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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/30216?offset=90</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31345/prokka-tool-for-the-rapid-annotation-of-prokaryotic-genomes</guid>
	<pubDate>Mon, 06 Mar 2017 03:49:57 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31345/prokka-tool-for-the-rapid-annotation-of-prokaryotic-genomes</link>
	<title><![CDATA[Prokka: tool for the rapid annotation of prokaryotic genomes]]></title>
	<description><![CDATA[<p>Prokka is a software tool for the rapid annotation of prokaryotic genomes. A typical 4 Mbp genome can be fully annotated in less than 10 minutes on a quad-core computer, and scales well to 32 core SMP systems. It produces GFF3, GBK and SQN files that are ready for editing in Sequin and ultimately submitted to Genbank/DDJB/ENA.</p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="http://www.vicbioinformatics.com/software.prokka.shtml" rel="nofollow">http://www.vicbioinformatics.com/software.prokka.shtml</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31375/cocacola-binning-metagenomic-contigs-using-sequence-composition-read-coverage-co-alignment-and-paired-end-read-linkage</guid>
	<pubDate>Tue, 07 Mar 2017 08:50:57 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31375/cocacola-binning-metagenomic-contigs-using-sequence-composition-read-coverage-co-alignment-and-paired-end-read-linkage</link>
	<title><![CDATA[COCACOLA (binning metagenomic contigs using sequence COmposition, read CoverAge, CO-alignment, and paired-end read LinkAge)]]></title>
	<description><![CDATA[<p>COCACOLA is a general framework that combines different types of information: sequence COmposition, CoverAge across multiple samples, CO-alignment to reference genomes and paired-end reads LinkAge to automatically bin contigs into OTUs. Furthermore, COCACOLA seamlessly embraces customized prior knowledge to facilitate binning accuracy.</p>
<p>News: Python version of COCACOLA is available now!</p><p>Address of the bookmark: <a href="https://github.com/younglululu/COCACOLA" rel="nofollow">https://github.com/younglululu/COCACOLA</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31552/multigenome-assembly</guid>
	<pubDate>Tue, 14 Mar 2017 04:41:23 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31552/multigenome-assembly</link>
	<title><![CDATA[Multigenome assembly]]></title>
	<description><![CDATA[<p>This project contains scripts and tutorials on how to assemble individual microbial genomes from metagenomes, as described in:</p>
<p>Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes</p>
<p>Mads Albertsen, Philip Hugenholtz, Adam Skarshewski, Gene W. Tyson, K&aring;re L. Nielsen and Per .H. Nielsen</p>
<p>Nature Biotechnology 2013, doi:&nbsp;<a href="http://www.nature.com/nbt/journal/vaop/ncurrent/abs/nbt.2579.html">10.1038/nbt.2579</a></p>
<p>See the associated&nbsp;<a href="http://madsalbertsen.github.io/multi-metagenome/">online guide</a>&nbsp;for detailed information.</p>
<p>https://github.com/MadsAlbertsen/multi-metagenome</p><p>Address of the bookmark: <a href="https://github.com/MadsAlbertsen/multi-metagenome" rel="nofollow">https://github.com/MadsAlbertsen/multi-metagenome</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<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/37257/asar-advanced-metagenomic-sequence-analysis-in-r</guid>
	<pubDate>Mon, 09 Jul 2018 05:20:50 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37257/asar-advanced-metagenomic-sequence-analysis-in-r</link>
	<title><![CDATA[ASAR: Advanced metagenomic Sequence Analysis in R]]></title>
	<description><![CDATA[<p><span>An interactive data analysis tool for selection, aggregation and visualization of metagenomic data is presented. Functional analysis with a SEED hierarchy and pathway diagram based on KEGG orthology based upon MG-RAST annotation results is available.</span></p>
<p><span><span>To read the manual, please click the link&nbsp;</span><a href="https://askarbek-orakov.github.io/ASAR/">https://askarbek-orakov.github.io/ASAR/</a></span></p><p>Address of the bookmark: <a href="https://github.com/Askarbek-orakov/ASAR" rel="nofollow">https://github.com/Askarbek-orakov/ASAR</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30111/eager</guid>
	<pubDate>Sat, 10 Dec 2016 18:07:23 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30111/eager</link>
	<title><![CDATA[EAGER]]></title>
	<description><![CDATA[<p><span>The automated reconstruction of genome sequences in ancient genome analysis is a multifaceted process.</span></p>
<p><span>EAGER encompasses both state-of-the-art tools for each step as well as new complementary tools tailored for ancient DNA data within a single integrated solution in an easily accessible format.</span></p>
<p>https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0918-z</p><p>Address of the bookmark: <a href="https://github.com/apeltzer/EAGER-GUI" rel="nofollow">https://github.com/apeltzer/EAGER-GUI</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31014/sockeye</guid>
	<pubDate>Fri, 17 Feb 2017 08:51:16 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31014/sockeye</link>
	<title><![CDATA[sockeye]]></title>
	<description><![CDATA[<p>This sockeye&nbsp;software uses the Ensembl database project to import sequence and annotation information from several eukaryotic species. A user can additionally import their own custom sequence and annotation data. Individual annotation objects are displayed in Sockeye by using custom 3D models. Ensembl-derived and imported sequences can be analyzed by using a suite of multiple and pair-wise alignment algorithms. The results of these comparative analyses are also displayed in the 3D environment of Sockeye. By using the Java3D API to visualize genomic data in a 3D environment, we are able to compactly display cross-sequence comparisons. This provides the user with a novel platform for visualizing and comparing genomic feature organization.</p><p>Address of the bookmark: <a href="http://www.bcgsc.ca/platform/bioinfo/software/sockeye/releases/1.3" rel="nofollow">http://www.bcgsc.ca/platform/bioinfo/software/sockeye/releases/1.3</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37840/long-read-assembly-workshop</guid>
	<pubDate>Thu, 04 Oct 2018 17:23:18 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37840/long-read-assembly-workshop</link>
	<title><![CDATA[Long read assembly workshop !]]></title>
	<description><![CDATA[<p>This is a tutorial for a workshop on long-read (PacBio) genome assembly.</p>
<p>It demonstrates how to use long PacBio sequencing reads to assemble a bacterial genome, and includes additional steps for circularising, trimming, finding plasmids, and correcting the assembly with short-read Illumina data.</p>
<p>&nbsp;Please comment if you know any other long read addembly tutorial.</p><p>Address of the bookmark: <a href="http://sepsis-omics.github.io/tutorials/modules/cmdline_assembly_v2/" rel="nofollow">http://sepsis-omics.github.io/tutorials/modules/cmdline_assembly_v2/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/31566/software-and-tools-to-detect-structure-variation-with-long-reads</guid>
	<pubDate>Wed, 15 Mar 2017 14:31:09 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/31566/software-and-tools-to-detect-structure-variation-with-long-reads</link>
	<title><![CDATA[Software and Tools to detect structure variation with long reads !!]]></title>
	<description><![CDATA[<p>Uncovering the connection between genetics and heritable diseases requires an approach that looks at all the variant bases and types in a genome. While a PacBio&nbsp;<em>de novo</em>&nbsp;assembly resolves the most novel SV variants. 8-10X PacBio coverage of single genomes or trios reveals triple the SVs detectable by short-read data.</p><p>With&nbsp;<span style="text-decoration: underline;"><a href="http://www.pacb.com/smrt-science/">Single Molecule, Real-Time (SMRT) Sequencing</a></span>, you can access structural variations having a broad range of sizes, types, and GC content with the ability to:</p><ul>
<li>Uncover missing heritability linked to structural variation</li>
<li>Unambiguously identify genomic context and variant breakpoints at the sequence level to unravel the genetic etiology of disease</li>
<li>Resolve structural variation across the complete size spectrum with basepair resolution</li>
</ul><p>Following are the SV tools, which can assist you to achieve your goal.</p><p><strong>Sniffles:</strong>&nbsp;Structural variation caller using third generation sequencing</p><p>Sniffles is a structural variation caller using third generation sequencing (PacBio or Oxford Nanopore). It detects all types of SVs using evidence from split-read alignments, high-mismatch regions, and coverage analysis. Please note the current version of Sniffles requires sorted output from BWA-MEM (use -M and -x parameter) or NGM-LR with the optional SAM attributes enabled!&nbsp;</p><p>More at&nbsp;https://github.com/fritzsedlazeck/Sniffles</p><p><strong style="font-size: 12.8px;"><br />MultiBreak-SV:</strong> It identifies structural variants from next-generation paired end data, third-generation long read data, or data from a combination of sequencing platforms.</p><p>There are two pieces of software in this release: (1) a pre-processor that takes machineformat (.m5) BLASR files, and (2) MultiBreak-SV. For installation and usage instructions, see doc/MultiBreakSV-Manual.txt.</p><p>More at&nbsp;https://github.com/raphael-group/multibreak-sv</p><p><strong style="font-size: 12.8px;"><br />Parliament:</strong>&nbsp;A Structural Variation Tool. Why ask a single sv-detection approach to find every variant when you can have a parliament of tools deciding?</p><p>Publication about the algorithm and &ldquo;&hellip;the first long-read characterization of structural variation in a diploid human personal genome&hellip;&rdquo; (HS1011) -&nbsp;<a href="http://www.biomedcentral.com/1471-2164/16/286">&ldquo;Assessing structural variation in a personal genome&mdash;towards a human reference diploid genome&rdquo;</a></p><p>More at&nbsp;https://sourceforge.net/projects/parliamentsv/</p><p>https://www.dnanexus.com/papers/Parliament_Info_Sheet.pdf</p><p><br /><strong>PBHoney:</strong>&nbsp;the structural variation discovery tool&nbsp;<br /><br />PBHoney is an implementation of two variant-identification approaches designed to exploit the high mappability of long reads (i.e., greater than 10,000 bp). PBHoney considers both intra-read discordance and soft-clipped tails of long reads to identify structural variants.</p><p>Read The Paper&nbsp;<a href="http://www.biomedcentral.com/1471-2105/15/180/abstract" target="_blank">http://www.biomedcentral.com/1471-2105/15/180/abstract</a></p><p>More at&nbsp;https://sourceforge.net/projects/pb-jelly/</p><p><strong><br />SMRT-SV:</strong> Structural variant and indel caller for PacBio reads</p><p>Structural variant (SV) and indel caller for PacBio reads based on methods from&nbsp;<a href="http://www.nature.com/nature/journal/vaop/ncurrent/full/nature13907.html">Chaisson et al. 2014</a>.</p><p>SMRT-SV provides an official software package for tools described in&nbsp;<a href="http://www.nature.com/nature/journal/vaop/ncurrent/full/nature13907.html">Chaisson et al. 2014</a>&nbsp;and adds several key features including the following.</p><ul>
<li>Unified variant calling user interface with built-in cluster compute support</li>
<li>Small indel calling (2-49 bp)</li>
<li>Improved inversion calling (<code>screenInversions</code>)</li>
<li>Quality metric for SV calls based on number of local assemblies supporting each call</li>
<li>Higher sensitivity for SV calls using tiled local assemblies across the entire genome instead of "signature" regions</li>
<li>Genotyping of SVs with Illumina paired-end reads from WGS samples</li>
</ul><p>More at&nbsp;https://github.com/EichlerLab/pacbio_variant_caller</p>]]></description>
	<dc:creator>Archana Malhotra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40598/mitoz-a-toolkit-for-animal-mitochondrial-genome-assembly-annotation-and-visualization</guid>
	<pubDate>Fri, 24 Jan 2020 04:09:15 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40598/mitoz-a-toolkit-for-animal-mitochondrial-genome-assembly-annotation-and-visualization</link>
	<title><![CDATA[MitoZ: a toolkit for animal mitochondrial genome assembly, annotation and visualization]]></title>
	<description><![CDATA[<p><span>MitoZ is a Python3-based toolkit which aims to automatically filter pair-end raw data (fastq files), assemble genome, search for mitogenome sequences from the genome assembly result, annotate mitogenome (genbank file as result), and mitogenome visualization. MitoZ is available from&nbsp;</span><code>https://github.com/linzhi2013/MitoZ</code><span>.</span></p>
<p><span><a href="https://academic.oup.com/nar/article/47/11/e63/5377471">https://academic.oup.com/nar/article/47/11/e63/5377471</a></span></p><p>Address of the bookmark: <a href="https://github.com/linzhi2013/MitoZ" rel="nofollow">https://github.com/linzhi2013/MitoZ</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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