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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/39039?offset=30</link>
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	<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/40969/leaflet-javascript-libraries-for-interactive-maps</guid>
	<pubDate>Mon, 10 Feb 2020 01:35:07 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40969/leaflet-javascript-libraries-for-interactive-maps</link>
	<title><![CDATA[Leaflet: JavaScript libraries for interactive maps]]></title>
	<description><![CDATA[<p><a href="http://leafletjs.com/">Leaflet</a><span>&nbsp;is one of the most popular open-source JavaScript libraries for interactive maps.</span></p>
<h3>Features</h3>
<ul>
<li>Interactive panning/zooming</li>
<li>Compose maps using arbitrary combinations of:
<ul>
<li>Map tiles</li>
<li>Markers</li>
<li>Polygons</li>
<li>Lines</li>
<li>Popups</li>
<li>GeoJSON</li>
</ul>
</li>
<li>Create maps right from the R console or RStudio</li>
<li>Embed maps in&nbsp;<a href="http://yihui.name/knitr/">knitr</a>/<a href="http://rmarkdown.rstudio.com/">R Markdown</a>&nbsp;documents and&nbsp;<a href="http://shiny.rstudio.com/">Shiny</a>&nbsp;apps</li>
<li>Easily render spatial objects from the&nbsp;<code>sp</code>&nbsp;or&nbsp;<code>sf</code>&nbsp;packages, or data frames with latitude/longitude columns</li>
<li>Use map bounds and mouse events to drive Shiny logic</li>
<li>Display maps in non spherical mercator projections</li>
<li>Augment map features using chosen plugins from&nbsp;<a href="http://leafletjs.com/plugins">leaflet plugins repository</a></li>
</ul>
<p><a href="https://rstudio.github.io/leaflet/">https://rstudio.github.io/leaflet/</a></p><p>Address of the bookmark: <a href="https://rstudio.github.io/leaflet/" rel="nofollow">https://rstudio.github.io/leaflet/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43859/mumco-is-a-simple-bash-script-that-uses-whole-genome-alignment-information-provided-by-mummer-v4-to-detect-variants</guid>
	<pubDate>Wed, 27 Apr 2022 04:34:12 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43859/mumco-is-a-simple-bash-script-that-uses-whole-genome-alignment-information-provided-by-mummer-v4-to-detect-variants</link>
	<title><![CDATA[MUM&amp;Co is a simple bash script that uses Whole Genome Alignment information provided by MUMmer (v4) to detect variants.]]></title>
	<description><![CDATA[<p dir="auto">MUM&amp;Co is able to detect:<br>Deletions, insertions, tandem duplications and tandem contractions (&gt;=50bp &amp; &lt;=150kb)<br>Inversions (&gt;=1kb) and translocations (&gt;=10kb)</p><p>Address of the bookmark: <a href="https://github.com/SAMtoBAM/MUMandCo" rel="nofollow">https://github.com/SAMtoBAM/MUMandCo</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31100/vaguevelvet-assembler-graphical-front-end</guid>
	<pubDate>Fri, 24 Feb 2017 08:56:49 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31100/vaguevelvet-assembler-graphical-front-end</link>
	<title><![CDATA[VAGUE:Velvet Assembler Graphical Front End]]></title>
	<description><![CDATA[<p>VAGUE is a vague acronym for "Velvet Assembler Graphical Front End", which means it is a GUI for the Velvet <em>de novo</em> assembler. The command line version of Velvet can be complicated for beginners to use, but VAGUE makes it clear and simple</p>
<p>More at&nbsp;http://www.vicbioinformatics.com/software.vague.shtml</p><p>Address of the bookmark: <a href="http://www.vicbioinformatics.com/software.vague.shtml" rel="nofollow">http://www.vicbioinformatics.com/software.vague.shtml</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29276/murasaki</guid>
	<pubDate>Fri, 30 Sep 2016 10:22:30 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29276/murasaki</link>
	<title><![CDATA[Murasaki]]></title>
	<description><![CDATA[<p>Murasaki is an anchor alignment program that is</p>
<ul style="margin-left: 16px;">
<li>exteremely fast (17 CPU hours for whole Human x Mouse genome (with 40 nodes: 35 wall minutes), or 8 mammals in 21 CPU hours (42 wall minutes))</li>
<li>scalable (Arbitrarily parallelizable across multiple nodes using MPI)</li>
<li>memory efficient. (Even a single node with 16GB of ram can handle over 1Gbp of sequence)</li>
<li>unlimited by pattern length or selection</li>
<li>repeat tolerant</li>
</ul>
<p><img src="http://murasaki.dna.bio.keio.ac.jp/9mammals-small.png" width="500" height="375" alt="image" style="border: 0px;"></p><p>Address of the bookmark: <a href="http://murasaki.dna.bio.keio.ac.jp/wiki/index.php?Murasaki" rel="nofollow">http://murasaki.dna.bio.keio.ac.jp/wiki/index.php?Murasaki</a></p>]]></description>
	<dc:creator>Anjana</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30966/maftools</guid>
	<pubDate>Thu, 16 Feb 2017 11:16:01 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30966/maftools</link>
	<title><![CDATA[MafTools]]></title>
	<description><![CDATA[<p>maftools - An R package to summarize, analyze and visualize MAF files. <a href="https://github.com/PoisonAlien/maftools#introduction"></a>Introduction.</p>
<p>With advances in Cancer Genomics, Mutation Annotation Format (MAF) is being widley accepted and used to store variants detected. <a href="http://cancergenome.nih.gov">The Cancer Genome Atlas</a> Project has seqenced over 30 different cancers with sample size of each cancer type being over 200. The <a href="https://wiki.nci.nih.gov/display/TCGA/TCGA+MAF+Files">resulting data</a> consisting of genetic variants is stored in the form of <a href="https://wiki.nci.nih.gov/display/TCGA/Mutation+Annotation+Format+%28MAF%29+Specification">Mutation Annotation Format</a>. This package attempts to summarize, analyze, annotate and visualize MAF files in an efficient manner either from TCGA sources or any in-house studies as long as the data is in MAF format. Maftools can also handle ICGC Simple Somatic Mutation format.</p>
<p>maftools is on <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f449.png" alt=":point_right:" width="20" height="20" style="border: 0px;"> <a href="http://biorxiv.org/content/early/2016/05/11/052662">bioRxiv</a> <img src="https://assets-cdn.github.com/images/icons/emoji/bowtie.png" alt=":bowtie:" title=":bowtie:" width="20" height="20" style="border: 0px; text-align: absmiddle;"></p>
<p>Please cite the below if you find this tool useful for you.</p>
<p>Mayakonda, A. and H.P. Koeffler, Maftools: Efficient analysis, visualization and summarization of MAF files from large-scale cohort based cancer studies. bioRxiv, 2016. doi: <a href="http://dx.doi.org/10.1101/052662">http://dx.doi.org/10.1101/052662</a></p><p>Address of the bookmark: <a href="https://github.com/PoisonAlien/maftools" rel="nofollow">https://github.com/PoisonAlien/maftools</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31371/phenogram</guid>
	<pubDate>Tue, 07 Mar 2017 08:35:12 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31371/phenogram</link>
	<title><![CDATA[PhenoGram]]></title>
	<description><![CDATA[<p><span>With PhenoGram researchers can create chomosomal ideograms annotated with lines in color at specific base-pair locations, or colored base-pair to base-pair regions, with or without other annotation. PhenoGram allows for annotation of chromosomal locations and/or regions with shapes in different colors, gene identifiers, or other text. PhenoGram also allows for creation of plots showing expanded chromosomal locations, providing a way to show results for specific chromosomal regions in greater detail.</span></p><p>Address of the bookmark: <a href="http://ritchielab.psu.edu/software/phenogram-downloads" rel="nofollow">http://ritchielab.psu.edu/software/phenogram-downloads</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36109/sankeynetwork-with-networkd3</guid>
	<pubDate>Fri, 06 Apr 2018 12:07:55 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36109/sankeynetwork-with-networkd3</link>
	<title><![CDATA[sankeyNetwork with networkD3]]></title>
	<description><![CDATA[<p><span>You can also create&nbsp;</span><a href="http://en.wikipedia.org/wiki/Sankey_diagram">Sankey diagrams</a><span>&nbsp;with&nbsp;</span><code>sankeyNetwork</code><span>. Here is an example using downloaded JSON data:</span></p>
<p><span>https://en.wikipedia.org/wiki/Sankey_diagram</span></p><p>Address of the bookmark: <a href="https://christophergandrud.github.io/networkD3/#sankey" rel="nofollow">https://christophergandrud.github.io/networkD3/#sankey</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38004/vcfr-a-package-to-manipulate-and-visualize-vcf-data-in-r</guid>
	<pubDate>Thu, 25 Oct 2018 09:05:59 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38004/vcfr-a-package-to-manipulate-and-visualize-vcf-data-in-r</link>
	<title><![CDATA[vcfR:  a package to manipulate and visualize VCF data in R]]></title>
	<description><![CDATA[<p><span>VcfR is an R package intended to allow easy manipulation and visualization of variant call format (VCF) data. Functions are provided to rapidly read from and write to VCF files. Once VCF data is read into R a parser function extracts matrices from the VCF data for use with typical R functions. This information can then be used for quality control or other purposes. Additional functions provide visualization of genomic data. Once processing is complete data may be written to a VCF file or converted into other popular R objects (e.g., genlight, DNAbin). VcfR provides a link between VCF data and the R environment connecting familiar software with genomic data.</span></p><p>Address of the bookmark: <a href="https://github.com/knausb/vcfR" rel="nofollow">https://github.com/knausb/vcfR</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41487/tinycov-standalone-command-line-utility-written-in-python-to-plot-coverage-from-a-bam-file</guid>
	<pubDate>Mon, 23 Mar 2020 06:22:08 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41487/tinycov-standalone-command-line-utility-written-in-python-to-plot-coverage-from-a-bam-file</link>
	<title><![CDATA[tinycov: standalone command line utility written in python to plot coverage from a BAM file]]></title>
	<description><![CDATA[<p>Tinycov is a small standalone command line utility written in python to plot the coverage of a BAM file quickly. This software was inspired by&nbsp;<a href="https://github.com/matted/genome_coverage_plotter">Matt Edwards' genome coverage plotter</a>.</p>
<p>To install the stable version:&nbsp;<code>pip3 install --user tinycov</code></p>
<p>To install the development version:</p>
<pre><code>git clone https://github.com/cmdoret/tinycov.git
cd tinycov
pip install .</code></pre><p>Address of the bookmark: <a href="https://github.com/cmdoret/tinycov" rel="nofollow">https://github.com/cmdoret/tinycov</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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