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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/33909?offset=10</link>
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	<item>
	<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/bookmarks/view/35135/alitv%E2%80%94interactive-visualization-of-whole-genome-comparisons</guid>
	<pubDate>Wed, 10 Jan 2018 07:08:17 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35135/alitv%E2%80%94interactive-visualization-of-whole-genome-comparisons</link>
	<title><![CDATA[AliTV—interactive visualization of whole genome comparisons]]></title>
	<description><![CDATA[<p>AliTV, which provides interactive visualization of whole genome alignments. AliTV reads multiple whole genome alignments or automatically generates alignments from the provided data. Optional feature annotations and phylo- genetic information are supported. The user-friendly, web-browser based and highly customizable interface allows rapid exploration and manipulation of the visualized data as well as the export of publication-ready high-quality figures. AliTV is freely available at&nbsp;<a href="https://github.com/AliTVTeam/AliTV">https://github.com/AliTVTeam/AliTV</a></p>
<p>https://alitvteam.github.io/AliTV/</p><p>Address of the bookmark: <a href="https://github.com/AliTVTeam/AliTV" rel="nofollow">https://github.com/AliTVTeam/AliTV</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37259/epiviz-an-interactive-visualization-tool-for-functional-genomics-data</guid>
	<pubDate>Mon, 09 Jul 2018 05:27:39 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37259/epiviz-an-interactive-visualization-tool-for-functional-genomics-data</link>
	<title><![CDATA[Epiviz: an interactive visualization tool for functional genomics data.]]></title>
	<description><![CDATA[<p><span>Epiviz is an interactive visualization tool for functional genomics data. It supports genome navigation like other genome browsers, but allows multiple visualizations of data within genomic regions using scatterplots, heatmaps and other user-supplied visualizations. It also includes data from the&nbsp;</span><a href="http://barcode.luhs.org/" target="_blank">Gene Expression Barcode project</a><span>&nbsp;for transcriptome visualization. It has a flexible plugin framework so users can add</span><a href="http://d3js.org/" target="_blank">d3</a><span>&nbsp;visualizations. You can see a video tour&nbsp;</span><a href="http://youtu.be/099c4wUxozA" target="_blank">here</a><span>.</span></p>
<p><span>https://bioconductor.org/packages/release/bioc/html/epivizr.html</span></p>
<p><span>https://github.com/epiviz</span></p>
<p><span>https://github.com/epiviz/epiviz</span></p><p>Address of the bookmark: <a href="https://epiviz.github.io/" rel="nofollow">https://epiviz.github.io/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38561/hawkeye-an-interactive-visual-analytics-tool-for-genome-assemblies</guid>
	<pubDate>Tue, 01 Jan 2019 11:56:17 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38561/hawkeye-an-interactive-visual-analytics-tool-for-genome-assemblies</link>
	<title><![CDATA[Hawkeye: an interactive visual analytics tool for genome assemblies]]></title>
	<description><![CDATA[<p><span>Genome sequencing remains an inexact science, and genome sequences can contain significant errors if they are not carefully examined. Hawkeye is our new visual analytics tool for genome assemblies, designed to aid in identifying and correcting assembly errors. Users can analyze all levels of an assembly along with summary statistics and assembly metrics, and are guided by a ranking component towards likely mis-assemblies. Hawkeye is freely available and released as part of the open source AMOS project&nbsp;</span><span><a href="http://amos.sourceforge.net/hawkeye"><span>http://amos.sourceforge.net/hawkeye</span></a></span><span>.</span></p>
<p>https://genomebiology.biomedcentral.com/articles/10.1186/gb-2007-8-3-r34</p><p>Address of the bookmark: <a href="http://amos.sourceforge.net/wiki/index.php?title=Hawkeye" rel="nofollow">http://amos.sourceforge.net/wiki/index.php?title=Hawkeye</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40937/shinycircos-an-rshiny-application-for-interactive-creation-of-circos-plot</guid>
	<pubDate>Fri, 07 Feb 2020 03:26:58 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40937/shinycircos-an-rshiny-application-for-interactive-creation-of-circos-plot</link>
	<title><![CDATA[shinyCircos: an R/Shiny application for interactive creation of Circos plot]]></title>
	<description><![CDATA[<p><span>shinyCircos, a graphical user interface for interactive creation of Circos plot. shinyCircos can be easily installed either on computers for personal use or on local or public servers to provide online use to the community. Furthermore, various types of Circos plots could be easily generated and decorated with simple mouse-click.</span></p>
<p>Tutorial&nbsp;<a href="http://shinycircos.ncpgr.cn/shinyCircos_Help_Manual.pdf">http://shinycircos.ncpgr.cn/shinyCircos_Help_Manual.pdf</a></p>
<p>Github&nbsp;<a href="https://github.com/venyao/shinyCircos">https://github.com/venyao/shinyCircos</a></p><p>Address of the bookmark: <a href="http://150.109.59.144:3838/shinyCircos/" rel="nofollow">http://150.109.59.144:3838/shinyCircos/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42303/fqc-dashboard-integrates-fastqc-results-into-a-web-based-interactive-and-extensible-fastq-quality-control-tool</guid>
	<pubDate>Tue, 10 Nov 2020 01:30:22 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42303/fqc-dashboard-integrates-fastqc-results-into-a-web-based-interactive-and-extensible-fastq-quality-control-tool</link>
	<title><![CDATA[FQC Dashboard: Integrates FastQC results into a web-based, interactive, and extensible FASTQ quality control tool]]></title>
	<description><![CDATA[<p>FQC is software that facilitates quality control of FASTQ files by carrying out a QC protocol using FastQC, parsing results, and aggregating quality metrics into an interactive dashboard designed to richly summarize individual sequencing runs. The dashboard groups samples in dropdowns for navigation among the data sets, utilizes human-readable configuration files to manipulate the pages and tabs, and is extensible with CSV data.</p><p>Address of the bookmark: <a href="https://github.com/pnnl/fqc" rel="nofollow">https://github.com/pnnl/fqc</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/8987/the-dna-of-a-successful-bioinformatician-decoded</guid>
	<pubDate>Wed, 12 Mar 2014 13:41:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/8987/the-dna-of-a-successful-bioinformatician-decoded</link>
	<title><![CDATA[The DNA of a Successful Bioinformatician decoded !!!]]></title>
	<description><![CDATA[<p>Many blogs exist about successful bioinformatician, but this blog so far now is my personal view on characteristics of successful bioinformatician or computational biologist. &nbsp;Hmm &hellip; of course these views are subjective to my own personal experiences and therefore I don't claim that the view listed here is complete. As a human, I don&rsquo;t take them too serious. The success must not be the only target of your work. The target is to work on your own virtues; some of those virtues are the topic of this blog.</p><p><img src="http://bioinformaticsonline.com/mod/photo/genome_decode.png" alt="image" width="509" height="458" style="border: 0px; border: 0px;"><br /> <br /> <strong>1. Update new things continuously<br /></strong>As per my personal experience, it&rsquo;s not always easy to work as a bioinformatician! &nbsp;There are couple of reasons to say that; First computational part of biology make our life&rsquo;s a little harder compared to other professional categories. The fact - for instance - that the technology cycle in the bioinformatics world is very short, the actual knowledge becomes outdated in a few months or years. Therefore, we need to learn continuously - new things get important. Second, to stay on top of things we really need the strong will to be good at our job. That's probably the most important characteristic to bioinformatician. They are usually an excellent knowledge worker with great technical abilities, and have the will to be that over decades!<br /> <br /> <strong>2. Avoid the sentence </strong><strong>"I did not know what to do!"</strong><br /> In our computational biology lab, we generally face lots of technical problems. But as you know, it's impossible to know everything to do the computational biology jobs ( Yup.. because you need diverse and multidisciplinary knowledge to understand biological problems and resolve their respective solutions), therefore it's absolutely necessary that a bioinformatician finds its way through a new topic. How I typically do that is I use google and I talk to other experts in our laboratory or online biostar community to find out what they think. "I did not know what to do!" should not be an argument for us.<strong><br /><br /> <strong>3. To make oneself useful</strong></strong><br /> Several time it does happen, you finished our task earlier than expected; in such cases if you have some time left then: Take a coffee and play chess; reversi, etc. In my case I take a rest. Afterwards I think about what I could do that helps the team to achieve its targets, 'cause some of my team mates probably didn't finish! (at least if I didn't met them at coffee bar !!)</p><p><strong>4. Care for all</strong><br /> During my rigorous research duration; I attended several workshop organized by my University departments. I had a discussion with other research fellow, professors; I generally ask &hellip; what it really takes to make a team successful or to be a successful research leader. They always said: "Well, you need some caring people!" I think there is a lot truth in that statement. If we do not care about quality, timelines, good team culture, respectful communication (!!), clean code, if all this doesn&rsquo;t matter to us, then I believe the probability is higher that we fail in research and analysis. <br /> <br /> <strong>5. Be good with people</strong><br /> Because bioinformatician and computational biologist jobs typically involves to work in a (most wanted J cross-departmental!) team, therefore it's important that we're (more or less) good in dealing with other individuals. Everyone have their own strengths and weaknesses, just like us. It's important to treat all the research team mates with respect, regardless of their technical competence or contributions. Of course, sometimes people deserve a clear statement (!!!), but try to do these things one-on-one. Make sure nobody loses his face. Attend the meetings at the coffee bar; be good at table top soccer and go out once in a while to have a beer with your team. You know what I'm talking about.</p><p>At the end of a week I look back and I ask myself what I have produced. This could be paperwork, community days or (best!!) programming code. Always remember there is always a solution to a problem. Most of the times there are at least three solutions. So, don&rsquo;t just blame, suggest a solution.<br /> <br /> That's it. I am looking forward to your thoughts and comments!</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/41899/stay-at-home-revbayes-workshop</guid>
  <pubDate>Sat, 20 Jun 2020 11:53:24 -0500</pubDate>
  <link></link>
  <title><![CDATA[Stay-at-Home RevBayes Workshop]]></title>
  <description><![CDATA[
<p>Stay-at-Home RevBayes Workshop<br />Location: Anywhere (online-only event)<br />Dates: 7/13, 2020 to 8/12, 2020<br />Instructors: Joëlle Barido-Sottani, Walker Pett, Josh Justison, Wade Dismukes, Luiza Fabreti, Tracy Heath, Jeremy M. Brown, Rosana Zenil-Ferguson<br />Register: https://iastate.qualtrics.com/jfe/form/SV_02sCYRWbxYK9I5D</p>

<p>Description<br />This free online-only RevBayes workshop will provide an introduction to the theory and use of RevBayes, with a focus on (1) tree inference from molecular data, (2) analyses combining fossil and extant taxa, and (3) evaluating MCMC performance, with advanced topics including assessing model adequacy and macroevolutionary analyses. Additional topics may be added depending on the interests of selected participants. The format will be a combination of interactive video sessions (via Zoom or similar tools), real-time discussions over Slack, self-guided tutorials, and pre-recorded videos.</p>

<p>The initial session will resolve technical issues and present the basics of using RevBayes. Participants will then be expected to work through several tutorials on their own schedule, with the help of pre-recorded materials. A Slack forum will be open for questions and issues. The workshop will conclude with several online Q&amp;A sessions with the instructors. The dates for the interactive sessions are currently tentative and may be adjusted depending on the schedules of the participants and instructors.</p>

<p>We are hoping to identify up to 15 participants for this online course. While we hope we are able to accommodate everyone who applies, we realize that this may not be possible because of time-zones and availability. If the number of applicants exceeds our capacity, we hope to organize a second round of sessions later in the year. Participants will not be charged for the course, but we will request that they commit to completing the tutorials and attending a majority of interactive sessions.</p>

<p>To apply to this course, please go to the registration form and submit your application by July 6, 2020.</p>

<p>More at https://revbayes.github.io/workshops/online2020.html</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40946/free-genomics-data</guid>
	<pubDate>Fri, 07 Feb 2020 14:08:31 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40946/free-genomics-data</link>
	<title><![CDATA[Free Genomics data !]]></title>
	<description><![CDATA[<p><span>The specimens were collected by the Oxford Wytham Woods and Edinburgh Lohse lab teams. DNA extraction and sequencing was carried out by the Sanger Institute Scientific Operations teams. Assemblies were carried out by the Tree of Life team (Shane McCarthy) and colleagues in Pacific Biosciences (Jonas Korlach).</span></p>
<p><a href="https://www.darwintreeoflife.org/an-initial-set-of-raw-genome-assemblies-from-the-darwin-tree-of-life-project/">https://www.darwintreeoflife.org/an-initial-set-of-raw-genome-assemblies-from-the-darwin-tree-of-life-project/</a></p><p>Address of the bookmark: <a href="https://www.darwintreeoflife.org/an-initial-set-of-raw-genome-assemblies-from-the-darwin-tree-of-life-project/" rel="nofollow">https://www.darwintreeoflife.org/an-initial-set-of-raw-genome-assemblies-from-the-darwin-tree-of-life-project/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44513/mike-an-ultrafast-assembly-and-alignment-free-approach-for-phylogenetic-tree-construction</guid>
	<pubDate>Mon, 08 Apr 2024 06:19:52 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44513/mike-an-ultrafast-assembly-and-alignment-free-approach-for-phylogenetic-tree-construction</link>
	<title><![CDATA[MIKE: an ultrafast, assembly-, and alignment-free approach for phylogenetic tree construction]]></title>
	<description><![CDATA[<p><span>MIKE (MinHash-based&nbsp;</span><em>k</em><span>-mer algorithm). This algorithm is designed for the swift calculation of the Jaccard coefficient directly from raw sequencing reads and enables the construction of phylogenetic trees based on the resultant Jaccard coefficient. Simulation results highlight the superior speed of MIKE compared to existing state-of-the-art methods. We used MIKE to reconstruct a phylogenetic tree, incorporating 238 yeast, 303&nbsp;</span><em>Zea</em><span>, 141&nbsp;</span><em>Ficus</em><span>, 67&nbsp;</span><em>Oryza</em><span>, and 43&nbsp;</span><em>Saccharum spontaneum</em><span>&nbsp;samples. MIKE demonstrated accurate performance across varying evolutionary scales, reproductive modes, and ploidy levels, proving itself as a powerful tool for phylogenetic tree construction.</span></p><p>Address of the bookmark: <a href="https://github.com/Argonum-Clever2/mike" rel="nofollow">https://github.com/Argonum-Clever2/mike</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
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