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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/37409?offset=190</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26525/ensembl-comparative-genomics-resources</guid>
	<pubDate>Sun, 28 Feb 2016 17:10:20 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26525/ensembl-comparative-genomics-resources</link>
	<title><![CDATA[Ensembl comparative genomics resources]]></title>
	<description><![CDATA[<div>
<p>The Ensembl comparative genomics resources are one such reference set that facilitates comprehensive and reproducible analysis of chordate genome data. Ensembl computes pairwise and multiple whole-genome alignments from which large-scale synteny, per-base conservation scores and constrained elements are obtained. Gene alignments are used to define Ensembl Protein Families, GeneTrees and homologies for both protein-coding and non-coding RNA genes. These resources are updated frequently and have a consistent informatics infrastructure and data presentation across all supported species. Specialized web-based visualizations are also available including synteny displays, collapsible gene tree plots, a gene family locator and different alignment views. The Ensembl comparative genomics infrastructure is extensively reused for the analysis of non-vertebrate species by other projects including Ensembl Genomes and Gramene and much of the information here is relevant to these projects. The consistency of the annotation across species and the focus on vertebrates makes Ensembl an ideal system to perform and support vertebrate comparative genomic analyses. We use robust software and pipelines to produce reference comparative data and make it freely available.</p>
<p><strong>Database URL:</strong> <a href="http://www.ensembl.org" target="pmc_ext">http://www.ensembl.org</a>.</p>
</div><p>Address of the bookmark: <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4761110/" rel="nofollow">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4761110/</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27427/rcircos-an-r-package-for-circos-2d-track-plots</guid>
	<pubDate>Fri, 20 May 2016 11:01:13 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27427/rcircos-an-r-package-for-circos-2d-track-plots</link>
	<title><![CDATA[RCircos: an R package for Circos 2D track plots]]></title>
	<description><![CDATA[<p>RCircos package provides a simple and flexible way to make Circos 2D track plots with R and could be easily integrated into other R data processing and graphic manipulation pipelines for presenting large-scale multi-sample genomic research data. It can also serve as a base tool to generate complex Circos images.</p>
<p>More at https://bitbucket.org/henryhzhang/rcircos/src</p><p>Address of the bookmark: <a href="https://bitbucket.org/henryhzhang/rcircos/src" rel="nofollow">https://bitbucket.org/henryhzhang/rcircos/src</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28835/a5-miseq</guid>
	<pubDate>Thu, 18 Aug 2016 04:05:23 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28835/a5-miseq</link>
	<title><![CDATA[A5-miseq]]></title>
	<description><![CDATA[<p><span><span>_A5-miseq_ is a pipeline for assembling DNA sequence data generated on the Illumina sequencing platform. This README will take you through the steps necessary for running _A5-miseq_. </span></span></p>
<p><span>Point to note:</span></p>
<p><span>There are many situations where A5-miseq is not the right tool for the job. In order to produce accurate results, A5-miseq requires Illumina data with certain characteristics. A5-miseq will likely not work well with Illumina reads shorter than around 80nt, or reads where the base qualities are low in all or most reads before 60nt. A5-miseq assumes it is assembling homozygous haploid genomes. Use a different assembler for metagenomes and heterozygous diploid or polyploid organisms. Use a different assembler if a tool like FastQC reports your data quality is dubious. You have been warned! Datasets consisting solely of unpaired reads are not currently supported.</span></p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/ngopt/" rel="nofollow">https://sourceforge.net/projects/ngopt/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<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/28855/vcfr</guid>
	<pubDate>Fri, 19 Aug 2016 07:38:24 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28855/vcfr</link>
	<title><![CDATA[vcfR]]></title>
	<description><![CDATA[<p><span>Most variant calling pipelines result in files containing large quantities of variant information. The&nbsp;</span><a href="http://samtools.github.io/hts-specs/" title="VCF format at hts-specs">variant call format (vcf)</a><span>&nbsp;is an increasingly popular format for this data. The format of these files and their content is discussed in the vignette &lsquo;vcf data.&rsquo; These files are typically intended to be post-processed (i.e., filtered) as an attempt to remove false positives or otherwise problematic sites. The R package vcfR provides tools to facilitate this filtering as well as to visualize the effects of choices made during this process.</span></p><p>Address of the bookmark: <a href="https://cran.r-project.org/web/packages/vcfR/vignettes/visualization_1.html" rel="nofollow">https://cran.r-project.org/web/packages/vcfR/vignettes/visualization_1.html</a></p>]]></description>
	<dc:creator>Archana Malhotra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28891/lumpy</guid>
	<pubDate>Thu, 25 Aug 2016 08:05:02 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28891/lumpy</link>
	<title><![CDATA[LUMPY]]></title>
	<description><![CDATA[<p>A probabilistic framework for structural variant discovery.</p>
<p>Ryan M Layer, Colby Chiang, Aaron R Quinlan, and Ira M Hall. 2014. "LUMPY: a Probabilistic Framework for Structural Variant Discovery." Genome Biology 15 (6): R84.&nbsp;<a href="http://dx.doi.org/10.1186/gb-2014-15-6-r84">doi:10.1186/gb-2014-15-6-r84</a>.</p>
<p>More at&nbsp;https://github.com/arq5x/lumpy-sv</p><p>Address of the bookmark: <a href="https://github.com/arq5x/lumpy-sv" rel="nofollow">https://github.com/arq5x/lumpy-sv</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28922/ka-ks-and-kaks-calculations</guid>
	<pubDate>Mon, 29 Aug 2016 11:44:11 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28922/ka-ks-and-kaks-calculations</link>
	<title><![CDATA[Ka, Ks and Ka/Ks calculations]]></title>
	<description><![CDATA[<p>gKaKs is a codon-based genome-level Ka/Ks computation pipeline developed and based on programs from four widely used packages: BLAT, BLASTALL (including bl2seq, formatdb and fastacmd), PAML (including codeml and yn00) and KaKs_Calculator (including 10 substitution rate estimation methods). gKaKs can automatically detect and eliminate frameshift mutations and premature stop codons to compute the substitution rates (Ka, Ks and Ka/Ks) between a well-annotated genome and a non-annotated genome or even a poorly assembled scaffold dataset. It is especially useful for newly sequenced genomes that have not been well annotated.&nbsp;</p>
<p>Look for KaKs calculation:</p>
<p>https://github.com/fumba/kaks-calculator</p>
<p>http://longlab.uchicago.edu/?q=gKaKs</p>
<p>http://www.ncbi.nlm.nih.gov/pubmed/23314322</p><p>Address of the bookmark: <a href="http://longlab.uchicago.edu/?q=gKaKs" rel="nofollow">http://longlab.uchicago.edu/?q=gKaKs</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29004/r-chie</guid>
	<pubDate>Thu, 01 Sep 2016 11:47:24 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29004/r-chie</link>
	<title><![CDATA[R-chie]]></title>
	<description><![CDATA[<p><strong>R-chie</strong><span>&nbsp;allows you to make arc diagrams of RNA secondary structures, allowing for easy comparison and overlap of two structures, rank and display basepairs in colour and to also visualize corresponding multiple sequence alignments and co-variation information.</span><br><strong>R4RNA</strong><span>&nbsp;is the R package powering R-chie, available for&nbsp;</span><a href="http://www.e-rna.org/r-chie/download.cgi">download</a><span>&nbsp;and local use for more customized figures and scripting.</span></p>
<p>http://www.e-rna.org/r-chie/plot.cgi?eg=single</p><p>Address of the bookmark: <a href="http://www.e-rna.org/r-chie/plot.cgi?eg=single" rel="nofollow">http://www.e-rna.org/r-chie/plot.cgi?eg=single</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/file/view/29110/structural-variants-ppt</guid>
	<pubDate>Wed, 07 Sep 2016 03:16:09 -0500</pubDate>
	<link>https://bioinformaticsonline.com/file/view/29110/structural-variants-ppt</link>
	<title><![CDATA[Structural variants PPT]]></title>
	<description><![CDATA[<p>1000 Genomes data tutorial at ASHG</p><p>Structural variants presentation by</p><p>Jan Korbel</p><p>European Molecular Biology Laboratory (EMBL) Heidelberg Genome Biology Research Unit</p><p>Reference:&nbsp;</p><p>https://www.genome.gov/pages/research/der/1000genomesprojecttutorials/structuralvariants-jankorbel.pdf</p>]]></description>
	<dc:creator>Jit</dc:creator>
	<enclosure url="https://bioinformaticsonline.com/file/download/29110" length="1090837" type="application/pdf" />
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29382/virmet</guid>
	<pubDate>Mon, 10 Oct 2016 08:27:19 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29382/virmet</link>
	<title><![CDATA[VirMet]]></title>
	<description><![CDATA[<p>Watch out: only a few files are counted in coverage statistics.</p>
<p>Full documentation on&nbsp;<a href="http://virmet.rtfd.org/en/latest/">Read the Docs</a>.</p>
<p>A set of tools for viral metagenomics.</p>
<p>virmet is called with a command subcommand syntax:&nbsp;<code>virmet fetch --viral n</code>, for example, downloads the bacterial database. Other available subcommands so far are</p>
<ul>
<li><code>fetch</code>&nbsp;download genomes</li>
<li><code>update</code>&nbsp;update viral/bacterial database</li>
<li><code>index</code>&nbsp;index genomes</li>
<li><code>wolfpack</code>&nbsp;analyze a Miseq run</li>
<li><code>covplot</code>&nbsp;plot coverage for a specific organism</li>
</ul>
<p>A short help is obtained with&nbsp;<code>virmet subcommand -h</code>.</p>
<p>More at&nbsp;https://github.com/ozagordi/VirMet</p><p>Address of the bookmark: <a href="https://github.com/ozagordi/VirMet" rel="nofollow">https://github.com/ozagordi/VirMet</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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