web-based-tool-for-the-analysis-of-sets-through-venn-diagrams</link>
	<title><![CDATA[InteractiVenn: a web-based tool for the analysis of sets through Venn diagrams]]></title>
	<description><![CDATA[<p><span>InteractiVenn, a more flexible tool for interacting with Venn diagrams including up to six sets. It offers a clean interface for Venn diagram construction and enables analysis of set unions while preserving the shape of the diagram. Set unions are useful to reveal differences and similarities among sets and may be guided in our tool by a tree or by a list of set unions. The tool also allows obtaining subsets&rsquo; elements, saving and loading sets for further analyses, and exporting the diagram in vector and image formats. InteractiVenn has been used to analyze two biological datasets, but it may serve set analysis in a broad range of domains.</span></p>
<p><span>More at&nbsp;https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-015-0611-3</span></p>
<p><span><img src="https://media.springernature.com/lw685/springer-static/image/art%3A10.1186%2Fs12859-015-0611-3/MediaObjects/12859_2015_611_Fig1_HTML.gif?as=webp" alt="image" style="border: 0px;"></span></p><p>Address of the bookmark: <a href="http://www.interactivenn.net/" rel="nofollow">http://www.interactivenn.net/</a></p>]]></description>
	<dc:creator>Jit</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>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44661/lovis4u-locus-visualisation-tool-for-comparative-genomics</guid>
	<pubDate>Tue, 17 Sep 2024 02:30:57 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44661/lovis4u-locus-visualisation-tool-for-comparative-genomics</link>
	<title><![CDATA[LoVis4u: Locus Visualisation tool for comparative genomics]]></title>
	<description><![CDATA[<p dir="auto"><a href="https://github.com/art-egorov/lovis4u/blob/main/docs/img/lovis4u_logo.png" target="_blank"><img src="https://github.com/art-egorov/lovis4u/raw/main/docs/img/lovis4u_logo.png" alt="image" width="300" style="border: 0px; border: 0px;"></a></p>
<div dir="auto">
<h2 dir="auto">Description</h2>
<a href="https://github.com/art-egorov/lovis4u#description"></a></div>
<p dir="auto"><span>LoVis4u</span>&nbsp;is a bioinformatics tool for&nbsp;<span>Lo</span>ci&nbsp;<span>Vis</span>ualisation.</p>
<p dir="auto"><span>LoVis4u, a command-line tool and Python API designed for highly customizable and fast visualisation of multiple genomic loci. LoVis4u generates vector images in PDF format based on annotation data from GenBank or GFF files. It is capable of visualising entire genomes of bacteriophages as well as plasmids and user-defined regions of longer prokaryotic genomes. Additionally, LoVis4u offers optional data processing steps to identify and highlight accessory and core genes in input sequences.</span></p>
<p dir="auto">https://art-egorov.github.io/lovis4u/</p>
<p dir="auto">&nbsp;</p><p>Address of the bookmark: <a href="https://github.com/art-egorov/lovis4u" rel="nofollow">https://github.com/art-egorov/lovis4u</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36830/crossmap-a-program-for-convenient-conversion-of-genome-coordinates</guid>
	<pubDate>Thu, 31 May 2018 06:00:47 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36830/crossmap-a-program-for-convenient-conversion-of-genome-coordinates</link>
	<title><![CDATA[CrossMap: a program for convenient conversion of genome coordinates]]></title>
	<description><![CDATA[CrossMap is a program for convenient conversion of genome coordinates (or annotation files) between different assemblies (such as Human hg18 (NCBI36) &lt;&gt; hg19 (GRCh37), Mouse mm9 (MGSCv37) &lt;&gt; mm10 (GRCm38)).

It supports most commonly used file formats including SAM/BAM, Wiggle/BigWig, BED, GFF/GTF, VCF.

CrossMap is designed to liftover genome coordinates between assemblies. 

It’s not a program for aligning sequences to reference genome.

We do not recommend using CrossMap to convert genome coordinates between species.<p>Address of the bookmark: <a href="http://crossmap.sourceforge.net" rel="nofollow">http://crossmap.sourceforge.net</a></p>]]></description>
	<dc:creator>Jit</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/38623/kallisto-a-program-for-quantifying-abundances-of-transcripts-from-bulk-and-single-cell-rna-seq-data</guid>
	<pubDate>Mon, 07 Jan 2019 10:35:14 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38623/kallisto-a-program-for-quantifying-abundances-of-transcripts-from-bulk-and-single-cell-rna-seq-data</link>
	<title><![CDATA[kallisto: a program for quantifying abundances of transcripts from bulk and single-cell RNA-Seq data]]></title>
	<description><![CDATA[<p><strong>kallisto</strong>&nbsp;is a program for quantifying abundances of transcripts from bulk and single-cell RNA-Seq data, or more generally of target sequences using high-throughput sequencing reads. It is based on the novel idea of&nbsp;<em>pseudoalignment</em>&nbsp;for rapidly determining the compatibility of reads with targets, without the need for alignment. On benchmarks with standard RNA-Seq data,&nbsp;<strong>kallisto</strong>&nbsp;can quantify 30 million human reads in less than 3 minutes on a Mac desktop computer using only the read sequences and a transcriptome index that itself takes less than 10 minutes to build. Pseudoalignment of reads preserves the key information needed for quantification, and&nbsp;<strong>kallisto</strong>&nbsp;is therefore not only fast, but also as accurate as existing quantification tools. In fact, because the pseudoalignment procedure is robust to errors in the reads, in many benchmarks&nbsp;<strong>kallisto</strong>&nbsp;significantly outperforms existing tools.&nbsp;<strong>kallisto</strong>&nbsp;is described in detail in:</p>
<p>Nicolas L Bray, Harold Pimentel, P&aacute;ll Melsted and Lior Pachter,&nbsp;<a href="http://www.nature.com/nbt/journal/v34/n5/full/nbt.3519.html">Near-optimal probabilistic RNA-seq quantification</a>, Nature Biotechnology&nbsp;<strong>34</strong>, 525&ndash;527 (2016), doi:10.1038/nbt.3519</p><p>Address of the bookmark: <a href="https://pachterlab.github.io/kallisto/about" rel="nofollow">https://pachterlab.github.io/kallisto/about</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40525/heatmaply-popular-graphical-method-for-visualizing-high-dimensional-data</guid>
	<pubDate>Sat, 11 Jan 2020 07:34:14 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40525/heatmaply-popular-graphical-method-for-visualizing-high-dimensional-data</link>
	<title><![CDATA[heatmaply: popular graphical method for visualizing high-dimensional data]]></title>
	<description><![CDATA[<p>This work is based on ggplot2 and plotly.js engine. It produces similar heatmaps as d3heatmap, with the advantage of speed (plotly.js is able to handle larger size matrix), and the ability to zoom from the dendrogram.</p>
<p>heatmaply also provides an interface based around the&nbsp;<a href="https://cran.r-project.org/package=plotly">plotly R package</a>. This interface can be used by choosing&nbsp;<code>plot_method = "plotly"</code>&nbsp;instead of the default&nbsp;<code>plot_method = "ggplot"</code>. This interface can provide smaller objects and faster rendering to disk in many cases and provides otherwise almost identical features.</p>
<p>Documentation for this package is also available as a&nbsp;<a href="https://cran.r-project.org/package=pkgdown">pkgdown</a>&nbsp;site:&nbsp;<a href="http://talgalili.github.io/heatmaply/">http://talgalili.github.io/heatmaply/</a></p><p>Address of the bookmark: <a href="http://talgalili.github.io/heatmaply/articles/heatmaply.html" rel="nofollow">http://talgalili.github.io/heatmaply/articles/heatmaply.html</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>

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