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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/42166?offset=260</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27432/gkno</guid>
	<pubDate>Fri, 20 May 2016 18:56:37 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27432/gkno</link>
	<title><![CDATA[GKNO]]></title>
	<description><![CDATA[<p><span>gkno opens the world of complex bioinformatic analysis to people of all level of computational expertise. This site contains documentation, tutorials and information on all the tools that comprise gkno.</span></p>
<p><span>http://gkno.me/how-to/install.html</span></p>
<p><span>http://gkno.me/software.html</span></p><p>Address of the bookmark: <a href="http://gkno.me/" rel="nofollow">http://gkno.me/</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27477/cytoscape</guid>
	<pubDate>Mon, 23 May 2016 02:32:00 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27477/cytoscape</link>
	<title><![CDATA[Cytoscape]]></title>
	<description><![CDATA[<p>Cytoscape is an <a href="http://www.cytoscape.org/download.php">open source</a> software platform for visualizing complex networks and integrating these with any type of attribute data. A lot of <a href="http://apps.cytoscape.org/"><em>Apps</em></a> are available for various kinds of problem domains, including bioinformatics, social network analysis, and semantic web.</p><p>Address of the bookmark: <a href="http://www.cytoscape.org/" rel="nofollow">http://www.cytoscape.org/</a></p>]]></description>
	<dc:creator>Anjana</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27959/darkhorse</guid>
	<pubDate>Wed, 22 Jun 2016 05:37:38 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27959/darkhorse</link>
	<title><![CDATA[DarkHorse]]></title>
	<description><![CDATA[<p><em>DarkHorse</em>&nbsp;is a bioinformatic method for rapid, automated identification and ranking of phylogenetically atypical proteins on a genome-wide basis. It works by selecting potential ortholog matches from a reference database of amino acid sequences, then using these matches to calculate a lineage probability index (LPI) score for each genome protein.</p>
<p>LPI scores are inversely proportional to the phylogenetic distance between database match sequences and the query genome. These scores are useful not only for large-scale<em>de novo</em>&nbsp;predictions of horizontally transferred proteins, but can also serve as an independent quality control test for potential horizontal transfer candidates identified by alternative methods, especially those based on nucleic acid signatures. Candidates having high LPI scores are unlikely to have been horizontally transferred, since they are highly conserved among closely related organisms.</p>
<p>One unique and powerful feature of the DarkHorse HGT Candidate database is the opportunity to explore the phylogenetic background of potential HGT donors as well as recipients. The breadth of the database allows not only query sequences, but also their database match partners to be evaluated for sequence similarity or novelty compared to taxonomically related organisms.</p>
<p><em>DarkHorse</em>&nbsp;is configurable for varying degrees of phylogenetic granularity and protein sequence conservation. Users should consult the&nbsp;<a href="http://darkhorse.ucsd.edu/#references">references</a>&nbsp;cited below for a complete explanation of parameter selection and result interpretation. A brief&nbsp;<a href="http://darkhorse.ucsd.edu/tutorial.html">tutorial</a>&nbsp;page is also available on-line.</p><p>Address of the bookmark: <a href="http://darkhorse.ucsd.edu/download.html" rel="nofollow">http://darkhorse.ucsd.edu/download.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/28199/genome-workbench-2107</guid>
	<pubDate>Fri, 01 Jul 2016 12:09:59 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/28199/genome-workbench-2107</link>
	<title><![CDATA[Genome Workbench 2.10.7]]></title>
	<description><![CDATA[<p>Genome Workbench 2.10.7 is here! New features include added support for local custom BLAST databases and improvements to Tree View.</p><p>For the full list of features, improvements and fixes, see the release notes:<a href="https://ncbi.nlm.nih.gov/tools/gbench/releasenotes" target="_blank">https://ncbi.nlm.nih.gov/tools/gbench/releasenotes</a></p><p>New Features</p><ul>
<li>BLAST Tool: added support for local custom BLAST databases</li>
<li>Graphical Sequence View: added log scaling option for graph tracks</li>
<li>Generic Table View:&nbsp;<a href="https://www.ncbi.nlm.nih.gov/tools/gbench/tutorial17">new tutorial</a>&nbsp;added</li>
</ul><p>Bug Fixes and Improvements</p><ul>
<li>Project Tree View: Genomic Collections/Assemblies now show accessions, not just names</li>
<li>Tree View: layout updated to better accommodate nodes of different sizes</li>
<li>Table Import Dialog (MacOS): fixed issue with table visibility</li>
<li>Fixed bug where different molecules IDs in GenBank could resolve to the same sequence</li>
<li>Graphical Sequence View: fixed issue where sequence track was not shown for some sequences</li>
<li>Graphical Sequence View: fixed protein coloration methods</li>
<li>Graphical Sequence View: improved rendering of Markers to better indicate boundaries and produce higher quality PDF images</li>
<li>Create Gene Model tool: fixed scenario when gene model tool failed with local sequences</li>
<li>Search View: ORF Finder &ndash; fixed incorrect protein lengths</li>
<li>Fixed bug with not opening project file (.gbp) on a click</li>
<li>Fixed issues in GVF import</li>
<li>Fixed BLAST Search tool against NCBI databases not working</li>
<li>Fixed tblastn (protein BLAST) not working in standalone mode</li>
<li>Fixed GTF export failure</li>
</ul>]]></description>
	<dc:creator>Gudiya Pal</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/28807/organellargenomedraw</guid>
	<pubDate>Tue, 16 Aug 2016 08:13:13 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28807/organellargenomedraw</link>
	<title><![CDATA[OrganellarGenomeDRAW]]></title>
	<description><![CDATA[<p><span>O</span><span>rganellar</span><span>G</span><span>enome</span><span>DRAW</span><span>&nbsp;is dedicated to convert genetic information stored in GenBank entries to graphical maps. The input text file has to be in GenBank flat file format, whereas the output format can be chosen among several formats. The application is especially optimized and adapted for the creation of high-quality, detailed circular maps of organellar genomes like the plastid genome (plastome) or the mitochondrial genome (chondriome). Nevertheless, you can upload any GenBank entry. The workflow is devided into three steps.&nbsp;</span></p>
<p><span>More at&nbsp;http://ogdraw.mpimp-golm.mpg.de/cgi-bin/ogdraw.pl</span></p><p>Address of the bookmark: <a href="http://ogdraw.mpimp-golm.mpg.de/index.shtml" rel="nofollow">http://ogdraw.mpimp-golm.mpg.de/index.shtml</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28844/teannot</guid>
	<pubDate>Thu, 18 Aug 2016 10:02:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28844/teannot</link>
	<title><![CDATA[TEannot]]></title>
	<description><![CDATA[<p>We advise to run first the TEdenovo pipeline but it is not compulsory. We suppose you begin by running the TEannot pipeline on the example provided in the directory "db/" rather than directly on your own genomic sequences. Thus, from now on, the project name is "DmelChr4".</p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="https://urgi.versailles.inra.fr/Tools/REPET/TEannot-tuto" rel="nofollow">https://urgi.versailles.inra.fr/Tools/REPET/TEannot-tuto</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35762/genome-assembly-stats-plotting</guid>
	<pubDate>Wed, 28 Feb 2018 03:45:39 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35762/genome-assembly-stats-plotting</link>
	<title><![CDATA[Genome assembly stats plotting]]></title>
	<description><![CDATA[<p>A&nbsp;<em>de novo</em>&nbsp;genome assembly can be summarised b</p>
<p>y a number of metrics, including:</p>
<ul>
<li>Overall assembly length</li>
<li>Number of scaffolds/contigs</li>
<li>Length of longest scaffold/contig</li>
<li>Scaffold/contig N50 and N90Assembly base composition, in particular percentage GC and percentage Ns</li>
<li>CEGMA completeness</li>
<li>Scaffold/contig length/count distribution</li>
</ul>
<p>assembly-stats supports two widely used presentations of these values, tabular and cumulative length plots, and introduces an additional circular plot that summarises most commonly used assembly metrics in a single visualisation. Each of these presentations is generated using javascript from a common (JSON) data structure, allowing toggling between alternative views, and each can be applied to a single or multiple assemblies to allow direct comparison of alternate assemblies.</p>
<p>Tabular presentation allows direct comparison of exact values between assemblies, the limitations of this approach lie in the necessary omission of distributions and the challenge of interpreting ratios of values that may vary by several orders of magnitude.</p><p>Address of the bookmark: <a href="https://github.com/rjchallis/assembly-stats" rel="nofollow">https://github.com/rjchallis/assembly-stats</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28870/genemania</guid>
	<pubDate>Mon, 22 Aug 2016 09:55:16 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28870/genemania</link>
	<title><![CDATA[GeneMANIA]]></title>
	<description><![CDATA[<p>Faster, more accurate algorithms function prediction "GeneMANIA (Multiple Association Network Integration Algorithm)" have however been developed in recent years and are publicly available on the web, indicating the future direction of function prediction.</p><p>Address of the bookmark: <a href="http://genemania.org/" rel="nofollow">http://genemania.org/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28903/genevalidator-identify-problems-with-predicted-genes</guid>
	<pubDate>Fri, 26 Aug 2016 06:00:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28903/genevalidator-identify-problems-with-predicted-genes</link>
	<title><![CDATA[GeneValidator - Identify problems with predicted genes]]></title>
	<description><![CDATA[<p>GeneValidator helps in identifing problems with gene predictions and provide useful information extracted from analysing orthologs in BLAST databases. The results produced can be used by biocurators and researchers who need accurate gene predictions.</p>
<p>If you would like to use GeneValidator on a few sequences, see our online&nbsp;<a href="http://genevalidator.sbcs.qmul.ac.uk/">GeneValidator Web App</a>&nbsp;-<a href="http://genevalidator.sbcs.qmul.ac.uk/">http://genevalidator.sbcs.qmul.ac.uk</a>.</p>
<p>If you use GeneValidator in your work, please cite us as follows:</p>
<blockquote>
<p><a href="http://bioinformatics.oxfordjournals.org/content/early/2016/02/26/bioinformatics.btw015">Dragan M<span>&Dagger;</span>, Moghul MI<span>&Dagger;</span>, Priyam A, Bustos C &amp; Wurm Y. 2016. GeneValidator: identify problems with protein-coding gene predictions.&nbsp;<em>Bioinformatics</em>, doi: 10.1093/bioinformatics/btw015</a>.</p>
<p>&nbsp;</p>
</blockquote>
<h2>&nbsp;</h2><p>Address of the bookmark: <a href="https://github.com/wurmlab/genevalidator" rel="nofollow">https://github.com/wurmlab/genevalidator</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>

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