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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/33973?offset=340</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/32719/download-assemblies-from-ncbi</guid>
	<pubDate>Mon, 15 May 2017 06:02:32 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/32719/download-assemblies-from-ncbi</link>
	<title><![CDATA[Download assemblies from NCBI]]></title>
	<description><![CDATA[<p>A new &ldquo;Download assemblies&rdquo; button is now available in the&nbsp;<a href="https://www.ncbi.nlm.nih.gov/assembly" target="_blank">Assembly</a>&nbsp;database. This makes it easy to download data for multiple genomes without having to write scripts.</p><p>For example, you can run a search in Assembly and use check boxes (see left side of screenshot below) to refine the set of genome assemblies of interest. Then, just open the &ldquo;Download assemblies&rdquo; menu, choose the source database (<a href="https://www.ncbi.nlm.nih.gov/genbank/" target="_blank">GenBank</a>&nbsp;or&nbsp;<a href="https://www.ncbi.nlm.nih.gov/refseq/" target="_blank">RefSeq</a>), choose the file type, and start the download. An archive file will be saved to your computer that can be expanded into a folder containing your selected genome data files.</p><p><img src="https://ncbiinsights.files.wordpress.com/2017/05/download_button.jpg?w=584" alt="image" width="584" height="444" style="border: 0px; border: 0px;"></p><p>&nbsp;</p><p>More at&nbsp;https://ncbiinsights.ncbi.nlm.nih.gov/2017/05/08/genome-data-download-made-easy/</p>]]></description>
	<dc:creator>Bulbul</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33221/genome-annotation-transfer-utility-gatu</guid>
	<pubDate>Mon, 29 May 2017 05:54:53 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33221/genome-annotation-transfer-utility-gatu</link>
	<title><![CDATA[Genome Annotation Transfer Utility (GATU)]]></title>
	<description><![CDATA[<p>Genome Annotation Transfer Utility (GATU) was designed to facilitate quick, efficient annotation of similar genomes using genomes that have already been annotated. For example, whenever a new strain of SARS coronavirus is sequenced, it is possible, using GATU, to automatically annotate the new strain using a previously-annotated strain of SARS CoV. This saves researchers from tedious manual annotation of these sequences.</p>
<p>The program utilizes tBLASTn and BLASTn algorithms to map genes from the reference genome (the annotated strain) to the new sequence (the unannotated strain). The goal is to annotate the majority of the new genome&rsquo;s genes in a single step. ORFs present in the target genome and absent from the reference genome are also identified; these ORFs can be further analyzed using BLAST, VGO and BBB. Afterwards, they can either be accepted for/rejected from annotation. GATU can handle multiple-exon genes as well as mature peptides. Although it was designed for use with viral genomes, GATU can also be used to help annotate larger genomes (ie. bacterial genomes).</p>
<p>The output is saved in GenBank, XML, or EMBL file format.</p><p>Address of the bookmark: <a href="https://virology.uvic.ca/help/tool-help/help-books/genome-annotation-transfer-utility-gatu-documentation/" rel="nofollow">https://virology.uvic.ca/help/tool-help/help-books/genome-annotation-transfer-utility-gatu-documentation/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34925/rectangle-graph-for-repeat-resolution-in-genome-assembly</guid>
	<pubDate>Thu, 28 Dec 2017 09:43:03 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34925/rectangle-graph-for-repeat-resolution-in-genome-assembly</link>
	<title><![CDATA[Rectangle Graph for Repeat Resolution in Genome Assembly]]></title>
	<description><![CDATA[<p>Ultimate tool for resolving repeats in genome assemblies.</p>
<p>Though the specific implementation of the idea of the rectangle graph approach is already included into the&nbsp;<a href="http://bioinf.spbau.ru/spades">current SPAdes distribution</a>, we're also releasing the Rectangle Graph Module (RGM) as the separate code which can be run independently of SPAdes. Although RGM differs from the current implementation of the rectangle graph approach in SPAdes, in the future we plan to integrate RGM in SPAdes. RGM can be run with other genome assemblers if they use the graph format as SPAdes files.</p>
<p>For more details see: Nikolay Vyahhi, Son K. Pham, Pavel Pevzner.&nbsp;<a href="http://www.springerlink.com/content/e617788h25u36440/">From de Bruijn Graphs to Rectangle Graphs for Genome Assembly</a>,&nbsp;<em>Lecture Notes in Bioinformatics</em>&nbsp;7534 (2012), pp. 249-261.</p><p>Address of the bookmark: <a href="http://bioinf.spbau.ru/en/rectangles" rel="nofollow">http://bioinf.spbau.ru/en/rectangles</a></p>]]></description>
	<dc:creator>Rahul Nayak</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/news/view/36405/earth-biogenome-project</guid>
	<pubDate>Wed, 25 Apr 2018 07:48:56 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/36405/earth-biogenome-project</link>
	<title><![CDATA[Earth BioGenome Project]]></title>
	<description><![CDATA[<p><span>The central goal of the Earth BioGenome Project is to understand the evolution and organization of life on our planet by sequencing and functionally annotating the genomes of 1.5 million known species of eukaryotes, a massive group that includes plants, animals, fungi and other organisms whose cells have a nucleus that houses their chromosomal DNA. To date, the genomes of less than 0.2 percent of eukaryotic species have been sequenced.&nbsp;</span></p><p><span>More at&nbsp;https://www.ucdavis.edu/news/earth-biogenome-project-aims-sequence-dna-all-complex-life</span></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36897/gmcloser-closing-gaps-in-assemblies-accurately-with-a-likelihood-based-selection-of-contig-or-long-read-alignments</guid>
	<pubDate>Mon, 11 Jun 2018 05:43:44 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36897/gmcloser-closing-gaps-in-assemblies-accurately-with-a-likelihood-based-selection-of-contig-or-long-read-alignments</link>
	<title><![CDATA[GMcloser: closing gaps in assemblies accurately with a likelihood-based selection of contig or long-read alignments]]></title>
	<description><![CDATA[GMcloser uses likelihood-based classifiers calculated from the alignment statistics between scaffolds, contigs and paired-end reads to correctly assign contigs or long reads to gap regions of scaffolds, thereby achieving accurate and efficient gap closure. We demonstrate with sequencing data from various organisms that the gap-closing accuracy of GMcloser is 3–100-fold higher than those of other available tools, with similar efficiency.

https://academic.oup.com/bioinformatics/article/31/23/3733/209212<p>Address of the bookmark: <a href="https://academic.oup.com/bioinformatics/article/31/23/3733/209212" rel="nofollow">https://academic.oup.com/bioinformatics/article/31/23/3733/209212</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/file/view/37581/comparativegenomics-exercise2</guid>
	<pubDate>Wed, 22 Aug 2018 22:10:56 -0500</pubDate>
	<link>https://bioinformaticsonline.com/file/view/37581/comparativegenomics-exercise2</link>
	<title><![CDATA[ComparativeGenomics Exercise2]]></title>
	<description><![CDATA[<p>COMPARATIVE MICROBIAL GENOMICS ANALYSIS WORKSHOP&nbsp; @&nbsp;cbs.dtu.dk</p><p>Free Bioinformatics workbench https://www.mn.uio.no/ifi/english/research/networks/clsi/earlier_seminars/2012/tammivesth_osloseminarfinal.pdf</p>]]></description>
	<dc:creator>Neel</dc:creator>
	<enclosure url="https://bioinformaticsonline.com/file/download/37581" length="139956" type="application/pdf" />
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38039/vgsc-a-web-based-vector-graph-toolkit-of-genome-synteny-and-collinearity</guid>
	<pubDate>Tue, 30 Oct 2018 10:46:28 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38039/vgsc-a-web-based-vector-graph-toolkit-of-genome-synteny-and-collinearity</link>
	<title><![CDATA[VGSC: A Web-Based Vector Graph Toolkit of Genome Synteny and Collinearity]]></title>
	<description><![CDATA[<p><span>VGSC, the Vector Graph toolkit of genome Synteny and Collinearity, and its online service, to visualize the synteny and collinearity in the common graphical format, including both raster (JPEG, Bitmap, and PNG) and vector graphic (SVG, EPS, and PDF).</span><em>&nbsp;</em></p><p>Address of the bookmark: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4783527/" rel="nofollow">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4783527/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/38418/charles-swanton-lab</guid>
  <pubDate>Tue, 11 Dec 2018 08:09:22 -0600</pubDate>
  <link></link>
  <title><![CDATA[CHARLES SWANTON LAB]]></title>
  <description><![CDATA[
<p>They are using the latest DNA sequencing technology to read the genetic makeup of cancer cells within tumours in ever greater detail, teasing out patterns of evolution (evolutionary rule books), cancer heterogeneity and working out what changes have happened as a tumour evolves. We’re also investigating the processes that cause mutations and accelerate tumour evolution and working out how they might be stopped. And we are running evolutionary clinical trials with immune and targeted therapies to bring the benefits of our work to patients as quickly as possible.</p>

<p>https://www.crick.ac.uk/research/labs/charles-swanton</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38735/genome-assembly-tutorial-genome-assembly-for-short-and-long-reads</guid>
	<pubDate>Sat, 19 Jan 2019 17:29:53 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38735/genome-assembly-tutorial-genome-assembly-for-short-and-long-reads</link>
	<title><![CDATA[Genome assembly tutorial &quot;Genome Assembly for short and long reads&quot;]]></title>
	<description><![CDATA[<p>In this lab we will perform de novo genome assembly of a bacterial genome. You will be guided through the genome assembly starting with data quality control, through to building contigs and analysis of the results. At the end of the lab you will know:</p>
<ol>
<li>How to perform basic quality checks on the input data</li>
<li>How to run a short read assembler on Illumina data</li>
<li>How to run a long read assembler on Pacific Biosciences or Oxford Nanopore data</li>
<li>How to improve the accuracy of a long read assembly using short reads</li>
<li>How to assess the quality of an assembly</li>
</ol>
<p>https://bioinformaticsdotca.github.io/high-throughput_biology_2017</p><p>Address of the bookmark: <a href="https://bioinformaticsdotca.github.io/high-throughput_biology_2017_module6_lab" rel="nofollow">https://bioinformaticsdotca.github.io/high-throughput_biology_2017_module6_lab</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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