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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/41464?offset=170</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44894/dna2bit-an-ultra-fast-and-accurate-genomic-distance-estimation-software</guid>
	<pubDate>Sun, 31 Aug 2025 06:24:58 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44894/dna2bit-an-ultra-fast-and-accurate-genomic-distance-estimation-software</link>
	<title><![CDATA[dna2bit: an ultra-fast and accurate genomic distance estimation software]]></title>
	<description><![CDATA[<p><span>dna2bit is a software tool developed in C++11, leveraging the capabilities of OpenMP for parallel computing and the popcount technique for efficient bit manipulation. It has been thoroughly tested using the g++ and clang compilers on both Linux and MacOS platforms.</span></p><p>Address of the bookmark: <a href="https://github.com/lijuzeng/dna2bit" rel="nofollow">https://github.com/lijuzeng/dna2bit</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/42137/plant-computational-genomics-lab-%E2%80%93-jill-wegrzyn</guid>
  <pubDate>Thu, 20 Aug 2020 19:49:12 -0500</pubDate>
  <link></link>
  <title><![CDATA[PLANT COMPUTATIONAL GENOMICS LAB – JILL WEGRZYN]]></title>
  <description><![CDATA[
<p>Our research focuses on the computational analysis of genomic and transcriptomic sequences from non-model plant species. We do this by developing approaches to examine gene finding, gene expression, transcriptome assembly, and conserved element identification, through machine learning and computational statistics. We use these novel methods to address questions related to genome biology and population genomics.</p>

<p>We also develop web-based applications that integrate data across domains to facilitate the forest geneticist or ecologist’s ability to analyze, share, and visualize their data. Such integration requires the implementation of semantic technologies and ontologies to connect genotype, phenotype, and environmental data.</p>

<p>http://plantcompgenomics.com/</p>
]]></description>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43062/jcvi-utility-libraries</guid>
	<pubDate>Sat, 08 May 2021 22:04:02 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43062/jcvi-utility-libraries</link>
	<title><![CDATA[JCVI utility libraries]]></title>
	<description><![CDATA[<p><span>Collection of Python libraries to parse bioinformatics files, or perform computation related to assembly, annotation, and comparative genomics.</span></p><p>Address of the bookmark: <a href="https://github.com/tanghaibao/jcvi" rel="nofollow">https://github.com/tanghaibao/jcvi</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/39378/your-turn-this-year</guid>
	<pubDate>Fri, 17 May 2019 03:16:45 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/39378/your-turn-this-year</link>
	<title><![CDATA[YOUR TURN THIS YEAR?]]></title>
	<description><![CDATA[<p>Apply for the&nbsp;Science&nbsp;&amp; SciLifeLab Prize for Young Scientists &mdash; an annual prize awarded to early-career scientists. The prize is presented in four categories: Cell and Molecular Biology, Ecology and Environment, Molecular Medicine, and Genomics, Proteomics and Systems Biology.</p><p>The winners will have their essays published by&nbsp;<span>Science</span>, win up to USD 30,000 and be invited to Sweden to attend the award ceremony. Get ready for a life-changing moment in your scientific career.&nbsp;</p><p><strong>Application deadline: July 15th, 2019</strong></p><p>Eligible entrants must have been awarded their doctoral degree in 2017 or 2018. The winners from each category will compete for the Grand Prize.&nbsp;</p><p>Read more:&nbsp;<a href="http://click.aaas.sciencepubs.org/?qs=1b029f79ee3c0443ac3eee1a3c87fb7f9bd85296743ed87917cf3bf79e90912df9214a9cac85a3213db78eed066d040a5a7df95e10c19576" target="_blank">scienceprize.scilifelab.se</a><br />Questions/Inquiries:&nbsp;<a href="mailto:SciLifeLabPrize@aaas.org" target="_blank">SciLifeLabPrize@aaas.org</a></p><div><div>
<p><span>&ldquo;It has absolutely brought a lot of exposure to me and to my research. I also think it went a long way in helping me to get my current position.&rdquo;</span></p>
<p>Allison Cleary, 2015 Grand Prize Winner</p></div><div>
<p><span>&ldquo;My advice for those thinking about applying for the&nbsp;Science&nbsp;and SciLifeLab Prize is to just go for it! It wasn&rsquo;t the easiest thing trying to frame my work in a broader context, but in the end, I think it was a very healthy experience. I think everyone could benefit from trying to do that with their work.&ldquo;</span></p>
<p>Tim Wang, 2018 Category Winner</p></div><div>
<p><span>&ldquo;I think it&rsquo;s really remarkable to have a prize for young researchers because most of the prizes reward people that are established in their fields. And for us, it means a great deal, because we are only launching our careers now, and such a prize is probably going to a be an extreme help.&rdquo;</span></p>
<p>Mijo Simunovic, 2017 Category Winner</p></div></div><div><a href="https://mail.google.com/mail/u/0/#m_-6351470468694581133_">APPLY NOW</a></div><p>&nbsp;</p>]]></description>
	<dc:creator>BioJoker</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38233/kubeflow-an-open-community-driven-project-to-make-it-easy-to-deploy-and-manage-an-ml-stack-on-kubernetes</guid>
	<pubDate>Fri, 16 Nov 2018 15:05:14 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38233/kubeflow-an-open-community-driven-project-to-make-it-easy-to-deploy-and-manage-an-ml-stack-on-kubernetes</link>
	<title><![CDATA[Kubeflow: an open, community driven project to make it easy to deploy and manage an ML stack on Kubernetes]]></title>
	<description><![CDATA[<p><span>The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures. Anywhere you are running Kubernetes, you should be able to run Kubeflow.</span></p><p>Address of the bookmark: <a href="https://www.kubeflow.org/" rel="nofollow">https://www.kubeflow.org/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34420/rita-rapid-identification-of-high-confidence-taxonomic-assignments-for-metagenomic-data</guid>
	<pubDate>Mon, 27 Nov 2017 08:25:33 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34420/rita-rapid-identification-of-high-confidence-taxonomic-assignments-for-metagenomic-data</link>
	<title><![CDATA[RITA: Rapid identification of high-confidence taxonomic assignments for metagenomic data]]></title>
	<description><![CDATA[<p>RITA is a standalone software package and Web server for taxonomic assignment of metagenomic sequence reads. By combining homology predictions from BLAST or UBLAST with compositional classifications from a Naive Bayes classifier, RITA is able to achieve very high accuracy on short reads. Unlike other hybrid approaches which combine these predictions for all sequences to be classified, RITA uses a pipeline to first identify cases where both types of classifier are in agreement, which constitute the highest-confidence set. Sequences not classified in this manner are subjected to a series of downstream classification steps.</p>
<p>This work has been accepted for publication:</p>
<p>MacDonald NJ, Parks DH, and Beiko RG. Rapid identification of taxonomic assignments. Accepted to&nbsp;<em>Nucleic Acids Research</em>&nbsp;April 4, 2012.</p>
<p>If you have any questions or bug reports, please let us know at &lt;beiko@cs.dal.ca&gt;.</p><p>Address of the bookmark: <a href="http://kiwi.cs.dal.ca/Software/RITA" rel="nofollow">http://kiwi.cs.dal.ca/Software/RITA</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35272/biocircosjs-is-an-open-source-interactive-javascript-library-to-interactive-display-biological-data-on-the-web</guid>
	<pubDate>Fri, 19 Jan 2018 15:03:51 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35272/biocircosjs-is-an-open-source-interactive-javascript-library-to-interactive-display-biological-data-on-the-web</link>
	<title><![CDATA[BioCircos.js is an open source interactive Javascript library to interactive display biological data on the web]]></title>
	<description><![CDATA[<p><a href="http://bioinfo.ibp.ac.cn/biocircos/index.php">BioCircos.js</a>&nbsp;is an open source interactive&nbsp;<code>Javascript</code>&nbsp;library which provides an easy way to interactive display biological data on the web. It implements a raster-based&nbsp;<code>SVG</code>&nbsp;visualization using the open source Javascript framework jquery.js. BioCircos.js is multiplatform and works in all major internet browsers (<strong>Internet Explorer</strong>,&nbsp;<strong>Mozilla Firefox</strong>,&nbsp;<strong>Google Chrome</strong>,&nbsp;<strong>Safari</strong>,&nbsp;<strong>Opera</strong>). Its speed is determined by the client&rsquo;s hardware and internet browser. For smoothest user experience, we recommend&nbsp;<strong>Google Chrome</strong>.</p>
<p>BioCircos.js provides&nbsp;<strong>SNP</strong>,&nbsp;<strong>CNV</strong>,&nbsp;<strong>HEATMAP</strong>,&nbsp;<strong>LINK</strong>,&nbsp;<strong>LINE</strong>,&nbsp;<strong>SCATTER</strong>,&nbsp;<strong>ARC</strong>,&nbsp;<strong>TEXT</strong>, and&nbsp;<strong>HISTGRAM</strong>modules to display genome-wide genetic variations (SNPs, CNVs and chromosome rearrangement), gene expression and biomolecule interactions. BioCircos.js also provides&nbsp;<strong>BACKGROUND</strong>&nbsp;module to display background and axis circles. Tooltips showing detailed information of SVG elements are also provided.</p>
<p><a href="http://bioinfo.ibp.ac.cn/biocircos/document/demo/pages/paper01.html">Demo</a></p><p>Address of the bookmark: <a href="http://bioinfo.ibp.ac.cn/biocircos/document/index.html" rel="nofollow">http://bioinfo.ibp.ac.cn/biocircos/document/index.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37205/afterqc-automatic-filtering-trimming-error-removing-and-quality-control-for-fastq-data</guid>
	<pubDate>Fri, 29 Jun 2018 03:26:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37205/afterqc-automatic-filtering-trimming-error-removing-and-quality-control-for-fastq-data</link>
	<title><![CDATA[AfterQC: Automatic Filtering, Trimming, Error Removing and Quality Control for fastq data]]></title>
	<description><![CDATA[Automatic Filtering, Trimming, Error Removing and Quality Control for fastq data
AfterQC can simply go through all fastq files in a folder and then output three folders: good, bad and QC folders, which contains good reads, bad reads and the QC results of each fastq file/pair.
Currently it supports processing data from HiSeq 2000/2500/3000/4000, Nextseq 500/550, MiniSeq...and other Illumina 1.8 or newer formats

The author has reimplemented this tool in C++ with multithreading support to make it much faster. The new tool is called fastp and can be found at: https://github.com/OpenGene/fastp . If you prefer a C++ based tool, please use fastp instead.

https://github.com/OpenGene/AfterQC<p>Address of the bookmark: <a href="https://github.com/OpenGene/AfterQC" rel="nofollow">https://github.com/OpenGene/AfterQC</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37672/seqmonka-tool-to-visualise-and-analyse-high-throughput-mapped-sequence-data</guid>
	<pubDate>Tue, 11 Sep 2018 04:39:38 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37672/seqmonka-tool-to-visualise-and-analyse-high-throughput-mapped-sequence-data</link>
	<title><![CDATA[SeqMonk:A tool to visualise and analyse high throughput mapped sequence data]]></title>
	<description><![CDATA[<p>SeqMonk is a program to enable the visualisation and analysis of mapped sequence data. It was written for use with mapped next generation sequence data but can in theory be used for any dataset which can be expressed as a series of genomic positions. It's main features are:</p>
<ul>
<li>Import of mapped data from mapped data (BAM/SAM/bowtie etc)</li>
<li>Creation of data groups for visualisation and analysis</li>
<li>Visualisation of mapped regions against an annotated genome.</li>
<li>Flexible quantitation of the mapped data to allow comparisons between data sets</li>
<li>Statistical analysis of data to find regions of interest</li>
<li>Creation of reports containing data and genome annotation</li>
</ul><p>Address of the bookmark: <a href="http://www.bioinformatics.babraham.ac.uk/projects/seqmonk/" rel="nofollow">http://www.bioinformatics.babraham.ac.uk/projects/seqmonk/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38535/nanopack-visualizing-and-processing-long-read-sequencing-data</guid>
	<pubDate>Tue, 25 Dec 2018 21:20:50 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38535/nanopack-visualizing-and-processing-long-read-sequencing-data</link>
	<title><![CDATA[NanoPack: visualizing and processing long-read sequencing data]]></title>
	<description><![CDATA[The NanoPack tools are written in Python3 and released under the GNU GPL3.0 License. The source code can be found at https://github.com/wdecoster/nanopack, together with links to separate scripts and their documentation. The scripts are compatible with Linux, Mac OS and the MS Windows 10 subsystem for Linux and are available as a graphical user interface, a web service at http://nanoplot.bioinf.be and command line tools.<p>Address of the bookmark: <a href="https://github.com/wdecoster/nanopack" rel="nofollow">https://github.com/wdecoster/nanopack</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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