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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/43806?offset=140</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29018/crossmap</guid>
	<pubDate>Mon, 05 Sep 2016 04:07:38 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29018/crossmap</link>
	<title><![CDATA[CrossMap]]></title>
	<description><![CDATA[<ul>
<li>CrossMap is a program for convenient conversion of genome coordinates (or annotation files) between&nbsp;<em>different assemblies</em>&nbsp;(such as Human&nbsp;<a href="http://www.ncbi.nlm.nih.gov/assembly/2928/">hg18 (NCBI36)</a>&nbsp;&lt;&gt;&nbsp;<a href="http://www.ncbi.nlm.nih.gov/assembly/2758/">hg19 (GRCh37)</a>, Mouse&nbsp;<a href="http://www.ncbi.nlm.nih.gov/assembly/165668/">mm9 (MGSCv37)</a>&nbsp;&lt;&gt;&nbsp;<a href="http://www.ncbi.nlm.nih.gov/assembly/327618/">mm10 (GRCm38)</a>).</li>
<li>It supports most commonly used file formats including SAM/BAM, Wiggle/BigWig, BED, GFF/GTF, VCF.</li>
<li>CrossMap is designed to liftover genome coordinates between assemblies. It&rsquo;s&nbsp;<em>not</em>&nbsp;a program for aligning sequences to reference genome.</li>
<li>We&nbsp;<em>do not</em>&nbsp;recommend using CrossMap to convert genome coordinates between species.</li>
</ul><p>Address of the bookmark: <a href="http://crossmap.sourceforge.net/" rel="nofollow">http://crossmap.sourceforge.net/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</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/28884/tgnet</guid>
	<pubDate>Wed, 24 Aug 2016 05:36:36 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28884/tgnet</link>
	<title><![CDATA[TGNet]]></title>
	<description><![CDATA[<p><span>Recent technological progress has greatly facilitated&nbsp;</span><em>de novo</em><span>&nbsp;genome sequencing. However,&nbsp;</span><em>de novo</em><span>&nbsp;assemblies consist in many pieces of contiguous sequence (contigs) arranged in thousands of scaffolds instead of small numbers of chromosomes. Confirming and improving the quality of such assemblies is critical for subsequent analysis.&nbsp;</span></p>
<p>Visualization and quality assessment of de novo genome assemblies</p>
<p>Citation</p>
<p>This software is fully described in the paper:<br>Riba-Grognuz, Keller, Falquet, Xenarios &amp; Wurm (2011) Visualization and quality assessment of de novo genome assemblies.</p>
<p>In brief, our scripts create Cytoscape files to visualize transcript evidence that suggests adjacency between scaffolds and contigs.</p>
<p>Software requirements</p>
<p>BLAT (tested with Standalone BLAT v. 32&times;1). Source Binaries .<br>Cytoscape (tested with versions 2.7.0, 2.8.2)<br>a UNIX machine (tested on Mac OS X 10.6 and CentOS 4.6)</p><p>Address of the bookmark: <a href="https://github.com/ksanao/TGNet" rel="nofollow">https://github.com/ksanao/TGNet</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28915/useful-bioinformatics-tools</guid>
	<pubDate>Mon, 29 Aug 2016 04:08:12 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28915/useful-bioinformatics-tools</link>
	<title><![CDATA[Useful Bioinformatics Tools]]></title>
	<description><![CDATA[<p>Collections of few handy tools for bioinformatician</p>
<p>http://molbiol-tools.ca/Convert.htm</p><p>Address of the bookmark: <a href="http://molbiol-tools.ca/Convert.htm" rel="nofollow">http://molbiol-tools.ca/Convert.htm</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28997/braker-pipeline-for-fully-automated-prediction-of-protein-coding-genes-with-genemark-eset-and-augustus-in-novel-eukaryotic-genomes</guid>
	<pubDate>Thu, 01 Sep 2016 08:02:59 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28997/braker-pipeline-for-fully-automated-prediction-of-protein-coding-genes-with-genemark-eset-and-augustus-in-novel-eukaryotic-genomes</link>
	<title><![CDATA[BRAKER: pipeline for fully automated prediction of protein coding genes with GeneMark-ES/ET and AUGUSTUS in novel eukaryotic genomes]]></title>
	<description><![CDATA[<p><span>Gene finding in eukaryotic genomes is notoriously difficult to automate. The task is to design a work flow with a minimal set of tools that would reach state-of-the-art performance across a wide range of species. GeneMark-ET is a gene prediction tool that incorporates RNA-Seq data into unsupervised training and subsequently generates ab initio gene predictions. AUGUSTUS is a gene finder that usually requires supervised training and uses information from RNA-Seq reads in the prediction step. Complementary strengths of GeneMark-ET and AUGUSTUS provided motivation for designing a new combined tool for automatic gene prediction.</span></p>
<p>http://www.ncbi.nlm.nih.gov/pubmed/26559507</p><p>Address of the bookmark: <a href="http://bioinf.uni-greifswald.de/bioinf/braker/" rel="nofollow">http://bioinf.uni-greifswald.de/bioinf/braker/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29029/ngs-tutorial</guid>
	<pubDate>Mon, 05 Sep 2016 09:50:46 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29029/ngs-tutorial</link>
	<title><![CDATA[NGS Tutorial]]></title>
	<description><![CDATA[<p><span>These tutorials are written for hundreds of bioinformaticians trying to cope with large volume of next-generation sequencing (NGS) data. NGS technologies brought a dramatic shift in the world of sequencing. Merely five years back, genome sequencing of higher eukaryotes used to be very expensive endeavor. To get a genome of interest sequenced, hundreds of scientists had to raise funds together by writing a joint white-paper and petitioning to various government agencies. The tasks of sequencing and assembly were handled by dedicated sequencing facilities, of which only a few existed around the globe. Naturally, the capacities at those sequencing facilities were significantly constrained from high volume of requests</span></p><p>Address of the bookmark: <a href="http://www.homolog.us/Tutorials/index.php" rel="nofollow">http://www.homolog.us/Tutorials/index.php</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29130/gage-genome-assembly-gold-standard-evaluation</guid>
	<pubDate>Wed, 07 Sep 2016 07:35:49 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29130/gage-genome-assembly-gold-standard-evaluation</link>
	<title><![CDATA[GAGE : Genome Assembly Gold-standard Evaluation]]></title>
	<description><![CDATA[<p><span>GAGE is an evaluation of the very latest large-scale genome assembly algorithms. We have organized this "bake-off" as an attempt to produce a realistic assessment of genome assembly software in a rapidly changing field of next-generation sequencing. The main results of GAGE have now been published in the journal Genome Research:&nbsp;</span><a href="http://genome.cshlp.org/content/early/2012/01/12/gr.131383.111">GAGE: A critical evaluation of genome assemblies and assembly algorithms</a><span>.</span></p>
<p><span>http://genome.cshlp.org/content/early/2012/01/12/gr.131383.111</span></p><p>Address of the bookmark: <a href="http://gage.cbcb.umd.edu/index.html" rel="nofollow">http://gage.cbcb.umd.edu/index.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29270/blast-ring-image-generator-brig</guid>
	<pubDate>Fri, 30 Sep 2016 09:18:50 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29270/blast-ring-image-generator-brig</link>
	<title><![CDATA[BLAST Ring Image Generator (BRIG)]]></title>
	<description><![CDATA[<p>BRIG is a free cross-platform (Windows/Mac/Unix) application that can display circular comparisons between a large number of genomes, with a focus on handling genome assembly data. The application is available at: <a href="http://sourceforge.net/projects/brig">http://sourceforge.net/projects/brig</a></p>
<p>If you have any questions or comments, post them on <a href="http://sourceforge.net/tracker/?group_id=328245">one of the trackers</a> on BRIG&rsquo;s SourceForge page: <a href="http://sourceforge.net/tracker/?group_id=328245">http://sourceforge.net/tracker/?group_id=328245</a>.</p>
<p>Features:</p>
<ul>
<li>Images show similarity between a central reference sequence and other sequences as concentric rings.</li>
<li>BRIG will perform all BLAST comparisons and file parsing automatically via a simple GUI.</li>
<li>Contig boundaries and read coverage can be displayed for draft genomes; customized graphs and annotations can be displayed.</li>
<li>Using a user-defined set of genes as input, BRIG can display gene presence, absence, truncation or sequence variation in a set of complete genomes, draft genomes or even raw, unassembled sequence data.</li>
<li>BRIG also accepts SAM-formatted read-mapping files enabling genomic regions present in unassembled sequence data from multiple samples to be compared simultaneously</li>
</ul><p>Address of the bookmark: <a href="http://brig.sourceforge.net/" rel="nofollow">http://brig.sourceforge.net/</a></p>]]></description>
	<dc:creator>Anjana</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/29485/ribbon</guid>
	<pubDate>Fri, 21 Oct 2016 04:54:30 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29485/ribbon</link>
	<title><![CDATA[Ribbon !!]]></title>
	<description><![CDATA[<p><span>Visualization has played an extremely important role in the current genomic revolution to inspect and understand variants, expression patterns, evolutionary changes, and a number of other relationships. However, most of the information in read-to-reference or genome-genome alignments is lost for structural variations in the one-dimensional views of most genome browsers showing only reference coordinates. Instead, structural variations captured by long reads or assembled contigs often need more context to understand, including alignments and other genomic information from multiple chromosomes. We have addressed this problem by creating Ribbon (genomeribbon.com) an interactive online visualization tool that displays alignments along both reference and query sequences, along with any associated variant calls in the sample. This way Ribbon shows patterns in alignments of many reads across multiple chromosomes, while allowing detailed inspection of individual reads (Supplementary Note 1). For example, here we show a gene fusion in the SK-BR-3 breast cancer cell line linking the genes CYTH1 and EIF3H. While it has been found in the transcriptome previously, genome sequencing did not identify a direct chromosomal fusion between these two genes. After SMRT sequencing, Ribbon shows that there are indeed long reads that span from one gene to the other, going through not one but two variants, for the first time showing the genomic link between these two genes (Figure 1a). More gene fusions of this cancer cell line are investigated in Supplementary Note 2. Figure 1b shows another complex event in this sample made simple in Ribbon: the translocation of a 4.4 kb sequence deleted from chr19 and inserted into chr16 (Figure 1b). Thus, Ribbon enables understanding of complex variants, and it may also help in the detection of sequencing and sample preparation issues, testing of aligners and variant-callers, and rapid curation of structural variant candidates (Supplementary Note 3). In addition to SAM and BAM files with long, short, or paired-end reads, Ribbon can also load coordinate files from whole genome aligners such as MUMmer. Therefore, Ribbon can be used to test assembly algorithms or inspect the similarity between species. Supplementary Note 4 shows a comparison of gorilla and human genomes using Ribbon, highlighting major structural differences. In conclusion, Ribbon is a powerful interactive web tool for viewing complex genomic alignments.</span></p>
<p>Script at&nbsp;https://github.com/MariaNattestad/ribbon</p><p>Address of the bookmark: <a href="http://genomeribbon.com/" rel="nofollow">http://genomeribbon.com/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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