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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/39917?offset=60</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38067/metaplotr-a-perlr-pipeline-for-plotting-metagenes-of-nucleotide-modifications-and-other-transcriptomic-sites</guid>
	<pubDate>Mon, 05 Nov 2018 08:12:45 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38067/metaplotr-a-perlr-pipeline-for-plotting-metagenes-of-nucleotide-modifications-and-other-transcriptomic-sites</link>
	<title><![CDATA[MetaPlotR: a Perl/R pipeline for plotting metagenes of nucleotide modifications and other transcriptomic sites]]></title>
	<description><![CDATA[<p><span>An increasing number of studies are mapping protein binding and nucleotide modifications sites throughout the transcriptome. Often, these sites cluster in certain regions of the transcript, giving clues to their function. Hence, it is informative to summarize where in the transcript these sites occur. A metagene is a simple and effective tool for visualizing the distribution of sites along a simplified transcript model. In this work, we introduce MetaPlotR, a Perl/R pipeline for creating metagene plots.</span></p><p>Address of the bookmark: <a href="https://github.com/olarerin/metaPlotR" rel="nofollow">https://github.com/olarerin/metaPlotR</a></p>]]></description>
	<dc:creator>Neel</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/39884/retrieving-taxonomic-information-with-r</guid>
	<pubDate>Thu, 29 Aug 2019 01:38:39 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39884/retrieving-taxonomic-information-with-r</link>
	<title><![CDATA[Retrieving Taxonomic Information with R]]></title>
	<description><![CDATA[<p>This vignette will introduce users to the retrieval of taxonomic information with&nbsp;<code>myTAI</code>. The&nbsp;<code>taxonomy()</code>&nbsp;function implemented in&nbsp;<code>myTAI</code>&nbsp;relies on the powerful package&nbsp;<a href="https://github.com/ropensci/taxize">taxize</a>. Nevertheless, taxonomic information retrieval has been customized for the&nbsp;<code>myTAI</code>&nbsp;standard and for organism specific information retrieval.</p>
<p>Specifically, the&nbsp;<code>taxonomy()</code>&nbsp;function implemented in&nbsp;<code>myTAI</code>&nbsp;can be used to classify genomes according to phylogenetic classification into Phylostrata (Phylostratigraphy) or to retrieve species specific taxonomic information when performing Divergence Stratigraphy (see&nbsp;<a href="https://cran.r-project.org/web/packages/myTAI/vignettes/Introduction.html">Introduction</a>&nbsp;for details).</p><p>Address of the bookmark: <a href="https://cran.r-project.org/web/packages/myTAI/vignettes/Taxonomy.html" rel="nofollow">https://cran.r-project.org/web/packages/myTAI/vignettes/Taxonomy.html</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40463/%E2%80%98dockr%E2%80%99-the-r-container</guid>
	<pubDate>Mon, 23 Dec 2019 09:56:49 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40463/%E2%80%98dockr%E2%80%99-the-r-container</link>
	<title><![CDATA[‘dockr’: the R container]]></title>
	<description><![CDATA[<p><code>dockr</code> 0.8.6 is now available on CRAN. <code>dockr</code> is a minimal toolkit to build a lightweight Docker container image for your R package, in which the package itself is available. The Docker image seeks to mirror your R session as close as possible with respect to R specific dependencies. Both dependencies on CRAN R packages as well as local non-CRAN R packages will be included in the Docker container image.</p>
<p>If you want to know, how Docker works, and why you should consider using Docker, please take a look at the <a href="https://www.docker.com/why-docker" target="_blank">Docker website</a>.</p><p>Address of the bookmark: <a href="https://www.docker.com/why-docker" rel="nofollow">https://www.docker.com/why-docker</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/42299/platypus-%E2%80%93-r-package-for-object-detection-and-image-segmentation</guid>
	<pubDate>Mon, 09 Nov 2020 02:56:25 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42299/platypus-%E2%80%93-r-package-for-object-detection-and-image-segmentation</link>
	<title><![CDATA[Platypus – R package for object detection and image segmentation.]]></title>
	<description><![CDATA[<p><a href="https://github.com/maju116/platypus" target="_blank">platypus</a>&nbsp;is an R package for object detection and semantic segmentation. Currently using&nbsp;</p>
<div>platypus&nbsp;you can perform:</div>
<ul>
<li>multi-class semantic segmentation using&nbsp;U-Net&nbsp;architecture</li>
<li>multi-class object detection using&nbsp;YOLOv3&nbsp;architecture</li>
</ul>
<p>You can install the latest version of&nbsp;platypus&nbsp;with&nbsp;remotes&nbsp;package:</p>
<div>
<div>
<div>
<div>remotes::install_github("maju116/platypus")</div>
</div>
</div>
</div>
<p>Note that in order to install&nbsp;platypus&nbsp;you need to install&nbsp;keras&nbsp;and&nbsp;tensorflow&nbsp;packages and&nbsp;Tensorflow&nbsp;version&nbsp;&gt;= 2.0.0&nbsp;(&nbsp;Tensorflow 1.x&nbsp;will not be supported!)</p><p>Address of the bookmark: <a href="https://github.com/maju116/platypus" rel="nofollow">https://github.com/maju116/platypus</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44541/powerful-books-for-learning-data-analysis-with-r</guid>
	<pubDate>Tue, 28 May 2024 07:42:56 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44541/powerful-books-for-learning-data-analysis-with-r</link>
	<title><![CDATA[Powerful books for learning data analysis with R]]></title>
	<description><![CDATA[<p><span>R is powerful tool for data analysis, visualization, and machine learning. And it costs $0 to use! Here are six FREE books you can use to learn R today:</span></p>
<p><span>https://csgillespie.github.io/efficientR/</span></p>
<p><span>https://r-graphics.org/</span></p>
<p><span>https://rstudio-education.github.io/hopr/</span></p>
<p><span>https://r-pkgs.org/</span></p>
<p><span>https://r4ds.had.co.nz/</span></p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="https://r-graphics.org/" rel="nofollow">https://r-graphics.org/</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44648/modern-statistics-with-r</guid>
	<pubDate>Thu, 22 Aug 2024 04:44:06 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44648/modern-statistics-with-r</link>
	<title><![CDATA[Modern Statistics with R]]></title>
	<description><![CDATA[<p>This is the online version of the second edition of&nbsp;<em>Modern Statistics with R</em>. It is free to use, and always will be.&nbsp;<a href="https://www.routledge.com/Modern-Statistics-with-R-From-Wrangling-and-Exploring-Data-to-Inference-and-Predictive-Modelling/Thulin/p/book/9781032512440">Printed copies</a>&nbsp;are available from CRC Press.</p>
<p><span>Live&nbsp;<a href="https://statistikakademin.se/in-english-r/">online courses on statistics with R</a></span>&nbsp;based on this book, led by the author, are offered regularly; see&nbsp;<a href="https://statistikakademin.se/in-english-r/">this page</a>&nbsp;for more information and dates.</p>
<p>The past decades have transformed the world of statistical data analysis, with new methods, new types of data, and new computational tools. The aim of&nbsp;<em>Modern Statistics with R</em>&nbsp;is to introduce you to key parts of the modern statistical toolkit. It teaches you:</p>
<ul>
<li><span>Data wrangling</span>&nbsp;- importing, formatting, reshaping, merging, and filtering data in R.</li>
<li><span>Exploratory data analysis</span>&nbsp;- using visualisations and multivariate techniques to explore datasets.</li>
<li><span>Statistical inference</span>&nbsp;- modern methods for testing hypotheses and computing confidence intervals.</li>
<li><span>Predictive modelling</span>&nbsp;- regression models and machine learning methods for prediction, classification, and forecasting.</li>
<li><span>Simulation</span>&nbsp;- using simulation techniques for sample size computations and evaluations of statistical methods.</li>
<li><span>Ethics in statistics</span>&nbsp;- ethical issues and good statistical practice.</li>
<li><span>R programming</span>&nbsp;- writing code that is fast, readable, and (hopefully!) free from bugs.</li>
</ul>
<p>The book includes plenty of examples and more than 200 exercises with worked solutions.&nbsp;<a href="http://www.modernstatisticswithr.com/data.zip">The datasets used for the examples and the exercises can be downloaded here.</a></p><p>Address of the bookmark: <a href="https://www.modernstatisticswithr.com/" rel="nofollow">https://www.modernstatisticswithr.com/</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/12944/orione-%E2%80%93-a-web-based-framework-for-ngs-analysis-in-microbiology</guid>
	<pubDate>Wed, 23 Jul 2014 06:43:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/12944/orione-%E2%80%93-a-web-based-framework-for-ngs-analysis-in-microbiology</link>
	<title><![CDATA[Orione – a web-based framework for NGS analysis in microbiology]]></title>
	<description><![CDATA[<p>End-to-end NGS microbiology data analysis requires a diversity of tools covering bacterial resequencing, de novo assembly, scaffolding, bacterial RNA-Seq, gene annotation and metagenomics. However, the construction of computational pipelines that use different software packages is difficult due to a lack of interoperability, reproducibility, and transparency. To overcome these limitations researchers at <a href="http://www.crs4.it/" target="_blank">CRS4</a>, Italy have developed Orione, a Galaxy-based framework consisting of publicly available research software and specifically designed pipelines to build complex, reproducible workflows for NGS microbiology data analysis. Enabling microbiology researchers to conduct their own custom analysis and data manipulation without software installation or programming, Orione provides new opportunities for data-intensive computational analyses in microbiology and metagenomics.</p>
<p>Reference</p>
<p>Cuccuru G1, Orsini M, Pinna A, Sbardellati A, Soranzo N, Travaglione A, Uva P, Zanetti G, Fotia G. (2014)<strong> Orione, a web-based framework for NGS analysis in microbiology.</strong> <em>Bioinformatics</em> [Epub ahead of print]. [<a href="http://bioinformatics.oxfordjournals.org/content/early/2014/03/10/bioinformatics.btu135.long" target="_blank">article</a>]</p><p>Address of the bookmark: <a href="http://orione.crs4.it/" rel="nofollow">http://orione.crs4.it/</a></p>]]></description>
	<dc:creator>Martin Jones</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26927/phylographer-graph-visualization-tool</guid>
	<pubDate>Wed, 06 Apr 2016 19:06:48 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26927/phylographer-graph-visualization-tool</link>
	<title><![CDATA[PhyloGrapher - Graph Visualization Tool]]></title>
	<description><![CDATA[<p><strong>PhyloGrapher</strong><span>&nbsp;is a program designed to visualize and study evolutionary relationships within families of homologous genes or proteins (elements).</span><strong>PhyloGrapher</strong><span>&nbsp;is a drawing tool that generates custom graphs for a given set of elements. In general, it is possible to use&nbsp;</span><strong>PhyloGrapher</strong><span>&nbsp;to visualize any type of relations between elements.&nbsp;</span></p>
<p><span>More at&nbsp;http://www.atgc.org/PhyloGrapher/PhyloGrapher_Welcome.html</span></p><p>Address of the bookmark: <a href="http://www.atgc.org/PhyloGrapher/PhyloGrapher_Welcome.html" rel="nofollow">http://www.atgc.org/PhyloGrapher/PhyloGrapher_Welcome.html</a></p>]]></description>
	<dc:creator>Jitendra Prajapati</dc:creator>
</item>

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