<?xml version='1.0'?><rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:georss="http://www.georss.org/georss" xmlns:atom="http://www.w3.org/2005/Atom" >
<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/35135?offset=380</link>
	<atom:link href="https://bioinformaticsonline.com/related/35135?offset=380" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33942/mulan-multiple-sequence-local-alignment-and-conservation-visualization-tool</guid>
	<pubDate>Thu, 20 Jul 2017 08:02:32 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33942/mulan-multiple-sequence-local-alignment-and-conservation-visualization-tool</link>
	<title><![CDATA[Mulan: MUltiple sequence Local AligNment and conservation visualization tool]]></title>
	<description><![CDATA[<p><span>Mulan performs multiple (2 or more) sequence alignments with an efficient and rapid "full local" alignment strategy that ensures a recapitulation of evolutionary sequence rearrangements (such as inversions and reshuffling) in any of the species. It combines&nbsp;</span><a href="http://www.bx.psu.edu/miller_lab/" target="_new"><em>refine</em>&nbsp;and&nbsp;<em>tba</em></a><span>&nbsp;tools to align either "draft" or "finished" quality sequences. Mulan provides a dynamic graphical interface to align and visualize conservation profiles for evolutionarily distant and closely related species.</span><br><span></span></p>
<p><span>Input formats, automated data upload from the&nbsp;</span><a href="http://genome.ucsc.edu/" target="_new">UCSC Genome Browser</a><span>, gene annotation, annotation of repetitive elements, and progress report were previously described in the&nbsp;</span><a href="https://zpicture.dcode.org/zpInstructions.html" target="_zp">zPicture instructions</a><span>&nbsp;and we refer the users to these materials for more details. This introduction is mainly focused on some novel features unique to the Mulan.</span><span><br></span></p><p>Address of the bookmark: <a href="https://mulan.dcode.org/mulanInstructions.php" rel="nofollow">https://mulan.dcode.org/mulanInstructions.php</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34920/xmatchview-smith-waterman-alignment-visualization</guid>
	<pubDate>Thu, 28 Dec 2017 09:00:58 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34920/xmatchview-smith-waterman-alignment-visualization</link>
	<title><![CDATA[xmatchview: smith-waterman alignment visualization]]></title>
	<description><![CDATA[<p><span>xmatchview and xmatchview-conifer are imaging tools for comparing the synteny between DNA sequences. It allows users to align 2 DNA sequences in fasta format using cross_match and displays the alignment in a variety of image formats. xmatchview and xmatchview-conifer are written in python and run on linux and windows. They serve as visual tools for analyzing cross_match alignments. Cross_match (Green, P. (1994)&nbsp;</span><a href="http://www.phrap.org/">http://www.phrap.org</a><span>) uses an implementation of the Smith-Waterman algorithm for comparing DNA sequences that is sensitive.</span></p>
<p><span>http://www.bcgsc.ca/platform/bioinfo/software/xmatchview</span></p><p>Address of the bookmark: <a href="https://github.com/warrenlr/xmatchview" rel="nofollow">https://github.com/warrenlr/xmatchview</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38646/visnetwork-an-r-package-for-network-visualization-using-visjs-javascript-library</guid>
	<pubDate>Wed, 09 Jan 2019 11:00:32 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38646/visnetwork-an-r-package-for-network-visualization-using-visjs-javascript-library</link>
	<title><![CDATA[visNetwork: an R package for network visualization, using vis.js javascript library]]></title>
	<description><![CDATA[<div id="introduction">
<p><strong>visNetwork</strong>&nbsp;is an R package for network visualization, using&nbsp;<strong>vis.js</strong>&nbsp;javascript library (<a href="http://visjs.org/">http://visjs.org/</a>). All remarks and bugs are welcome on github :&nbsp;<a href="https://github.com/datastorm-open/visNetwork">https://github.com/datastorm-open/visNetwork</a>.</p>
</div>
<div id="features">
<h2>Features</h2>
<p>Based on&nbsp;<a href="http://www.htmlwidgets.org/">htmlwidgets</a>, so :</p>
<ul>
<li>compatible with&nbsp;<a href="http://shiny.rstudio.com/">shiny</a>, R Markdown documents, and RStudio viewer</li>
</ul>
<p>The package proposes all the features available in&nbsp;<strong>vis.js</strong>&nbsp;API, and even more with special features for R :</p>
<ul>
<li>easy to use</li>
<li>custom shapes, styles, colors, sizes, &hellip;</li>
<li>works smooth on any modern browser for up to a few thousand nodes and edges</li>
<li>interactivity controls (highlight, collapsed nodes, selection, zoom, physics, movement of nodes, tooltip, events, &hellip;)</li>
<li>visualize&nbsp;<code>rpart</code>&nbsp;tree</li>
<li></li>
</ul>
</div><p>Address of the bookmark: <a href="https://datastorm-open.github.io/visNetwork/" rel="nofollow">https://datastorm-open.github.io/visNetwork/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/44284/tools-for-geospatial-data-analysis</guid>
	<pubDate>Wed, 22 Mar 2023 02:10:28 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/44284/tools-for-geospatial-data-analysis</link>
	<title><![CDATA[Tools for Geospatial data analysis !]]></title>
	<description><![CDATA[<div><div><div><div><div><div><div><div><div><div><p>Geospatial data is becoming increasingly important in many fields, including urban planning, environmental science, public health, and more. These tools can help you work with data from a variety of sources, including satellite imagery, GPS data, and other forms of spatial data. They can help you visualize data, perform complex analysis, and even create maps and other visualizations.</p><p>The list includes some of the most popular and widely used geospatial tools available in Python. These tools can help you work with data from a variety of sources and in a variety of formats. Some of the tools are focused on visualization, such as Cartopy, Folium, and Contextily, which allow you to create interactive maps and other visualizations. Other tools are more focused on data manipulation and analysis, such as Fiona, GeoPandas, and Rasterio, which allow you to manipulate and analyze spatial data in a variety of ways.</p><p>The list also includes some tools for working with specific types of geospatial data. For example, the H3 library is designed specifically for working with hexagonal grids, while PySAL is focused on spatial econometrics and spatial analysis. Whether you are a data scientist, GIS specialist, or geospatial enthusiast, these tools are sure to enhance your work and help you achieve your goals.</p><p>In summary, this list is an excellent resource for anyone working with geospatial data in Python. It contains a wide range of tools for working with different types of data, and can help you visualize data, perform complex analysis, and create maps and other visualizations. If you're looking to enhance your skills in geospatial analysis, this list is definitely worth checking out.</p></div></div></div><div><p>These tools are:</p><ul>
<li>ArcGIS - <a href="https://lnkd.in/dgC6sKJH" target="_new">https://lnkd.in/dgC6sKJH</a></li>
<li>Cartopy - <a href="https://lnkd.in/dc8ijXRg" target="_new">https://lnkd.in/dc8ijXRg</a></li>
<li>Contextily - <a href="https://lnkd.in/dTdQsmKX" target="_new">https://lnkd.in/dTdQsmKX</a></li>
<li>Descartes - <a href="https://lnkd.in/dCJykxwW" target="_new">https://lnkd.in/dCJykxwW</a></li>
<li>Fiona - <a href="https://lnkd.in/d8sJ3Q5a" target="_new">https://lnkd.in/d8sJ3Q5a</a></li>
<li>Folium - <a href="https://lnkd.in/dfSsE-MB" target="_new">https://lnkd.in/dfSsE-MB</a></li>
<li>GDAL - <a href="https://lnkd.in/dYBJBaAY" target="_new">https://lnkd.in/dYBJBaAY</a></li>
<li>Geohash - <a href="https://lnkd.in/d_NxJ4_M" target="_new">https://lnkd.in/d_NxJ4_M</a></li>
<li>GeoJSON - <a href="https://lnkd.in/daGs2WYq" target="_new">https://lnkd.in/daGs2WYq</a></li>
<li>GeoPandas - <a href="https://lnkd.in/dBTFKKV3" target="_new">https://lnkd.in/dBTFKKV3</a></li>
<li>Geopy - <a href="https://lnkd.in/dfAzR8Xa" target="_new">https://lnkd.in/dfAzR8Xa</a></li>
<li>Gevent - <a href="http://www.gevent.org/" target="_new">http://www.gevent.org</a></li>
<li>H3 - <a href="https://h3geo.org/docs/" target="_new">https://h3geo.org/docs/</a></li>
<li>OSMnx - <a href="https://lnkd.in/dm3pHgUS" target="_new">https://lnkd.in/dm3pHgUS</a></li>
<li>PyQGIS - <a href="https://lnkd.in/dShWyWVr" target="_new">https://lnkd.in/dShWyWVr</a></li>
<li>PySAL - <a href="https://pysal.org/" target="_new">https://pysal.org</a></li>
<li>Pydeck - <a href="https://lnkd.in/dGBFu-iw" target="_new">https://lnkd.in/dGBFu-iw</a></li>
<li>Pyproj - <a href="https://lnkd.in/dNG9fdkm" target="_new">https://lnkd.in/dNG9fdkm</a></li>
<li>RTree - <a href="https://lnkd.in/dURMiYpU" target="_new">https://lnkd.in/dURMiYpU</a></li>
<li>Rasterio - <a href="https://lnkd.in/dEMC6ve6" target="_new">https://lnkd.in/dEMC6ve6</a></li>
<li>Scikit-mobility - <a href="https://lnkd.in/dpHhaX2J" target="_new">https://lnkd.in/dpHhaX2J</a></li>
<li>Shapely - <a href="https://lnkd.in/d568datK" target="_new">https://lnkd.in/d568datK</a></li>
</ul><p>These tools offer a wide range of capabilities for working with geospatial data, from visualizing and manipulating data to performing complex analysis and modeling. Whether you are a data scientist, GIS specialist, or geospatial enthusiast, these tools are sure to enhance your work and help you achieve your goals.</p></div></div></div></div></div></div></div></div>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44630/genofig-a-user-friendly-application-for-the-visualization-and-comparison-of-genomic-regions</guid>
	<pubDate>Mon, 05 Aug 2024 23:06:58 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44630/genofig-a-user-friendly-application-for-the-visualization-and-comparison-of-genomic-regions</link>
	<title><![CDATA[GenoFig: a user-friendly application for the visualization and comparison of genomic regions]]></title>
	<description><![CDATA[<p>Tool for graphical vizualisation of annotated genetic regions, and homologous regions comparison. It is an independent recoding of Easyfig 2 initially developped by at the S. Beatson Lab [<a href="https://mjsull.github.io/Easyfig/" target="_blank">https://mjsull.github.io/Easyfig/</a>]</p>
<p dir="auto">Download the GenoFig source code using the 'Download' button on top of this page. Cloning is currently not available for people not member of the INRAE French Institution. After decompression, open a terminal in the folder containing the decompressed files and run:</p>
<div>
<pre id="code-47"><code><span>conda env create -f extras/requirements.yml</span>
<span>extras/SETUP.sh</span></code></pre>
</div><p>Address of the bookmark: <a href="https://forgemia.inra.fr/public-pgba/genofig" rel="nofollow">https://forgemia.inra.fr/public-pgba/genofig</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/4590/tigers-genome-sequenced</guid>
	<pubDate>Tue, 17 Sep 2013 16:48:24 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/4590/tigers-genome-sequenced</link>
	<title><![CDATA[Tigers genome sequenced]]></title>
	<description><![CDATA[<p>Fifteen scientists led by Dr Jong Bhak of Genome Research Foundation, South Korea, decoded as many as 3 billion nucleotides (organic molecules that form the basic building blocks of nucleic acids, such as DNA). They identified 20,000 genes related to various functions of the tiger.&nbsp;</p><p>The biggest and perhaps most fearsome of the world's big cats, the tiger, shares 95.6 percent of its DNA with humans' cute and furry companions, domestic cats.</p><p>The new research showed that big cats have genetic mutations that enabled them to be carnivores. The team also identified mutations that allow snow leopards to thrive at high altitudes.</p><p>Reference:</p><p><a href="http://www.nbcnews.com/science/your-cat-ferocious-tigers-share-lot-95-6-percent-their-4B11182690">http://www.nbcnews.com/science/your-cat-ferocious-tigers-share-lot-95-6-percent-their-4B11182690</a></p><p><a href="http://timesofindia.indiatimes.com/home/environment/flora-fauna/Gene-mapping-of-tiger-completed/articleshow/22671681.cms">http://timesofindia.indiatimes.com/home/environment/flora-fauna/Gene-mapping-of-tiger-completed/articleshow/22671681.cms</a></p><p>Paper:</p><p><a href="http://www.nature.com/ncomms/2013/130917/ncomms3433/full/ncomms3433.html">http://www.nature.com/ncomms/2013/130917/ncomms3433/full/ncomms3433.html</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34443/opera-an-optimal-genome-scaffolding-program</guid>
	<pubDate>Mon, 27 Nov 2017 10:18:20 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34443/opera-an-optimal-genome-scaffolding-program</link>
	<title><![CDATA[Opera: An optimal genome scaffolding program]]></title>
	<description><![CDATA[<p><span>Opera (Optimal Paired-End Read Assembler) is a sequence assembly program (</span><a href="http://en.wikipedia.org/wiki/Sequence_assembly" target="_blank">http://en.wikipedia.org/wiki/Sequence_assembly&nbsp;<img src="https://a.fsdn.com/con/img/icons/external_asset.png" alt="image" style="border: 0px;"></a><span>). It uses information from paired-end or long reads to optimally order and orient contigs assembled from shotgun-sequencing reads.</span><br><br><span>An updated version called OPERA-LG has been re-engineered with features for the assembly of large and complex genomes.</span><br><br><span>Song Gao, Denis Bertrand, Burton K. H. Chia and Niranjan Nagarajan. OPERA-LG: efficient and exact scaffolding of large, repeat-rich eukaryotic genomes with performance guarantees. Genome Biology, May 2016, doi: 10.1186/s13059-016-0951-y.</span><br><br><span>Song Gao, Wing-Kin Sung, Niranjan Nagarajan. Opera: reconstructing optimal genomic scaffolds with high-throughput paired-end sequences. Journal of Computational Biology, Sept. 2011, doi:10.1089/cmb.2011.0170.</span></p>
<p><span>https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0951-y</span></p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/operasf/" rel="nofollow">https://sourceforge.net/projects/operasf/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/34418/spades-hybrid-genome-assembly</guid>
	<pubDate>Mon, 27 Nov 2017 08:05:40 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/34418/spades-hybrid-genome-assembly</link>
	<title><![CDATA[SPAdes hybrid genome assembly]]></title>
	<description><![CDATA[<p>When you have both Illumina and Nanopore data, then SPAdes remains a good option for hybrid assembly - SPAdes was used to produce the&nbsp;<a href="https://gigascience.biomedcentral.com/articles/10.1186/s13742-015-0101-6">B fragilis assembly</a>&nbsp;by Mick Watson&rsquo;s group.</p><p>Again, running spades.py will show you the options:</p><div><pre><code>spades.py
</code></pre></div><p>This produces:</p><div><pre><code>SPAdes genome assembler v3.10.1

Usage: /usr/local/SPAdes-3.10.1-Linux/bin/spades.py [options] -o &lt;output_dir&gt;

Basic options:
-o      &lt;output_dir&gt;    directory to store all the resulting files (required)
--sc                    this flag is required for MDA (single-cell) data
--meta                  this flag is required for metagenomic sample data
--rna                   this flag is required for RNA-Seq data
--plasmid               runs plasmidSPAdes pipeline for plasmid detection
--iontorrent            this flag is required for IonTorrent data
--test                  runs SPAdes on toy dataset
-h/--help               prints this usage message
-v/--version            prints version

Input data:
--12    &lt;filename&gt;      file with interlaced forward and reverse paired-end reads
-1      &lt;filename&gt;      file with forward paired-end reads
-2      &lt;filename&gt;      file with reverse paired-end reads
-s      &lt;filename&gt;      file with unpaired reads
--pe&lt;#&gt;-12      &lt;filename&gt;      file with interlaced reads for paired-end library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--pe&lt;#&gt;-1       &lt;filename&gt;      file with forward reads for paired-end library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--pe&lt;#&gt;-2       &lt;filename&gt;      file with reverse reads for paired-end library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--pe&lt;#&gt;-s       &lt;filename&gt;      file with unpaired reads for paired-end library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--pe&lt;#&gt;-&lt;or&gt;    orientation of reads for paired-end library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9; &lt;or&gt; = fr, rf, ff)
--s&lt;#&gt;          &lt;filename&gt;      file with unpaired reads for single reads library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--mp&lt;#&gt;-12      &lt;filename&gt;      file with interlaced reads for mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--mp&lt;#&gt;-1       &lt;filename&gt;      file with forward reads for mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--mp&lt;#&gt;-2       &lt;filename&gt;      file with reverse reads for mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--mp&lt;#&gt;-s       &lt;filename&gt;      file with unpaired reads for mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--mp&lt;#&gt;-&lt;or&gt;    orientation of reads for mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9; &lt;or&gt; = fr, rf, ff)
--hqmp&lt;#&gt;-12    &lt;filename&gt;      file with interlaced reads for high-quality mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--hqmp&lt;#&gt;-1     &lt;filename&gt;      file with forward reads for high-quality mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--hqmp&lt;#&gt;-2     &lt;filename&gt;      file with reverse reads for high-quality mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--hqmp&lt;#&gt;-s     &lt;filename&gt;      file with unpaired reads for high-quality mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--hqmp&lt;#&gt;-&lt;or&gt;  orientation of reads for high-quality mate-pair library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9; &lt;or&gt; = fr, rf, ff)
--nxmate&lt;#&gt;-1   &lt;filename&gt;      file with forward reads for Lucigen NxMate library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--nxmate&lt;#&gt;-2   &lt;filename&gt;      file with reverse reads for Lucigen NxMate library number &lt;#&gt; (&lt;#&gt; = 1,2,..,9)
--sanger        &lt;filename&gt;      file with Sanger reads
--pacbio        &lt;filename&gt;      file with PacBio reads
--nanopore      &lt;filename&gt;      file with Nanopore reads
--tslr  &lt;filename&gt;      file with TSLR-contigs
--trusted-contigs       &lt;filename&gt;      file with trusted contigs
--untrusted-contigs     &lt;filename&gt;      file with untrusted contigs

Pipeline options:
--only-error-correction runs only read error correction (without assembling)
--only-assembler        runs only assembling (without read error correction)
--careful               tries to reduce number of mismatches and short indels
--continue              continue run from the last available check-point
--restart-from  &lt;cp&gt;    restart run with updated options and from the specified check-point ('ec', 'as', 'k&lt;int&gt;', 'mc')
--disable-gzip-output   forces error correction not to compress the corrected reads
--disable-rr            disables repeat resolution stage of assembling

Advanced options:
--dataset       &lt;filename&gt;      file with dataset description in YAML format
-t/--threads    &lt;int&gt;           number of threads
                                [default: 16]
-m/--memory     &lt;int&gt;           RAM limit for SPAdes in Gb (terminates if exceeded)
                                [default: 250]
--tmp-dir       &lt;dirname&gt;       directory for temporary files
                                [default: &lt;output_dir&gt;/tmp]
-k              &lt;int,int,...&gt;   comma-separated list of k-mer sizes (must be odd and
                                less than 128) [default: 'auto']
--cov-cutoff    &lt;float&gt;         coverage cutoff value (a positive float number, or 'auto', or 'off') [default: 'off']
--phred-offset  &lt;33 or 64&gt;      PHRED quality offset in the input reads (33 or 64)
                                [default: auto-detect]
</code></pre></div><p>As you can see this is also a &ldquo;pipeline&rdquo; of tools that can be switched on or off. SPAdes takes quite a long time, so for the purposes of this practical, something like this may suffice:</p><div><pre><code>spades.py -t 4 <span>\</span>
          -m 32 <span>\</span>
          -k 31,51,71 <span>\</span>
          --only-assembler <span>\</span>
          -1 miseq.1.fastq -2 miseq.2.fastq <span>\</span>
          --nanopore minion.fastq <span>\</span>
          -o hybrid_assembly
</code></pre></div><p>In turn, these parameters mean</p><ul>
<li>use 4 threads</li>
<li>max memory is 32Gb</li>
<li>use 3 kmer values to build the de bruijn graph(s) - 31, 51 and 71</li>
<li>only run the assembler, not the correction algorithm (for speed)</li>
<li>read 1 and read 2 of the MiSeq data</li>
<li>the nanopore data</li>
<li>put the output in folder &ldquo;hybrid_assembly&rdquo;</li>
</ul>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34528/cope-an-accurate-k-mer-based-pair-end-reads-connection-tool-to-facilitate-genome-assembly</guid>
	<pubDate>Wed, 06 Dec 2017 02:08:14 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34528/cope-an-accurate-k-mer-based-pair-end-reads-connection-tool-to-facilitate-genome-assembly</link>
	<title><![CDATA[COPE: an accurate k-mer-based pair-end reads connection tool to facilitate genome assembly]]></title>
	<description><![CDATA[<p><span>An efficient tool called Connecting Overlapped Pair-End (COPE) reads, to connect overlapping pair-end reads using k-mer frequencies. We evaluated our tool on 30&times; simulated pair-end reads from Arabidopsis thaliana with 1% base error. COPE connected over 99% of reads with 98.8% accuracy, which is, respectively, 10 and 2% higher than the recently published tool FLASH. When COPE is applied to real reads for genome assembly, the resulting contigs are found to have fewer errors and give a 14-fold improvement in the N50 measurement when compared with the contigs produced using unconnected reads.</span></p><p>Address of the bookmark: <a href="ftp://ftp.genomics.org.cn/pub/cope" rel="nofollow">ftp://ftp.genomics.org.cn/pub/cope</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35432/mummer4-a-fast-and-versatile-genome-alignment-system</guid>
	<pubDate>Sat, 03 Feb 2018 04:59:17 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35432/mummer4-a-fast-and-versatile-genome-alignment-system</link>
	<title><![CDATA[MUMmer4: A fast and versatile genome alignment system]]></title>
	<description><![CDATA[<p><span>MUMmer4, a substantially improved version of MUMmer that addresses genome size constraints by changing the 32-bit suffix tree data structure at the core of MUMmer to a 48-bit suffix array, and that offers improved speed through parallel processing of input query sequences. With a theoretical limit on the input size of 141Tbp, MUMmer4 can now work with input sequences of any biologically realistic length. We show that as a result of these enhancements, the&nbsp;</span><span>nucmer</span><span>&nbsp;program in MUMmer4 is easily able to handle alignments of large genomes;&nbsp;</span></p><p>Address of the bookmark: <a href="https://mummer4.github.io/" rel="nofollow">https://mummer4.github.io/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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