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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/44663?offset=210</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34504/minion-gc-an-r-script-to-do-some-qc-on-minion-data</guid>
	<pubDate>Sun, 03 Dec 2017 15:19:18 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34504/minion-gc-an-r-script-to-do-some-qc-on-minion-data</link>
	<title><![CDATA[MinION_GC: An R script to do some QC on MinION data]]></title>
	<description><![CDATA[<p><span>Other tools focus on getting data out of the fastq or fast5 files, which is slow and computationally intensive. The benefit of this approach is that it works on a single, small, .txt summary file. So it's a lot quicker than most other things out there: it takes about a minute to analyse a 4GB flowcell on my laptop.</span></p>
<p>https://github.com/roblanf/minion_qc</p><p>Address of the bookmark: <a href="https://github.com/roblanf/minion_qc" rel="nofollow">https://github.com/roblanf/minion_qc</a></p>]]></description>
	<dc:creator>Radha Agarkar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/36585/custom-r-charts-coming-to-excel</guid>
	<pubDate>Sat, 12 May 2018 07:30:28 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/36585/custom-r-charts-coming-to-excel</link>
	<title><![CDATA[Custom R charts coming to Excel !]]></title>
	<description><![CDATA[<p>This week at the BUILD conference, Microsoft&nbsp;<a href="https://dev.office.com/blogs/azure-machine-learning-javascript-custom-functions-and-power-bi-custom-visuals-further-expand-developers-capabilities-with-excel" target="_blank">announced</a>&nbsp;that Power BI custom visuals will soon be available as charts with Excel. You'll be able to choose a range of data within an Excel workbook, and pass those data to one of the built-in Power BI custom visuals, or one you've&nbsp;<a href="https://github.com/Microsoft/PowerBI-Visuals/" target="_blank">created yourself using the API</a>.</p><p><a href="http://a0.typepad.com/6a0105360ba1c6970c0224e038fa08200d-pi" target="_blank"><img src="https://www.r-bloggers.com/wp-content/plugins/lazy-load/images/1x1.trans.gif" alt="Excel custom visuals" title="Excel custom visuals" style="border: 0px; border: 0px;"></a></p><p>Since you can&nbsp;<a href="https://docs.microsoft.com/en-us/power-bi/desktop-r-visuals?WT.mc_id=Revolutions-blog-davidsmi" target="_blank">create Power BI custom visuals using R</a>, that means you'll be able to design a custom R-based chart, and make it available to people using Excel &mdash; even if they don't know how to use R themselves. There also many&nbsp;<a href="https://appsource.microsoft.com/en-us/marketplace/apps?product=power-bi-visuals&amp;page=1&amp;src=office" target="_blank">pre-defined custom visuals available</a>, including some familiar R charts like&nbsp;<a href="https://appsource.microsoft.com/en-us/product/power-bi-visuals/WA104380817?tab=Overview" target="_blank">decision trees</a>,&nbsp;<a href="https://appsource.microsoft.com/en-us/product/power-bi-visuals/WA104380905?tab=Overview" target="_blank">calendar heatmaps</a>, and&nbsp;<a href="https://appsource.microsoft.com/en-us/product/power-bi-visuals/WA104381492?tab=Overview" target="_blank">hexbin scatterplots</a>.</p><p>For more details on how you'll be able to use custom R visuals in Excel, check out the blog post linked below.</p><p>PowerBI Blog:&nbsp;<a href="https://powerbi.microsoft.com/en-us/blog/excel-announces-new-data-visualization-capabilities-with-power-bi-custom-visuals/" target="_blank">Excel announces new data visualization capabilities with Power BI custom visuals</a></p>]]></description>
	<dc:creator>Surabhi Chaudhary</dc:creator>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/file/view/37610/applied-statistics-for-bioinformatics-using-r</guid>
	<pubDate>Thu, 30 Aug 2018 03:45:39 -0500</pubDate>
	<link>https://bioinformaticsonline.com/file/view/37610/applied-statistics-for-bioinformatics-using-r</link>
	<title><![CDATA[Applied Statistics for Bioinformatics using R]]></title>
	<description><![CDATA[<p>The purpose of this book is to give an introduction into statistics in order to solve some problems of bioinformatics. Statistics provides procedures to explore and visualize data as well as to test biological hypotheses. The book intends to be introductory in explaining and programming elementary statistical concepts, thereby bridging the gap between high school levels and the specialized statistical literature</p>]]></description>
	<dc:creator>Neel</dc:creator>
	<enclosure url="https://bioinformaticsonline.com/file/download/37610" length="1368378" type="application/pdf" />
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39917/chromomap-an-r-package-for-interactive-visualization-and-annotation-of-chromosomes</guid>
	<pubDate>Sat, 07 Sep 2019 10:45:31 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39917/chromomap-an-r-package-for-interactive-visualization-and-annotation-of-chromosomes</link>
	<title><![CDATA[chromoMap-An R package for Interactive Visualization and Annotation of Chromosomes]]></title>
	<description><![CDATA[<p><code>chromoMap</code>&nbsp;provides interactive, configurable and elegant graphics visualization of chromosomes or chromosomal regions allowing users to map chromosome elements (like genes,SNPs etc.) on the chromosome plot.Each chromosome is composed of loci(representing a specific range determined based on chromosome length) that, on hover, shows details about the annotations in that locus range. The plots can be saved as HTML documents that can be shared easily. In addition, you can include them in R Markdown or in R Shiny applications.</p>
<p>Some of the prominent features of the package are:</p>
<ul>
<li>visualizing polyploidy simultaneously on the same plot.</li>
<li>annotating groups of elements as distinct colors.</li>
<li>creating chromosome heatmaps.</li>
<li>adjusting chromosome range or visualizing chromosome regions such as genes</li>
<li>adding labels to the plot</li>
<li>adding hyperlinks to each element</li>
</ul><p>Address of the bookmark: <a href="https://cran.r-project.org/web/packages/chromoMap/vignettes/chromoMap.html" rel="nofollow">https://cran.r-project.org/web/packages/chromoMap/vignettes/chromoMap.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/40085/github-replacement</guid>
	<pubDate>Thu, 26 Sep 2019 03:42:39 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/40085/github-replacement</link>
	<title><![CDATA[Github replacement !]]></title>
	<description><![CDATA[<p><span>For a number of reasons researchers have been trying out&nbsp;</span><a href="https://www.noamross.net/2019/09/24/drake-docker-and-gitlab-ci/gitlab.com" target="_blank">GitLab</a><span>&nbsp;as a replacement&nbsp;</span><span>for for both GitHub and various continuous integration systems, and have&nbsp;</span><span>been exploring configurations useful for model-fitting pipelines. Researchers turned&nbsp;</span><span>one of these into an&nbsp;</span><a href="https://gitlab.com/ecohealthalliance/drake-gitlab-docker-example" target="_blank">example repository</a><span>&nbsp;that shows how to use GitLab together&nbsp;</span><span>with the&nbsp;</span><a href="https://www.rocker-project.org/" target="_blank">Rocker</a><span>&nbsp;Docker images and the&nbsp;</span><a href="https://docs.ropensci.org/drake/" target="_blank"><strong>drake</strong></a><span>&nbsp;build system to reproducibly run a project pipeline, using the cacheing functionality across all three tools to&nbsp;</span><span>make things reasonably speedy and enable both local and remote builds. </span></p><p><span>Check it out&nbsp;</span><span>at&nbsp;</span><a href="https://gitlab.com/ecohealthalliance/drake-gitlab-docker-example" target="_blank">https://gitlab.com/ecohealthalliance/drake-gitlab-docker-example</a><span>.</span></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/40770/scientist-bioinformatics-positions</guid>
  <pubDate>Thu, 30 Jan 2020 06:53:40 -0600</pubDate>
  <link></link>
  <title><![CDATA[Scientist Bioinformatics Positions]]></title>
  <description><![CDATA[
<p>Bioinformatics-Multi_Omics_Integration</p>

<p>https://www.researchgate.net/job/939073_Senior_Scientist_Bioinformatics-Multi_Omics_Integration</p>

<p> <br />Senior_Scientist_Bioinformatics-Transcriptomics_Analysis     </p>

<p>https://www.researchgate.net/job/939075_Senior_Scientist_Bioinformatics-Transcriptomics_Analysis-Belgium_France_Switzerland_The_Netherlands</p>

<p>Senior Scientist Bioinformatics - Network Analytics</p>

<p>https://www.researchgate.net/job/939070_Senior_Scientist_Bioinformatics-Network_Analytics_Belgium_France_Switzerland_the_Netherlands</p>

<p>Team Leader Bioinformatics Data Sciences - Mechelen, Belgium</p>

<p>https://www.researchgate.net/job/938787_Team_Leader_Bioinformatics_Data_Sciences-Mechelen_Belgium</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/41496/new-machine-learning-packages-in-r</guid>
	<pubDate>Fri, 27 Mar 2020 12:11:21 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/41496/new-machine-learning-packages-in-r</link>
	<title><![CDATA[New Machine Learning Packages in R]]></title>
	<description><![CDATA[<h3 id="machine-learning">Machine Learning</h3><p><a href="https://cran.r-project.org/package=autokeras">autokeras</a>&nbsp;v1.0.1: Implements an interface to&nbsp;<a href="https://autokeras.com/">AutoKeras</a>, an open source software library for automated machine learning. See&nbsp;<a href="https://cran.r-project.org/web/packages/autokeras/readme/README.html">README</a>&nbsp;for an example.</p><p><a href="https://cran.r-project.org/package=MTPS">MTPS</a>&nbsp;v0.1.9: Implements functions to predict simultaneous multiple outcomes based on revised stacking algorithms as described in&nbsp;<a href="denied:doi:10.1093/bioinformatics/btz531">Xing et al. (2019)</a>. See the&nbsp;<a href="https://cran.r-project.org/web/packages/MTPS/vignettes/Guide.html">vignette</a>&nbsp;to get started.</p><p><a href="https://cran.r-project.org/package=quanteda.textmodels">quanteda.textmodels</a>&nbsp;v0.9.1: Implements methods for scaling models and classifiers based on sparse matrix objects representing textual data. It includes implementations of the&nbsp;<a href="denied:doi:10.1017/S0003055403000698">Laver et al. (2003)</a>&nbsp;wordscores model, the&nbsp;<a href="denied:arxiv:1710.08963">Perry &amp; Benoit&rsquo;s (2017)</a>&nbsp;class affinity scaling model, and the&nbsp;<a href="denied:doi:10.1111/j.1540-5907.2008.00338.x">Slapin &amp; Proksch (2008)</a>&nbsp;wordfish model. See the&nbsp;<a href="https://cran.r-project.org/web/packages/quanteda.textmodels/vignettes/textmodel_performance.html">vignette</a>&nbsp;to get started.</p><p><a href="https://cran.r-project.org/package=SeqDetect">SeqDetect</a>&nbsp;v1.0.7: Implements the automaton model found in&nbsp;<a href="https://ieeexplore.ieee.org/document/8910574">Krleža, Vrdoljak &amp; Brčić (2019)</a>&nbsp;to detect and process sequences. See the&nbsp;<a href="https://cran.r-project.org/web/packages/SeqDetect/vignettes/SequentialDetector.pdf">vignette</a>&nbsp;for examples and theory.</p><p><a href="https://cran.r-project.org/package=studyStrap">studyStrap</a>&nbsp;v1.0.0: Implements multi-Study Learning algorithms such as Merging, Study-Specific Ensembling (Trained-on-Observed-Studies Ensemble), the Study Strap, and the Covariate-Matched Study Strap. and offers over 20 similarity measures. See&nbsp;<a href="denied:doi:10.1101/856385">Kishida, et al. (2019)</a>&nbsp;for background and the&nbsp;<a href="https://cran.r-project.org/web/packages/studyStrap/vignettes/vignette.html">vignette</a>&nbsp;for how to use the package.</p>]]></description>
	<dc:creator>Rahul Nayak</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>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/26499/katju-lab</guid>
  <pubDate>Fri, 26 Feb 2016 03:25:32 -0600</pubDate>
  <link></link>
  <title><![CDATA[Katju Lab]]></title>
  <description><![CDATA[
<p>TheLab seek to understand the genetic factors contributing to genomic variation and phenotypic diversity.  To this end, we employ molecular and bioinformatic tools to study evolutionary processes at the level of populations, both experimental and natural, and genomes.  Our research interests encompass a wide range of topics, including the evolution of organellar and nuclear genomes, gene duplication and the origin of novel function, and the fitness and phenotypic consequences of mutation in evolution. For details regards ongoing projects, please see the Research page.</p>

<p>http://katjulab.com/research.html</p>
]]></description>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38310/sisrs-site-identification-from-short-read-sequences</guid>
	<pubDate>Wed, 28 Nov 2018 08:56:03 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38310/sisrs-site-identification-from-short-read-sequences</link>
	<title><![CDATA[SISRS: Site Identification from Short Read Sequences]]></title>
	<description><![CDATA[<p>Next-gen sequence data such as Illumina HiSeq reads. Data must be sorted into folders by taxon (e.g. species or genus). Paired reads in fastq format must be specified by _R1 and _R2 in the (otherwise identical) filenames. Paired and unpaired reads must have a fastq file extension.</p><p>Address of the bookmark: <a href="https://github.com/rachelss/SISRS" rel="nofollow">https://github.com/rachelss/SISRS</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
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