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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/30205?offset=310</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27331/andi</guid>
	<pubDate>Fri, 13 May 2016 05:16:35 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27331/andi</link>
	<title><![CDATA[Andi]]></title>
	<description><![CDATA[<p>This is the <code>andi</code> program for estimating the evolutionary distance between closely related genomes. These distances can be used to rapidly infer phylogenies for big sets of genomes. Because <code>andi</code> does not compute full alignments, it is so efficient that it scales even up to thousands of bacterial genomes.</p>
<p>This readme covers all necessary instructions for the impatient to get <code>andi</code> up and running. For extensive instructions please consult the <a href="https://github.com/EvolBioInf/andi/blob/master/andi-manual.pdf">manual</a>.</p>
<p>More at https://github.com/evolbioinf/andi/</p><p>Address of the bookmark: <a href="http://bioinformatics.oxfordjournals.org/content/early/2015/01/13/bioinformatics.btu815.full" rel="nofollow">http://bioinformatics.oxfordjournals.org/content/early/2015/01/13/bioinformatics.btu815.full</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27440/stampy</guid>
	<pubDate>Fri, 20 May 2016 19:13:32 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27440/stampy</link>
	<title><![CDATA[Stampy]]></title>
	<description><![CDATA[<p><strong>Stampy&nbsp;</strong><span>is a package for the mapping of short reads from illumina sequencing machines onto a reference genome. It's recommended for most workflows, including those for genomic resequencing, RNA-Seq and Chip-seq. Stampy excels in the mapping of reads containing that contain sequence variation relative to the reference, in particular for those containing insertions or deletions.</span></p><p>Address of the bookmark: <a href="http://www.well.ox.ac.uk/project-stampy" rel="nofollow">http://www.well.ox.ac.uk/project-stampy</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27973/wgsim</guid>
	<pubDate>Thu, 23 Jun 2016 07:26:49 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27973/wgsim</link>
	<title><![CDATA[WgSim]]></title>
	<description><![CDATA[<p>Reads simulator</p>
<p>Wgsim is a small tool for simulating sequence reads from a reference genome. It is able to simulate diploid genomes with SNPs and insertion/deletion (INDEL) polymorphisms, and simulate reads with uniform substitution sequencing errors. It does not generate INDEL sequencing errors, but this can be partly compensated by simulating INDEL polymorphisms.<br><br>Wgsim outputs the simulated polymorphisms, and writes the true read coordinates as well as the number of polymorphisms and sequencing errors in read names. One can evaluate the accuracy of a mapper or a SNP caller with wgsim_eval.pl that comes with the package.<br><br></p><p>Address of the bookmark: <a href="https://github.com/lh3/wgsim" rel="nofollow">https://github.com/lh3/wgsim</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32152/upsetr-shiny-app</guid>
	<pubDate>Fri, 14 Apr 2017 06:19:54 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32152/upsetr-shiny-app</link>
	<title><![CDATA[UpSetR Shiny App!]]></title>
	<description><![CDATA[<p>UpSetR generates static&nbsp;<a href="http://vcg.github.io/upset/?dataset=0&amp;duration=1000&amp;orderBy=subsetSize&amp;grouping=groupByIntersectionSize&amp;selection=">UpSet plots</a>. The UpSet technique visualizes set intersections in a matrix layout and introduces aggregates based on groupings and queries. The matrix layout enables the effective representation of associated data, such as the number of elements in the aggregates and intersections, as well as additional summary statistics derived from subset or element attributes.</p>
<h4>To begin, input your data using one of the three input styles.</h4>
<ol>
<li>"File" takes a correctly formatted.csv file.</li>
<li>"List" takes up to 6 different lists that contain unique elements, similar to that used in the web applications BioVenn&nbsp;<a href="http://www.biomedcentral.com/content/pdf/1471-2164-9-488.pdf">(Hulsen et al., 2008)</a>&nbsp;and jvenn&nbsp;<a href="http://www.biomedcentral.com/content/pdf/1471-2105-15-293.pdf">(Bardou et al., 2014)</a></li>
<li>"Expression" takes the input used by the venneuler R package&nbsp;<a href="https://cran.r-project.org/web/packages/venneuler/venneuler.pdf">(Wilkinson, 2015)</a></li>
</ol><p>Address of the bookmark: <a href="https://gehlenborglab.shinyapps.io/upsetr/" rel="nofollow">https://gehlenborglab.shinyapps.io/upsetr/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28121/kaiju</guid>
	<pubDate>Mon, 27 Jun 2016 11:23:04 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28121/kaiju</link>
	<title><![CDATA[Kaiju]]></title>
	<description><![CDATA[<p>Kaiju is a program for the taxonomic classification of metagenomic high-throughput sequencing reads. Each read is directly assigned to a taxon within the NCBI taxonomy by comparing it to a reference database containing microbial and viral protein sequences.</p>
<p>By default, Kaiju uses either the available complete genomes from NCBI RefSeq or the microbial subset of the non-redundant protein database <em>nr</em> used by NCBI BLAST, optionally also including fungi and microbial eukaryotes.</p>
<p>Kaiju translates reads into amino acid sequences, which are then searched in the database using a modified backward search on a memory-efficient implementation of the Burrows-Wheeler transform, which finds maximum exact matches (MEMs), optionally allowing mismatches in the protein alignment. The search can process up to millions of reads per minute using, for example, only 10 GB RAM with a protein database comprising 4821 microbial genomes. Kaiju can also be used for querying any other protein database without taxonomic classification, using either protein or nucleotide queries.</p>
<p>Kaiju is described in <a href="http://www.nature.com/ncomms/2016/160413/ncomms11257/full/ncomms11257.html">Menzel, P. et al. (2016) Fast and sensitive taxonomic classification for metagenomics with Kaiju. <em>Nat. Commun.</em> 7:11257</a> (open access).</p><p>Address of the bookmark: <a href="http://kaiju.binf.ku.dk/" rel="nofollow">http://kaiju.binf.ku.dk/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32483/cla-contig-layout-authenticator</guid>
	<pubDate>Fri, 05 May 2017 05:58:36 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32483/cla-contig-layout-authenticator</link>
	<title><![CDATA[CLA: Contig-Layout-Authenticator]]></title>
	<description><![CDATA[<p><span>To improve upon the shortcomings associated with the construction of draft genomes with Illumina paired-end sequencing, we developed Contig-Layout-Authenticator (CLA). The CLA pipeline can scaffold reference-sorted contigs based on paired reads, resulting in better assembled genomes. Moreover, CLA also hints at probable misassemblies and contaminations, for the users to cross-check before constructing the consensus draft. The CLA pipeline was designed and trained extensively on various bacterial genome datasets for the ordering and scaffolding of large repetitive contigs. The tool has been validated and compared favorably with other widely-used scaffolding and ordering tools using both simulated and real sequence datasets. CLA is a user friendly tool that requires a single command line input to generate ordered scaffolds.</span></p>
<p><span>Script&nbsp;https://sourceforge.net/projects/c-l-authenticator/files/</span></p><p>Address of the bookmark: <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0155459" rel="nofollow">http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0155459</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29274/strudel</guid>
	<pubDate>Fri, 30 Sep 2016 09:47:02 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29274/strudel</link>
	<title><![CDATA[Strudel]]></title>
	<description><![CDATA[<p>Strudel is our graphical tool for visualizing genetic and physical maps of genomes for comparative purposes. The application aims to let the user examine their data at a variety of different levels of resolution, from entire maps to individual markers, and explore syntenic relationships between genomes. All browsing and interaction with Strudel happens in real-time &ndash; there is no need to wait while the maps are generated. It is built using Java 1.6 and ships with its own JRE, so there is no need for users to install or update Java.</p><p>Address of the bookmark: <a href="https://ics.hutton.ac.uk/strudel/" rel="nofollow">https://ics.hutton.ac.uk/strudel/</a></p>]]></description>
	<dc:creator>Anjana</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34715/delta-a-new-web-based-3d-genome-visualization-and-analysis-platform</guid>
	<pubDate>Wed, 20 Dec 2017 08:49:55 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34715/delta-a-new-web-based-3d-genome-visualization-and-analysis-platform</link>
	<title><![CDATA[Delta: a new Web-based 3D genome visualization and analysis platform]]></title>
	<description><![CDATA[<p><em>Delta</em><span>&nbsp;is an integrative visualization and analysis platform to facilitate visually annotating and exploring the 3D physical architecture of genomes.&nbsp;</span><em>Delta</em><span>&nbsp;takes Hi-C or ChIA-PET contact matrix as input and predicts the topologically associating domains and chromatin loops in the genome. It then generates a physical 3D model which represents the plausible consensus 3D structure of the genome.&nbsp;</span><em>Delta</em><span>features a highly interactive visualization tool which enhances the integration of genome topology/physical structure with extensive genome annotation by juxtaposing the 3D model with diverse genomic assay outputs.</span></p>
<p>https://github.com/zhangzhwlab/delta</p><p>Address of the bookmark: <a href="https://github.com/zhangzhwlab/delta" rel="nofollow">https://github.com/zhangzhwlab/delta</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35384/mgcv-the-microbial-genomic-context-viewer-for-comparative-genome-analysis</guid>
	<pubDate>Mon, 29 Jan 2018 04:55:46 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35384/mgcv-the-microbial-genomic-context-viewer-for-comparative-genome-analysis</link>
	<title><![CDATA[MGcV: the microbial genomic context viewer for comparative genome analysis]]></title>
	<description><![CDATA[<p><span>MGcV is an interactive web-based visalization tool tailored to facilitate small scale genome analysis. To start using MGcV:</span></p>
<ol>
<li>Supply your genes/genomic segments/phylogenetic tree of interest in the input-box by
<ul>
<li>selecting the type of identifier and pasting identifiers (one per line)</li>
<li><em><strong>or</strong></em>&nbsp;by using the&nbsp;<a>gene ID search tool</a></li>
<li><em><strong>or</strong></em>&nbsp;with the&nbsp;<a>BLAST search tool</a></li>
</ul>
</li>
<li>Click "Visualize context".</li>
</ol>
<p><span>Consult the&nbsp;</span><a href="http://mgcv.cmbi.ru.nl/help.html" target="_blank">documentation</a><span>&nbsp;to learn more about MGcV.</span></p><p>Address of the bookmark: <a href="http://mgcv.cmbi.ru.nl/" rel="nofollow">http://mgcv.cmbi.ru.nl/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29614/art-set-of-simulation-tools</guid>
	<pubDate>Thu, 03 Nov 2016 08:28:25 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29614/art-set-of-simulation-tools</link>
	<title><![CDATA[ART: Set of Simulation Tools]]></title>
	<description><![CDATA[<p>ART is a set of simulation tools to generate synthetic next-generation sequencing reads. ART simulates sequencing reads by mimicking real sequencing process with empirical error models or quality profiles summarized from large recalibrated sequencing data. ART can also simulate reads using user own read error model or quality profiles. ART supports simulation of single-end, paired-end/mate-pair reads of three major commercial next-generation sequencing platforms: Illumina's Solexa, Roche's 454 and Applied Biosystems' SOLiD. ART can be used to test or benchmark a variety of method or tools for next-generation sequencing data analysis, including read alignment, de novo assembly, SNP and structure variation discovery. ART was used as a primary tool for the simulation study of the <span><a href="http://www.1000genomes.org/" target="_blank">1000 Genomes Project<span></span></a></span> . ART is implemented in C++ with optimized algorithms and is highly efficient in read simulation. ART outputs reads in the FASTQ format, and alignments in the ALN format. ART can also generate alignments in the SAM alignment or UCSC BED file format. ART can be used together with genome variants simulators (e.g. <span><a href="http://bioinform.github.io/varsim/" target="_blank">VarSim<span></span></a></span>) for evaluating variant calling tools or methods.</p><p>Address of the bookmark: <a href="http://www.niehs.nih.gov/research/resources/software/biostatistics/art/" rel="nofollow">http://www.niehs.nih.gov/research/resources/software/biostatistics/art/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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