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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/44663?offset=180</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/36418/r-350-has-been-released</guid>
	<pubDate>Thu, 26 Apr 2018 11:31:58 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/36418/r-350-has-been-released</link>
	<title><![CDATA[R 3.5.0 has been Released!]]></title>
	<description><![CDATA[<ul>
<li>The latest version of R is a major release! It comes with a ton of new features, including performance and speed improvements</li>
<li>All R packages will now be byte-compiled, hence boosting packages installed from GitHub</li>
<li>You may need to re-install all previously installed R packages; old scripts however will continue to work normally</li>
</ul><p>More at&nbsp;<a href="https://cran.r-project.org/doc/manuals/r-release/NEWS.html">https://cran.r-project.org/doc/manuals/r-release/NEWS.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37732/making-2d-hilbert-curve</guid>
	<pubDate>Mon, 17 Sep 2018 05:43:35 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37732/making-2d-hilbert-curve</link>
	<title><![CDATA[Making 2D Hilbert Curve]]></title>
	<description><![CDATA[<p><a href="https://en.wikipedia.org/wiki/Hilbert_curve">Hilbert curve</a>&nbsp;is a type of space-filling curves that folds one dimensional axis into a two dimensional space, but still keeps the locality. It has advantages to visualize data with long axis in following two aspects:</p>
<ol>
<li>greatly improve resolution of the visualization fron n to&nbsp;<span><span><span><span><span><span><span>&radic;</span></span><span><span><span><span>n</span></span></span></span></span></span></span></span><span>n</span></span>;</li>
<li>easy to visualize clusters because generally data points in the axis will also be close in the 2D space.</li>
</ol>
<p>This package aims to provide an easy and flexible way to visualize data through Hilbert curve. The implementation and example figures are based on following sources:</p>
<ul>
<li><a href="http://mkweb.bcgsc.ca/hilbert/">http://mkweb.bcgsc.ca/hilbert/</a></li>
<li><a href="http://corte.si/posts/code/hilbert/portrait/index.html">http://corte.si/posts/code/hilbert/portrait/index.html</a></li>
<li><a href="http://bioconductor.org/packages/devel/bioc/html/HilbertVis.html">http://bioconductor.org/packages/devel/bioc/html/HilbertVis.html</a></li>
</ul><p>Address of the bookmark: <a href="https://bioconductor.org/packages/devel/bioc/vignettes/HilbertCurve/inst/doc/HilbertCurve.html" rel="nofollow">https://bioconductor.org/packages/devel/bioc/vignettes/HilbertCurve/inst/doc/HilbertCurve.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38443/genoplotr-plot-gene-and-genome-maps-project</guid>
	<pubDate>Wed, 12 Dec 2018 08:33:41 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38443/genoplotr-plot-gene-and-genome-maps-project</link>
	<title><![CDATA[genoPlotR - plot gene and genome maps project!]]></title>
	<description><![CDATA[<p>genoPlotR is a R package to produce reproducible, publication-grade graphics of gene and genome maps. It allows the user to read from usual format such as protein table files and blast results, as well as home-made tabular files.</p>
<h3>Features</h3>
<ul>
<li>Linear representation of several segments of DNA</li>
<li>Comparisons represented by areas between the segments (like Artemis, for example)</li>
<li>Reads from common formats: Genbank, EMBL, blast, Mauve, and from user-generated tab files</li>
<li>Plot several subsegments of the same segment on the same line, separated by a //</li>
<li>Automatic or manual placement of the segments on the plot</li>
<li>Add annotations to all the lines</li>
<li>Create smart, automatic annotations for genomes, based on gene names</li>
<li>Add a user-generated tree</li>
<li>Add a global scale or a scale to each line</li>
<li>Use user-defined graphical functions to represent genes</li>
<li></li>
</ul><p>Address of the bookmark: <a href="http://genoplotr.r-forge.r-project.org/" rel="nofollow">http://genoplotr.r-forge.r-project.org/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39956/alluvial-diagram</guid>
	<pubDate>Sat, 21 Sep 2019 07:31:08 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39956/alluvial-diagram</link>
	<title><![CDATA[alluvial diagram]]></title>
	<description><![CDATA[<p><span style="color: #000000; font-size: 14px; font-style: normal; font-weight: 400; text-align: start; background-color: #ffffff; float: none;">Alluvial diagram is a variant of a Parallel Coordinates Plot (PCP) but for categorical variables. Variables are assigned to vertical axes that are parallel. Values are represented with blocks on each axis. Observations are represented with<span>&nbsp;</span></span><em style="color: #000000; font-size: 14px; font-weight: 400; text-align: start; background-color: #ffffff;">alluvia</em><span style="color: #000000; font-size: 14px; font-style: normal; font-weight: 400; text-align: start; background-color: #ffffff; float: none;"><span>&nbsp;</span>(sing. &ldquo;alluvium&rdquo;) spanning across all the axes.</span></p><p>Address of the bookmark: <a href="https://cran.r-project.org/web/packages/alluvial/vignettes/alluvial.html" rel="nofollow">https://cran.r-project.org/web/packages/alluvial/vignettes/alluvial.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40721/efs-an-ensemble-feature-selection-tool-implemented-as-r-package-and-web-application</guid>
	<pubDate>Tue, 28 Jan 2020 05:12:23 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40721/efs-an-ensemble-feature-selection-tool-implemented-as-r-package-and-web-application</link>
	<title><![CDATA[EFS: an ensemble feature selection tool implemented as R-package and web-application]]></title>
	<description><![CDATA[<p><span>The software EFS (Ensemble Feature Selection) makes use of multiple feature selection methods and combines their normalized outputs to a quantitative ensemble importance. Currently, eight different feature selection methods have been integrated in EFS, which can be used separately or combined in an ensemble.</span></p>
<p><a href="https://biodatamining.biomedcentral.com/articles/10.1186/s13040-017-0142-8">https://biodatamining.biomedcentral.com/articles/10.1186/s13040-017-0142-8</a></p><p>Address of the bookmark: <a href="http://efs.heiderlab.de/" rel="nofollow">http://efs.heiderlab.de/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41272/rainbowr-reliable-association-inference-by-optimizing-weights-with-r-r-package-for-snp-set-gwas-and-multi-kernel-mixed-model</guid>
	<pubDate>Fri, 28 Feb 2020 23:27:37 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41272/rainbowr-reliable-association-inference-by-optimizing-weights-with-r-r-package-for-snp-set-gwas-and-multi-kernel-mixed-model</link>
	<title><![CDATA[RAINBOWR: Reliable Association INference By Optimizing Weights with R (R package for SNP-set GWAS and multi-kernel mixed model)]]></title>
	<description><![CDATA[<p><code>RAINBOWR</code>(Reliable Association INference By Optimizing Weights with R) is a package to perform several types of <code>GWAS</code> as follows.</p>
<ul>
<li>Single-SNP GWAS with <code>RGWAS.normal</code> function</li>
<li>SNP-set (or gene set) GWAS with <code>RGWAS.multisnp</code> function (which tests multiple SNPs at the same time)</li>
<li>Check epistatic (SNP-set x SNP-set interaction) effects with <code>RGWAS.epistasis</code> (very slow and less reliable)</li>
</ul>
<p>https://github.com/KosukeHamazaki/RAINBOWR</p>
<p>https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007663</p>
<p>https://cran.r-project.org/web/packages/RAINBOWR/index.html</p><p>Address of the bookmark: <a href="https://github.com/KosukeHamazaki/RAINBOWR" rel="nofollow">https://github.com/KosukeHamazaki/RAINBOWR</a></p>]]></description>
	<dc:creator>Surabhi Chaudhary</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43848/r-shiny-in-life-sciences-%E2%80%93-top-7-dashboard-examples</guid>
	<pubDate>Fri, 01 Apr 2022 19:05:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43848/r-shiny-in-life-sciences-%E2%80%93-top-7-dashboard-examples</link>
	<title><![CDATA[R Shiny in Life Sciences – Top 7 Dashboard Examples]]></title>
	<description><![CDATA[<p><span>&nbsp;R Shiny is one of the easiest ways for developers to make production-ready dashboards when speed and functionality are crucial. Shiny is approachable with a lot of documentation available, and because of this, a lot of developers/researchers with non-coding backgrounds are able to produce some impressive results. The whole ecosystem is easy to get your head around and pretty much limitless with regard to what you can do.</span></p><p>Address of the bookmark: <a href="https://www.r-bloggers.com/2022/03/r-shiny-in-life-sciences-top-7-dashboard-examples/" rel="nofollow">https://www.r-bloggers.com/2022/03/r-shiny-in-life-sciences-top-7-dashboard-examples/</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/26569/genome-stability-laboratory</guid>
  <pubDate>Mon, 07 Mar 2016 04:16:32 -0600</pubDate>
  <link></link>
  <title><![CDATA[Genome Stability Laboratory]]></title>
  <description><![CDATA[
<p>The bakers yeast, Saccharomyces cerevisiae is an ideal model organism to understand mechanisms of meiotic chromosome segregation. In S. cerevisiae and in mammals, the majority of meiotic crossovers are formed through a highly conserved MSH4p-MSH5p, MLH1p-MLH3p dependent pathway. We are interested in charactering the role of these complexes in crossover formation and distribution among all homolog pairs. Errors in this process are linked to congenital birth defects in humans such as Down's syndrome.Our laboratory is also interested in understanding the effect of genetic background on mutation rate variation using S. cerevisiae as a model. These studies are relevant for understanding cancer progression, genome evolution and architecture. We use high- throughput genomic methods as well as classical genetics to achieve these aims. </p>

<p>More at http://faculty.iisertvm.ac.in/~nishantkt/index.html</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37820/s-plot2-rapid-visual-and-statistical-analysis-of-genomic-sequences</guid>
	<pubDate>Tue, 02 Oct 2018 17:57:27 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37820/s-plot2-rapid-visual-and-statistical-analysis-of-genomic-sequences</link>
	<title><![CDATA[S-plot2: Rapid Visual and Statistical Analysis of Genomic Sequences]]></title>
	<description><![CDATA[<p><span>S-plot2 creates an interactive, two-dimensional heatmap capturing the similarities and dissimilarities in nucleotide usage between genomic sequences (partial or complete). In S-plot2, whole eukaryotic chromosomes and smaller prokaryotic genomes can be efficiently compared. The tool includes functionality to extract, analyze, and automate BLAST queries of regions of interest within the heatmap. This facilitates the investigation of quickly evolving coding regions, novel coding regions, and laterally transferred elements.</span></p><p>Address of the bookmark: <a href="https://bitbucket.org/lkalesinskas/splot" rel="nofollow">https://bitbucket.org/lkalesinskas/splot</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41948/predict-gene-ontology-with-sequences</guid>
	<pubDate>Wed, 08 Jul 2020 04:59:28 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41948/predict-gene-ontology-with-sequences</link>
	<title><![CDATA[Predict Gene Ontology with sequences !]]></title>
	<description><![CDATA[<p><strong>PANNZER</strong>&nbsp;(Protein ANNotation with Z-scoRE) is a fully automated service for functional annotation of prokaryotic and eukaryotic proteins of unknown function. The tool is designed to predict the functional description (DE) and GO classes.</p>
<p>PANNZER2 processes bacterial proteomes in minutes and eukaryotic proteomes in an hour. You can use&nbsp;<a href="http://ekhidna2.biocenter.helsinki.fi/AAI/">AAI-profiler</a>&nbsp;to summarize a proteome's species neighbors and reveal taxonomic identity or contamination.</p>
<p>http://ekhidna2.biocenter.helsinki.fi/sanspanz/</p>
<p>IterPro is for the beginners</p>
<p><a href="https://www.ebi.ac.uk/interpro/">h</a><a href="https://www.ebi.ac.uk/interpro/">ttps://www.ebi.ac.uk/interpro/</a></p>
<p>You can find other comparative info at&nbsp;<a href="https://academic.oup.com/view-large/118391389">https://academic.oup.com/view-large/118391389</a></p><p>Address of the bookmark: <a href="http://ekhidna2.biocenter.helsinki.fi/sanspanz/" rel="nofollow">http://ekhidna2.biocenter.helsinki.fi/sanspanz/</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>

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