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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/29108?offset=920</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44766/genome-simulation-with-slim-and-msprime</guid>
	<pubDate>Fri, 31 Jan 2025 12:47:43 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44766/genome-simulation-with-slim-and-msprime</link>
	<title><![CDATA[Genome Simulation with SLiM and msprime]]></title>
	<description><![CDATA[<p>Genome simulation is an essential tool in population genetics, enabling researchers to model evolutionary processes and study genetic variation. Two widely used simulation tools in this field are <strong style="font-size: 12.8px;">SLiM</strong><span style="font-size: 12.8px; font-weight: normal;"> and </span><strong style="font-size: 12.8px;">msprime</strong><span style="font-size: 12.8px; font-weight: normal;">. While both serve different purposes, they can be used together with the </span><strong style="font-size: 12.8px;">slendr</strong><span style="font-size: 12.8px; font-weight: normal;"> framework to compare simulation outputs effectively.</span></p><h2>Overview of SLiM and msprime</h2><h3>SLiM: Forward Genetic Simulator</h3><p>SLiM is a <strong>free, open-source</strong> tool designed for forward genetic simulations. It allows researchers to model complex evolutionary scenarios, including selection, recombination, and demographic events, making it particularly useful for studying adaptation and selection in populations.</p><p><strong>Key Features of SLiM:</strong></p><ul>
<li>
<p>Simulates population evolution forward in time</p>
</li>
<li>
<p>Supports custom evolutionary models using an embedded scripting language</p>
</li>
<li>
<p>Allows modeling of spatial and ecological dynamics</p>
</li>
<li>
<p>Provides high flexibility and extensibility for user-defined scenarios</p>
</li>
<li>
<p>Available on GitHub as an open-source project</p>
</li>
</ul><h3>msprime: Ancestry and Mutation Simulator</h3><p>msprime is an efficient, <strong>open-source</strong> tool that simulates ancestry and mutations using a coalescent framework. It is known for its high-speed performance and low memory requirements, making it a popular choice for large-scale genomic simulations.</p><p><strong>Key Features of msprime:</strong></p><ul>
<li>
<p>Implements coalescent simulations for ancestry modeling</p>
</li>
<li>
<p>Efficiently simulates large population histories</p>
</li>
<li>
<p>Supports the addition of mutations to genealogies</p>
</li>
<li>
<p>Developed using an open-source community model</p>
</li>
<li>
<p>Often faster and more memory-efficient than alternative simulators</p>
</li>
</ul><h2>Using SLiM and msprime with slendr</h2><p>Both SLiM and msprime can be integrated with <strong>slendr</strong>, a framework that facilitates structured population genetic simulations. This integration allows for seamless comparison of simulation outputs.</p><h3>How They Work Together:</h3><ul>
<li>
<p>SLiM and msprime simulations can be analyzed within slendr.</p>
</li>
<li>
<p>The <strong>ts_read()</strong> function in slendr enables loading and comparing tree sequence outputs from both simulators.</p>
</li>
<li>
<p>This integration allows researchers to validate simulation results and gain deeper insights into evolutionary processes.</p>
</li>
</ul><h2>Performance Considerations</h2><p>While SLiM offers powerful forward simulations with extensive customization, msprime is often preferred for its <strong>speed and memory efficiency</strong> when simulating ancestry and mutations. The choice between the two depends on the research goals:</p><ul>
<li>
<p><strong>For detailed evolutionary modeling with selection and recombination:</strong> Use SLiM.</p>
</li>
<li>
<p><strong>For large-scale coalescent simulations with mutations:</strong> Use msprime.</p>
</li>
<li>
<p><strong>For comparing different simulation models and their outputs:</strong> Use slendr to integrate SLiM and msprime results.</p>
</li>
</ul><h2>Conclusion</h2><p>SLiM and msprime are valuable tools for genome simulation, each serving distinct but complementary purposes in population genetics research. By leveraging the strengths of both simulators with slendr, researchers can conduct robust and efficient evolutionary simulations, enhancing our understanding of genetic diversity and adaptation.</p><p>For more information, check out the official GitHub repositories for <strong>SLiM</strong> and <strong>msprime</strong>, and explore the <strong>slendr</strong> framework for streamlined simulation workflow</p>]]></description>
	<dc:creator>BioStar</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/18819/jrfsrf-at-jawaharlal-nehru-institute-ofadvanced-studies-jnias-hyderabad</guid>
  <pubDate>Fri, 31 Oct 2014 08:48:23 -0500</pubDate>
  <link></link>
  <title><![CDATA[JRF/SRF at Jawaharlal Nehru Institute ofAdvanced Studies (JNIAS), Hyderabad]]></title>
  <description><![CDATA[
<p>Applications for Academic Projects in Biotechnology, Bioinformatics, Environmental Sciences and Computer Science &amp; Engineering</p>

<p>About JNIAS<br />Jawaharlal Nehru Institute of Advanced Studies (JNIAS), Hyderabad has been established by Dr. D. Swaminadhan Research Foundation (DSRF), Hyderabad as a Research and Educational Institution with a view to contribute in developing advanced technologies and build „core competence‟ in specific areas. The activities of JNIAS involves: Education, Research Training and Innovations in the fields of Sciences, Technologies, Humanities and Social Sciences. It aims to blossom into an Advanced Institute of education and research with a reservoir of expertise and experience in the relevant fields and the necessary capability to harness multi-disciplinary research and studies. JNIAS has been recognized as an Advanced Research Institute by Jawaharlal Nehru Technological University Hyderabad (JNTUH), Hyderabad and Jawaharlal Nehru Technological University Anantapur (JNTUA), for offering Ph.D., P.G M.Phil, P.G Diploma and Training Programmes in Sciences and Engineering &amp; Technology.</p>

<p>Jawaharlal Nehru Architecture and Fine Arts University (JNAFAU) Hyderabad also recognized JNIAS for offering UG, PG degree in Architecture.</p>

<p>Projects &amp; Facilities</p>

<p>JNIAS offers wide range of projects:</p>

<p>Biotechnology area:</p>

<p>Molecular Biology<br />Microbiology<br />Nanotechnology<br />Bioinformatics (Schrodinger Software)<br />In Silico studies &amp; Drug Designing<br />Sequence analysis<br />Protein structure function studies</p>

<p>Registration<br />Tuition Fees: Interested students need to pay the following tuition fees:<br />1. Six Month’s Project: Rs. 20,000/-<br />2. Four Month’s Project: Rs. 15,000/-<br />3. Three Month’s Project: Rs. 10,000/-<br />4. One Month - Hands on Training : Rs. 8,000/-</p>

<p>For enquires call:<br />91-7893203414 (Biotechnology), 91-9949582263 (Environmental Sciences) 91-8977369305 (Computer Science)</p>

<p>Interested student may download the application from the website (www.jnias.in) and send the hard copy of the completed application forms and Curriculum Vitae along with the Demand Draft drawn on any nationalized Banks in favor of “The Registrar, JNIAS, Secunderabad”. Application forms can be sent through email to academicprojects@jnias.in</p>

<p>Address<br />Jawaharlal Nehru Institute of Advanced Studies (JNIAS)<br />6th Floor, Buddha Bhavan, M.G Road,<br />Secunderabad - 500 003<br />Andhra Pradesh, India<br />Tele/Fax: 040- 27541551; 27541553<br />Mobile: 08885541554<br />Web site: www.jnias.in</p>

<p>Brochure : https://drive.google.com/file/d/0B3zPwhgA-u-nU0dyMFd2OWcxNUpSTWNYc0xDSGs5UDI4UDNB/view?usp=sharing</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/view/2044</guid>
	<pubDate>Mon, 12 Aug 2013 12:19:29 -0500</pubDate>
	<link>https://bioinformaticsonline.com/view/2044</link>
	<title><![CDATA[Does anyone have Nanopore latest updates?]]></title>
	<description><![CDATA[<p>There was a lot of buzz about&nbsp;<span>Oxford Nanopore Technologies&reg; is developing the GridION&trade; system and miniaturised MinION&trade; device. These are a new generation of electronic molecular analysis system for use in scientific research, personalised medicine, crop science, security/defence and more. The platform technology uses nanopores to analyse single molecules including DNA/RNA and proteins. With a broad patent portfolio, the Oxford Nanopore pipeline includes biological nanopores and solid-state nanopores.</span></p><p>Is this available, or still under trial mode?&nbsp;</p><p><a href="https://www.nanoporetech.com/">https://www.nanoporetech.com/</a></p><p><a href="https://www.nanoporetech.com/technology/the-minion-device-a-miniaturised-sensing-system/the-minion-device-a-miniaturised-sensing-system">https://www.nanoporetech.com/technology/the-minion-device-a-miniaturised-sensing-system/the-minion-device-a-miniaturised-sensing-system</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/18382/google-genomics</guid>
	<pubDate>Fri, 17 Oct 2014 02:14:14 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/18382/google-genomics</link>
	<title><![CDATA[Google Genomics]]></title>
	<description><![CDATA[<p>Google Genomics provides an API to store, process, explore, and share DNA sequence reads, reference-based alignments, and variant calls, using Google's cloud infrastructure.</p>
<ul>
<li><strong>Store</strong> alignments and variant calls for one genome or a million.</li>
<li><strong>Process</strong> genomic data in batch by running principal component analysis or Hardy-Weinberg equilibrium, in minutes or hours, by using parallel computing frameworks like MapReduce.</li>
<li><strong>Explore</strong> data by slicing alignments and variants by genomic range across one or multiple samples -- for your own algorithms or for visualization; or interactively process entire cohorts to find transition/transversion ratios, allelic frequency, genome-wide association and more using BigQuery.</li>
<li><strong>Share</strong> genomic data with your research group, collaborators, the broader community, or the public. You decide.</li>
</ul>
<p>Google Genomics is implementing the API defined by the <a href="http://genomicsandhealth.org/">Global Alliance for Genomics and Health</a> for visualization, analysis and more. Compliant software can access Google Genomics, local servers, or any other implementation.</p><p>Address of the bookmark: <a href="https://cloud.google.com/genomics/" rel="nofollow">https://cloud.google.com/genomics/</a></p>]]></description>
	<dc:creator>Reshma Khatun</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/18741/a-powerful-yet-simple-gene-set-analysis-tool-for-interpreting-rna-seq-and-ngs-results</guid>
	<pubDate>Thu, 30 Oct 2014 09:19:29 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/18741/a-powerful-yet-simple-gene-set-analysis-tool-for-interpreting-rna-seq-and-ngs-results</link>
	<title><![CDATA[A powerful, yet simple, gene set analysis tool for interpreting RNA-seq and NGS results.]]></title>
	<description><![CDATA[<p>LifeMap Sciences is introducing&nbsp;<a href="http://geneanalytics.genecards.org/">GeneAnalytics</a>, our new gene set analysis tool, which is applicable for NGS results and differentially expressed gene lists from variable sources. GeneAnalytics provides&nbsp;gene associations with tissues &amp; cells, diseases, pathways, GO terms and compounds.</p><p>Our main advantages over other similar tools are:</p><ul>
<li>GeneAnalytics is very simple and intuitive to use.</li>
<li>GeneAnalytics is based on our proprietary databases &ndash;&nbsp;<strong>GeneCards</strong>, MalaCards, PathCards and LifeMap Discovery, each of them integrates information from a very large number of resources.</li>
<li>GeneAnalytics supplies links for extensive background information on each of the matched results.</li>
</ul><p>&nbsp;</p><p>I invite you to try it out for free at&nbsp;geneanalytics.genecards.org, and would be happy to hear your comments and thoughts on how we can improve.</p><p>&nbsp;</p><p>Yours,</p><p>Shani Ben-Ari Fuchs</p><p>LifeMap Sciences Team</p>]]></description>
	<dc:creator>Shani</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/18578/research-scientist-%E2%80%93-national-institute-of-cholera-and-enteric-diseases</guid>
  <pubDate>Wed, 22 Oct 2014 10:26:46 -0500</pubDate>
  <link></link>
  <title><![CDATA[Research Scientist – National Institute of Cholera and Enteric Diseases]]></title>
  <description><![CDATA[
<p>The following post is to be filled up on purely temporary basis under the project entitled "Second phase of Task Force Biomedical Informatics Center of ICMR" under Dr. Santasabuj Das, Scientist 'D' of this Institute:-</p>

<p>01. Scientist II 01<br />Essential: Ph.D. degree in Life Sciences from a recognized university along with a minimum of 2 years of research experience in Bioinformatics as evidenced by publications in the peer reviewed journals.</p>

<p>OR<br />Ph.D. degree in Bioinformatics from a recognized university.</p>

<p>OR<br />M.Sc. in Bioinformatics from a recognized university along with a minimum of 3 years of research experience in Bioinformatics as evidenced by publications in the peer reviewed journals.</p>

<p>Desirable:<br />Thorough Knowledge about In silico genome analysis and comparative genomics.<br />Experience with in silico identification of novel virulence factors of pathogens, host-pathogen interactions and Systems Biology.<br />Additional Postdoctoral research experience in relevant subjects from a recognized institutions.</p>

<p>Rs. 44,000/- p.m. (consolidated) plus 30% HRA</p>

<p>Below 40 years</p>

<p>Applications along with Bio-Data containing detail work experience and full list of publications may be sent via email tosantasabujdas@yahoo.com latest by October 27, 2014.</p>

<p>Short-listed candidates will be called via email for an interview to be held at the institute in the second week of November, 2014.</p>

<p>Advertisement: www.niced.org.in/placements.htm</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35418/karyoploter-plot-whole-genomes-with-arbitrary-data</guid>
	<pubDate>Fri, 02 Feb 2018 03:24:28 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35418/karyoploter-plot-whole-genomes-with-arbitrary-data</link>
	<title><![CDATA[karyoploteR: plot whole genomes with arbitrary data]]></title>
	<description><![CDATA[<p><span><a href="http://bioconductor.org/packages/karyoploteR">karyoploteR</a></span><span>&nbsp;is an R package to create karyoplots, that is, representations of whole genomes with arbitrary data plotted on them. It is inspired by the R base graphics system and does not depend on other graphics packages. The aim of karyoploteR is to offer the user an easy way to plot data along the genome to get broad genome-wide view to facilitate the identification of genome wide relations and distributions.</span></p><p>Address of the bookmark: <a href="https://bernatgel.github.io/karyoploter_tutorial/" rel="nofollow">https://bernatgel.github.io/karyoploter_tutorial/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/18866/celebrating-crystallography-an-animated-adventure</guid>
	<pubDate>Fri, 31 Oct 2014 15:59:00 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/18866/celebrating-crystallography-an-animated-adventure</link>
	<title><![CDATA[Celebrating Crystallography - An animated adventure]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/uqQlwYv8VQI" frameborder="0" allowfullscreen></iframe>NEW: Now with French or Spanish subtitles (click on the 'Captions' icon to select). Plus... Watch the French language version here: https://www.youtube.com/watch?v=PvLu7BOsJhM

X-ray crystallography is arguably one of the greatest innovations of the twentieth century, but not that many people know what it is or how it came about.

Join us on an animated journey through the 100 year history of crystallography -- from the pioneering work of William and Lawrence Bragg in 1913 to the surface of Mars!

Narrated by structural biologist Stephen Curry and produced by animation company 12foot6, the film explores the extraordinary history of crystallography. To date 28 Nobel Prizes have been awarded to projects related to the field and X-ray crystallography remains the foremost technique in determining the structures of a huge range of complex molecules.

This film was produced in celebration of the Bragg Centenary and was funded by STFC.

Watch more science videos on the amazing Ri Channel: http://richannel.org

Watch more animations from 12foot6: http://12foot6.com/

The Ri is on Twitter: http://twitter.com/ri_science
and Facebook: http://www.facebook.com/royalinstitution
Subscribe for the latest science videos: http://richannel.org/newsletter]]></description>
	
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32946/grass-a-generic-algorithm-for-scaffolding-next-generation-sequencing-assemblies</guid>
	<pubDate>Tue, 23 May 2017 05:20:32 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32946/grass-a-generic-algorithm-for-scaffolding-next-generation-sequencing-assemblies</link>
	<title><![CDATA[GRASS: a generic algorithm for scaffolding next-generation sequencing assemblies.]]></title>
	<description><![CDATA[<p><span>GRASS (GeneRic ASsembly Scaffolder)-a novel algorithm for scaffolding second-generation sequencing assemblies capable of using diverse information sources. GRASS offers a mixed-integer programming formulation of the contig scaffolding problem, which combines contig order, distance and orientation in a single optimization objective. The resulting optimization problem is solved using an expectation-maximization procedure and an unconstrained binary quadratic programming approximation of the original problem. We compared GRASS with existing HTS scaffolders using Illumina paired reads of three bacterial genomes. Our algorithm constructs a comparable number of scaffolds, but makes fewer errors. This result is further improved when additional data, in the form of related genome sequences, are used.</span></p><p>Address of the bookmark: <a href="https://github.com/AlexeyG/GRASS" rel="nofollow">https://github.com/AlexeyG/GRASS</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/19086/postdoctoral-fellowship-in-bioinformatics</guid>
  <pubDate>Sat, 08 Nov 2014 14:41:14 -0600</pubDate>
  <link></link>
  <title><![CDATA[Postdoctoral fellowship in Bioinformatics]]></title>
  <description><![CDATA[
<p>A two-year post-doctoral position is available in the Biocomputing group of the Sapienza University led by Anna Tramontano to work on either genomics research or structural bioinformatics, focusing on the study of relevant biomedical problems.<br />The ideal candidate should be motivated and talented, hold a PhD degree, have good programming skills, a grasp of statistical methods and an understanding of biology.<br />Experience in the development of computational biology methods would be an added value.</p>

<p>Good communication skills and fluency in spoken and written English are required.<br />Please apply sending a curriculum vitae, the names of at least two referees and a letter of motivation describing past experience and future goals to anna.tramontano@uniroma1.it with subject: “Application for post-doctoral position November 2014 YOUR LAST NAME”</p>

<p>Deadline: No later than November 28th, 2014.<br />Duration: 2 years</p>

<p>Salary on grant: Commeasured to the experience of the candidate<br />Contact Person (Referent): Anna Tramontano<br />Ref. E-Mail: anna.tramontano@uniroma1.it<br />Group Web Page: http:/www.biocomputing.it</p>
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