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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/21241?offset=1130</link>
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	<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>
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	<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/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/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>
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	<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/opportunity/view/41043/postdoctoral-scientist-genome-analytics-genome-bioinformatics-mf</guid>
  <pubDate>Sun, 16 Feb 2020 02:57:40 -0600</pubDate>
  <link></link>
  <title><![CDATA[Postdoctoral scientist genome analytics/ genome bioinformatics (m/f/*)]]></title>
  <description><![CDATA[
<p>https://www.uksh.de/jobs/Stellenangebote-nr-20190570-p-8.html<br />Your profile:<br />Degree in bioinformatics, biostatistics, or equivalent<br />Experience in the processing and analysis of large-scale genomics data using compute clusters / high-performance computing<br />Strong competence in working in Unix/Linux environments (shell)<br />Strong programming skills (in particular: Python, R, Perl)<br />Experience with using git and snakemake<br />Fluent English language skills, both spoken and written<br />Strong communication skills and motivation to work in a young, interdisciplinary, dynamic team</p>

<p>Additional Information:</p>

<p>If you have any questions about scientific aspects of this position, please contact Prof. Lars Bertram, head of LIGA (lars.bertram@uni-luebeck.de).</p>

<p>Please contact Ms. Anna Wolbert for further questions about administrative details (recruiting@uksh.de).</p>

<p>Weitere Informationen erhalten Sie auch unter www.uksh.de/karriere.</p>

<p>Wir freuen uns auf Ihre Bewerbung bis zum 15.03.2020 unter Angabe unserer Ausschreibungsnummer 20190570.119.CL.</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43254/quasr-quantification-and-annotation-of-short-reads-in-r</guid>
	<pubDate>Fri, 13 Aug 2021 07:44:05 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43254/quasr-quantification-and-annotation-of-short-reads-in-r</link>
	<title><![CDATA[QuasR: Quantification and annotation of short reads in R]]></title>
	<description><![CDATA[<p>The <em><a href="https://bioconductor.org/packages/3.14/QuasR">QuasR</a></em> package (short for <em>Qu</em>antify and <em>a</em>nnotate <em>s</em>hort reads in <em>R</em>) integrates the functionality of several <strong>R</strong> packages (such as <em><a href="https://bioconductor.org/packages/3.14/IRanges">IRanges</a></em> <span>(Lawrence et al. 2013)</span> and <em><a href="https://bioconductor.org/packages/3.14/Rsamtools">Rsamtools</a></em>) and external software (e.g.&nbsp;<code>bowtie</code>, through the <em><a href="https://bioconductor.org/packages/3.14/Rbowtie">Rbowtie</a></em> package, and <code>HISAT2</code>, through the <em><a href="https://bioconductor.org/packages/3.14/Rhisat2">Rhisat2</a></em> package). The package aims to cover the whole analysis workflow of typical high throughput sequencing experiments, starting from the raw sequence reads, over pre-processing and alignment, up to quantification. A single <strong>R</strong> script can contain all steps of a complete analysis, making it simple to document, reproduce or share the workflow containing all relevant details.</p><p>Address of the bookmark: <a href="https://www.bioconductor.org/packages/devel/bioc/vignettes/QuasR/inst/doc/QuasR.html" rel="nofollow">https://www.bioconductor.org/packages/devel/bioc/vignettes/QuasR/inst/doc/QuasR.html</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44362/biostats-book</guid>
	<pubDate>Mon, 14 Aug 2023 03:11:39 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44362/biostats-book</link>
	<title><![CDATA[Biostats book !]]></title>
	<description><![CDATA[<p>https://practical-stats-med-r.netlify.app/</p><p>Address of the bookmark: <a href="https://practical-stats-med-r.netlify.app/" rel="nofollow">https://practical-stats-med-r.netlify.app/</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/17504/postdoc-scientist-bioinformatics-at-ccmb</guid>
  <pubDate>Fri, 26 Sep 2014 19:58:41 -0500</pubDate>
  <link></link>
  <title><![CDATA[PostDoc Scientist Bioinformatics at CCMB]]></title>
  <description><![CDATA[
<p>1. Project Assistant/Junior Research Fellow/ Project Fellow [PA_JRF_PF]</p>

<p>a) M.Sc/or equivalent in biological sciences/related areas [Position Code: PA_JRF_PF_a]<br />b) B.E/B.Tech/ M.Sc in biotechnology/bioinformatics/computer science/Chemistry/Physics or MCA [Position Code: PA_JRF_PF_b]<br />c) M.Sc/or equivalent in wildlife sciences/ecology/environmental sciences or MBBS/BVSc/MVSc. [Position Code: PA_JRF_PF_c]</p>

<p>(Candidates with result awaited are NOT eligible to apply)</p>

<p>Upper Age limit 28years</p>

<p>Rs.12000 / Rs.16000 (as sanctioned by the funding agency)</p>

<p>2. Post Doctoral Fellow/Research Associate in multiple research areas [PDF_RA]</p>

<p>Ph.D. (submitted/awarded) in any branch of biological Sciences. Candidates with Ph.D. in other sciences are also encouraged to apply.</p>

<p>Experience in molecular biology, biochemistry, structural biology, cell biology, infectious disease, conservation genetics, veterinary science, reproductive biology, and molecular diagnostics is desired but not mandatory.</p>

<p>[Position Code: PDF_RA]</p>

<p>UpperAge limit 35years</p>

<p>Rs. 22000- 26000 (as sanctioned by the funding agency)</p>

<p>3. Post Doctoral Scientist Fellow [PDSF]</p>

<p>Ph.D in any of the following areas: bioinformatics, next generation sequencing, high throughput data analysis, proteomics, bio-statistics, computer science, information technology, computer hardware and networking/clustering, parallel processing.<br />[Position Code: PDSF]</p>

<p>Upper Age limit 40 years</p>

<p>Rs. 40000 consolidated (as sanctioned by the funding agency)</p>

<p>Download Application: Last date for apply online: 09th Oct 2014</p>

<p>Advertisement: www.ccmb.res.in//index.php?view=notifications&amp;mid=0&amp;id=71&amp;nid=38</p>

<p>Apply online http://www.ccmb.res.in/positions/temp_notif/online_form.html</p>

<p>More at http://www.ccmb.res.in//index.php?view=notifications&amp;mid=0&amp;id=71&amp;nid=38</p>
]]></description>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/17652/arraygen-bioinformatics-genomics-group</guid>
  <pubDate>Sun, 28 Sep 2014 14:09:55 -0500</pubDate>
  <link></link>
  <title><![CDATA[ArrayGen Bioinformatics Genomics Group]]></title>
  <description><![CDATA[
<p>ArrayGen is a global bioinformatics company which is a one stop solution for microarray designing and genomics data analysis. Our novel Array Design Approach Strategy (ADAS) aims to condense the time lag between demands of scientific community and manufacture industry, thereby expediting research processes.</p>

<p>ArrayGen specializes in Genomics data analysis and research, as we believe in the level of precision, predictability, benchmark-ability, and data analysis capability of genomics data over other forms of biological data. ArrayGen constantly strives to develop new solutions, and plug the existing gaps in the technological advancement of the field.</p>

<p>More http://www.arraygen.com/</p>
]]></description>
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