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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/44470?offset=150</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30207/gam-ngs-genomic-assemblies-merger-for-next-generation-sequencing</guid>
	<pubDate>Mon, 19 Dec 2016 06:07:05 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30207/gam-ngs-genomic-assemblies-merger-for-next-generation-sequencing</link>
	<title><![CDATA[GAM-NGS: genomic assemblies merger for next generation sequencing]]></title>
	<description><![CDATA[<p><span>GAM-NGS (Genomic Assemblies Merger for Next Generation Sequencing), whose primary goal is to merge two or more assemblies in order to enhance contiguity and correctness of both. GAM-NGS does not rely on global alignment: regions of the two assemblies representing the same genomic&nbsp;</span><em>locus</em><span>&nbsp;(called&nbsp;</span><em>blocks</em><span>) are identified through reads' alignments and stored in a&nbsp;</span><em>weighted</em><span>graph. The merging phase is carried out with the help of this weighted graph that allows an&nbsp;</span><em>optimal</em><span>&nbsp;resolution of&nbsp;</span><em>local</em><span>&nbsp;problematic regions.</span></p><p>Address of the bookmark: <a href="https://github.com/vice87/gam-ngs" rel="nofollow">https://github.com/vice87/gam-ngs</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30249/genome-assembly-tutorial</guid>
	<pubDate>Tue, 20 Dec 2016 07:56:01 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30249/genome-assembly-tutorial</link>
	<title><![CDATA[Genome Assembly Tutorial]]></title>
	<description><![CDATA[<p><span>If genomes were completely random sequences in a statistical sense, 'overlap-consensus-layout' method would have been enough to assemble large genomes from Sanger reads. In contrast, real genomes often have long repetitive regions, and they are hard to assemble using overlap-consensus-layout approach. De Bruijn graph-based assembly approach was originally proposed to handle the assembly of repetitive regions better.</span></p>
<p><span>More at&nbsp;http://www.homolog.us/Tutorials/index.php?p=1.4&amp;s=1</span></p><p>Address of the bookmark: <a href="http://www.homolog.us/Tutorials/index.php?p=1.4&amp;s=1" rel="nofollow">http://www.homolog.us/Tutorials/index.php?p=1.4&amp;s=1</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31343/metabat-an-efficient-tool-for-accurately-reconstructing-single-genomes-from-complex-microbial-communities</guid>
	<pubDate>Mon, 06 Mar 2017 03:44:34 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31343/metabat-an-efficient-tool-for-accurately-reconstructing-single-genomes-from-complex-microbial-communities</link>
	<title><![CDATA[MetaBAT:  An Efficient Tool for Accurately Reconstructing Single Genomes from Complex Microbial Communities]]></title>
	<description><![CDATA[<p>MetaBAT, An Efficient Tool for Accurately Reconstructing Single Genomes from Complex Microbial Communities</p>
<p>Grouping large genomic fragments assembled from shotgun metagenomic sequences to deconvolute complex microbial communities, or metagenome binning, enables the study of individual organisms and their interactions. Here we developed an automated metagenome binning software, called MetaBAT, which integrates empirical probabilistic distances of genome abundance and tetranucleotide frequency. Tested on both synthetic and real metagenome datasets, MetaBAT outperforms alternative methods in both accuracy and computational efficiency. Applying MetaBAT to an assembly from 1,704 human gut samples formed 1,634 genome bins (&gt;200kb) in 3 hours, where 621 genome bins are &gt;50% complete with &lt;5% contamination from other species. Further analysis shows that the quality of these genome bins approaches manually curated genomes.</p><p>Address of the bookmark: <a href="https://bitbucket.org/berkeleylab/metabat" rel="nofollow">https://bitbucket.org/berkeleylab/metabat</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31105/understanding-pacbio</guid>
	<pubDate>Fri, 24 Feb 2017 10:17:36 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31105/understanding-pacbio</link>
	<title><![CDATA[Understanding PacBio]]></title>
	<description><![CDATA[<p>This tutorial includes resources for learning more about PacBio data and bioinformatics analysis, and includes content suitable for both beginners and experts. Below are links to training modules (webinars and PowerPoint presentations) to help you get started with your data processing, as well as information for specialized applications.</p>
<p>Training Resources:</p>
<ul>
<li><a href="https://github.com/PacificBiosciences/Bioinformatics-Training/wiki/Bioinformatics-Workshop">Bioinformatics Workshop (Webinars)</a></li>
<li><a href="https://github.com/PacificBiosciences/Bioinformatics-Training/wiki/Bioinformatics-Training-Slides">Bioinformatics Training Slides</a></li>
</ul>
<p>Specialized Applications:</p>
<ul>
<li><a href="https://github.com/PacificBiosciences/Bioinformatics-Training/wiki/De-Novo-Assembly">De Novo Assembly</a></li>
<li><a href="https://github.com/PacificBiosciences/cDNA_primer/wiki">Transcriptome analysis</a></li>
<li><a href="https://github.com/PacificBiosciences/Bioinformatics-Training/wiki/Base-modification-analysis">Base Modification Analysis</a></li>
<li><a href="https://github.com/PacificBiosciences/Bioinformatics-Training/wiki/Barcoding">Barcoding</a></li>
<li><a href="https://github.com/PacificBiosciences/Bioinformatics-Training/wiki/Data-Analysis-Tools">Data Analysis Tools</a></li>
<li><a href="https://github.com/PacificBiosciences/Bioinformatics-Training/wiki/Minor-Variants-and-Phasing-Analysis">Minor Variants and Phasing Analysis</a></li>
</ul><p>Address of the bookmark: <a href="https://github.com/PacificBiosciences/Bioinformatics-Training/wiki" rel="nofollow">https://github.com/PacificBiosciences/Bioinformatics-Training/wiki</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30973/abacas</guid>
	<pubDate>Thu, 16 Feb 2017 12:15:55 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30973/abacas</link>
	<title><![CDATA[ABACAS]]></title>
	<description><![CDATA[<p><span>ABACAS is intended to rapidly contiguate (align, order, orientate) , visualize and design primers to close gaps on shotgun assembled contigs based on a reference sequence. It uses MUMmer to find alignment positions and identify syntenies of assembly contigs against the reference. The output is then processed to generate a pseudomolecule taking overlaping contigs and gaps in to account. MUMmer's alignment generating programs, Nucmer and Promer are used followed by the 'delta-filter' utility function. Users could also run tblastx on contigs that are not used to generate the pseudomolecule.&nbsp;</span></p><p>Address of the bookmark: <a href="http://abacas.sourceforge.net/Manual.html#9._Colour_code" rel="nofollow">http://abacas.sourceforge.net/Manual.html#9._Colour_code</a></p>]]></description>
	<dc:creator>Surabhi Chaudhary</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31137/finishersc-a-repeat-aware-and-scalable-tool-for-upgrading-de-novo-assembly-using-long-reads</guid>
	<pubDate>Mon, 27 Feb 2017 09:49:45 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31137/finishersc-a-repeat-aware-and-scalable-tool-for-upgrading-de-novo-assembly-using-long-reads</link>
	<title><![CDATA[FinisherSC: a repeat-aware and scalable tool for upgrading de novo assembly using long reads]]></title>
	<description><![CDATA[<p><span>FinisherSC, a repeat-aware and scalable tool for upgrading&nbsp;</span><em>de novo</em><span>&nbsp;assembly using long reads. Experiments with real data suggest that FinisherSC can provide longer and higher quality contigs than existing tools while maintaining high concordance.</span></p><p>Address of the bookmark: <a href="http://kakitone.github.io/finishingTool/" rel="nofollow">http://kakitone.github.io/finishingTool/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31300/clgenomics</guid>
	<pubDate>Fri, 03 Mar 2017 09:57:28 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31300/clgenomics</link>
	<title><![CDATA[CLgenomics]]></title>
	<description><![CDATA[<p>CLgenomics is a standalone desktop software specifically designed for bacterial genome analysis. This program has a powerful multi-genome browser, which enables rapid and responsive exploration of bacterial genomes.</p>
<p>To use CLgenomics, individual genome data (genome sequences + annotation details) are compiled and saved in a specially formatted file called CLG (ChunLab Genomics).&nbsp;Each CLG file corresponds with one bacterial genome. If multiple genomes are being considered and compared, multiple CLG files are needed. ChunLab offers &gt;40,000 CLG files of publicly available Bacterial and Archaeal genomes.</p><p>Address of the bookmark: <a href="https://chunlab.wordpress.com/clgenomics-software/" rel="nofollow">https://chunlab.wordpress.com/clgenomics-software/</a></p>]]></description>
	<dc:creator>Radha Agarkar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31353/concoct-clustering-contigs-with-coverage-and-composition</guid>
	<pubDate>Mon, 06 Mar 2017 04:08:16 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31353/concoct-clustering-contigs-with-coverage-and-composition</link>
	<title><![CDATA[CONCOCT: Clustering cONtigs with COverage and ComposiTion]]></title>
	<description><![CDATA[<p>A program for unsupervised binning of metagenomic contigs by using nucleotide composition, coverage data in multiple samples and linkage data from paired end reads.</p>
<p>Warning! This software is to be considered under development. Functionality and the user interface may still change significantly from one version to another. If you want to use this software, please stay up to date with the list of known issues:<a href="https://github.com/BinPro/CONCOCT/issues">https://github.com/BinPro/CONCOCT/issues</a></p><p>Address of the bookmark: <a href="https://github.com/BinPro/CONCOCT" rel="nofollow">https://github.com/BinPro/CONCOCT</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31382/seqmule-automated-human-exomegenome-variants-detection</guid>
	<pubDate>Tue, 07 Mar 2017 10:12:36 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31382/seqmule-automated-human-exomegenome-variants-detection</link>
	<title><![CDATA[SeqMule: Automated human exome/genome variants detection]]></title>
	<description><![CDATA[<p><span>SeqMule takes single-end or paird-end FASTQ or BAM files, generates a script consisting of more than 10 popular alignment, analysis tools and runs the script line by line. Users can change the pipeline or fine-tune the parameters by modifying its configuration file. SeqMule also has some built-in functions, such as pooling consensus calls from various callers, plotting a Venn diagram showing intersection among different callers, and downloading databases. SeqMule can be used for both Mendelian disease study and cancer genome study.</span></p><p>Address of the bookmark: <a href="http://seqmule.openbioinformatics.org/en/latest/" rel="nofollow">http://seqmule.openbioinformatics.org/en/latest/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32152/upsetr-shiny-app</guid>
	<pubDate>Fri, 14 Apr 2017 06:19:54 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32152/upsetr-shiny-app</link>
	<title><![CDATA[UpSetR Shiny App!]]></title>
	<description><![CDATA[<p>UpSetR generates static&nbsp;<a href="http://vcg.github.io/upset/?dataset=0&amp;duration=1000&amp;orderBy=subsetSize&amp;grouping=groupByIntersectionSize&amp;selection=">UpSet plots</a>. The UpSet technique visualizes set intersections in a matrix layout and introduces aggregates based on groupings and queries. The matrix layout enables the effective representation of associated data, such as the number of elements in the aggregates and intersections, as well as additional summary statistics derived from subset or element attributes.</p>
<h4>To begin, input your data using one of the three input styles.</h4>
<ol>
<li>"File" takes a correctly formatted.csv file.</li>
<li>"List" takes up to 6 different lists that contain unique elements, similar to that used in the web applications BioVenn&nbsp;<a href="http://www.biomedcentral.com/content/pdf/1471-2164-9-488.pdf">(Hulsen et al., 2008)</a>&nbsp;and jvenn&nbsp;<a href="http://www.biomedcentral.com/content/pdf/1471-2105-15-293.pdf">(Bardou et al., 2014)</a></li>
<li>"Expression" takes the input used by the venneuler R package&nbsp;<a href="https://cran.r-project.org/web/packages/venneuler/venneuler.pdf">(Wilkinson, 2015)</a></li>
</ol><p>Address of the bookmark: <a href="https://gehlenborglab.shinyapps.io/upsetr/" rel="nofollow">https://gehlenborglab.shinyapps.io/upsetr/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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