<?xml version='1.0'?><rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:georss="http://www.georss.org/georss" xmlns:atom="http://www.w3.org/2005/Atom" >
<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/26414?offset=810</link>
	<atom:link href="https://bioinformaticsonline.com/related/26414?offset=810" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	
<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/22024/research-associate-bioinformatics-job-position-in-indian-agricultural-statistics-research-institute-iasri-pusa-new-delhi</guid>
  <pubDate>Tue, 14 Apr 2015 11:57:13 -0500</pubDate>
  <link></link>
  <title><![CDATA[Research Associate Bioinformatics job position in Indian Agricultural Statistics Research Institute (IASRI), Pusa, New Delhi]]></title>
  <description><![CDATA[
<p>Indian Agricultural Statistics Research Institute is inviting applications from indian citizens for recruiting following posts:</p>

<p>Vacancies:<br />Research Associate-02<br />Age Limits:<br />Candidates age limit should be not more than 40 years as on date of interview.<br />Qualification:<br />Candidates should possess Ph.D in Bioinformatics/Agricultural Statistics/Statistics/Computer Science/Computer Application or equivalent.<br />Selection Process:<br />Selection will be based on interview.<br />How to Apply:<br />Eligible candidates may attend for interview along with application in prescribed format, recent passport size photograph pasted on the application form, bio-data, original certificates and self attested copies of relevant documents, all experience certificates, testimonials etc, held at Indian Agricultural Statistics Research Institute, Pusa, New Delhi on 18-04-2015 at 10:30 AM.<br />Last Date:<br />18-04-2015</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44489/proksee</guid>
	<pubDate>Wed, 27 Mar 2024 11:11:54 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44489/proksee</link>
	<title><![CDATA[Proksee]]></title>
	<description><![CDATA[<p><span>Proksee is an expert system for genome assembly, annotation and visualization. To begin using Proksee, provide a complete genome sequence, sequencing reads or a CGView/Proksee map JSON file.</span></p>
<fieldset><legend>Please Cite the Following</legend>
<div>Grant JR, Enns E, Marinier E, Mandal A, Herman EK, Chen C, Graham M, Van Domselaar G, and Stothard P</div>
<div><a href="https://pubmed.ncbi.nlm.nih.gov/37140037/">Proksee: in-depth characterization and visualization of bacterial genomes</a></div>
<div>Nucleic Acids Research, 2023, gkad326, https://doi.org/10.1093/nar/gkad326</div>
</fieldset><p>Address of the bookmark: <a href="https://proksee.ca/" rel="nofollow">https://proksee.ca/</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/file/view/22044/binc-sample-question-paper</guid>
	<pubDate>Thu, 16 Apr 2015 09:12:39 -0500</pubDate>
	<link>https://bioinformaticsonline.com/file/view/22044/binc-sample-question-paper</link>
	<title><![CDATA[BINC Sample Question Paper !!!]]></title>
	<description><![CDATA[<p>BINC sample question paper for round ONE.</p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
	<enclosure url="https://bioinformaticsonline.com/file/download/22044" length="1260" type="text/plain" />
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44628/uncovar-workflow-for-transparent-and-robust-virus-variant-calling-genome-reconstruction-and-lineage-assignment</guid>
	<pubDate>Mon, 05 Aug 2024 23:01:29 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44628/uncovar-workflow-for-transparent-and-robust-virus-variant-calling-genome-reconstruction-and-lineage-assignment</link>
	<title><![CDATA[UnCoVar: Workflow for Transparent and Robust Virus Variant Calling, Genome Reconstruction and Lineage Assignment]]></title>
	<description><![CDATA[<p>UnCoVar: Workflow for Transparent and Robust Virus Variant Calling, Genome Reconstruction and Lineage Assignment</p>
<ul>
<li>
<p>Using state of the art tools, easily extended for other viruses</p>
</li>
<li>
<p>Tool and database updates for critical components via Conda</p>
</li>
<li>
<p>Built using modern design patterns with Conda and Snakemake</p>
</li>
<li>
<p>Extensible and easy to customize</p>
</li>
<li>
<p>Submission Ready Genomes</p>
</li>
<li>
<p>Customizable reporting with comprehensive visualization</p>
</li>
</ul>
<p>https://ikim-essen.github.io/uncovar/</p>
<p>Github&nbsp;https://github.com/IKIM-Essen/uncovar</p>
<p>&nbsp;</p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="https://ikim-essen.github.io/uncovar/" rel="nofollow">https://ikim-essen.github.io/uncovar/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/file/view/22068/binc-examination-2015</guid>
	<pubDate>Fri, 17 Apr 2015 03:34:28 -0500</pubDate>
	<link>https://bioinformaticsonline.com/file/view/22068/binc-examination-2015</link>
	<title><![CDATA[BINC examination 2015 !!!]]></title>
	<description><![CDATA[<p>BioInformatics National Certification (BINC) Examination 2015 organized by Department of Biotechnology, Government of India, New Delhi Pondicherry University, Puducherry</p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
	<enclosure url="https://bioinformaticsonline.com/file/download/22068" length="281577" type="application/pdf" />
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44770/nvidia-and-arc-institute-unveil-evo-2-a-breakthrough-ai-for-dna-design</guid>
	<pubDate>Fri, 21 Feb 2025 10:39:47 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44770/nvidia-and-arc-institute-unveil-evo-2-a-breakthrough-ai-for-dna-design</link>
	<title><![CDATA[NVIDIA and Arc Institute Unveil Evo 2: A Breakthrough AI for DNA Design]]></title>
	<description><![CDATA[<p>NVIDIA and the Arc Institute have introduced <strong style="font-size: 12.8px;">Evo 2</strong>, a groundbreaking AI model designed to <strong style="font-size: 12.8px;">understand, predict, and generate DNA sequences</strong>. This marks a major advancement in computational biology, offering scientists an unprecedented tool to decode the genetic blueprint of life and even design entirely new biological systems.</p><h3><strong>The Power of Evo 2: AI Meets DNA</strong></h3><p>Evo 2 is <strong>the largest AI model for biology ever created</strong>, trained on an astonishing <strong>9.3 trillion DNA "letters"</strong> (nucleotides) carefully selected from genomes spanning the entire tree of life. This massive dataset ensures that Evo 2 can recognize patterns and relationships in genetic sequences at an unparalleled scale.</p><p>For the first time, scientists can <strong>design DNA with AI</strong>, moving beyond simple sequence analysis to active DNA generation. Evo 2 enables researchers to <strong>predict, modify, and even create entire genetic sequences</strong>, opening new possibilities in medicine, agriculture, and synthetic biology.</p><h3><strong>Decoding the Dark Genome</strong></h3><p>One of the biggest challenges in genetics is understanding the <strong>non-coding regions</strong> of DNA&mdash;vast stretches of the genome that do not code for proteins but play crucial roles in regulating gene expression. These regions control when and how genes are activated, influencing everything from development to disease.</p><p>Evo 2 is designed to <strong>decode these non-coding elements</strong>, helping researchers uncover their functions and use this knowledge to develop gene-based therapies, synthetic life forms, and precision agriculture solutions.</p><h3><strong>From Reading DNA to Writing It</strong></h3><p>To put Evo 2&rsquo;s impact into perspective:</p><ul>
<li><strong>Previous AI models could "read" DNA</strong> like a book, analyzing genetic sequences and identifying patterns.</li>
<li><strong>Evo 2 can "write" entirely new DNA</strong>, designing functional genes, chromosomes, and even full genomes from scratch.</li>
</ul><p>This means scientists can now <strong>engineer biological systems with AI</strong>, designing new proteins, metabolic pathways, and genetic circuits to address real-world challenges.</p><h3><strong>A Step Toward Generative Biology</strong></h3><p>The Arc Institute describes Evo 2 as a major step toward <strong>"generative biology"</strong>&mdash;a revolutionary approach where AI is used to create <strong>novel biological structures</strong> rather than just analyzing existing ones. This could lead to breakthroughs such as:</p><ul>
<li><strong>New medicines</strong>: AI-generated enzymes and proteins tailored for targeted therapies.</li>
<li><strong>Disease-resistant crops</strong>: Genetically optimized plants for higher yield and climate resilience.</li>
<li><strong>Synthetic organisms</strong>: Custom-designed microbes for bioremediation, biofuel production, and industrial applications.</li>
</ul><h3><strong>An Open-Source Revolution</strong></h3><p>Unlike many proprietary AI models, <strong>Evo 2 is open source</strong>, making its capabilities accessible to researchers worldwide. This democratization of AI-driven biology means that scientists from different disciplines can <strong>collaborate, experiment, and innovate</strong>, accelerating discoveries in genetic engineering and synthetic biology.</p><p>With Evo 2, the boundaries of what&rsquo;s possible in <strong>DNA design, genetic engineering, and biological innovation</strong> are being redrawn. The future of life sciences is no longer just about understanding life&rsquo;s code&mdash;it&rsquo;s about writing it.</p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/22234/national-institute-of-biologicals-recruitment-2015</guid>
  <pubDate>Mon, 27 Apr 2015 19:44:45 -0500</pubDate>
  <link></link>
  <title><![CDATA[National Institute of Biologicals Recruitment 2015]]></title>
  <description><![CDATA[
<p>National Institute of Biologicals (NIB), Noida<br />Job Code: 260415(04)Y</p>

<p>National Institute of Biologicals (NIB), Noida invites applications to recruit on vacant posts of Scientist, Training Officer, Administrative Assistant, Stenographer, Junior Engineer, Computer Operator etc. Applications against these Government Jobs can be submitted on or before 01 July 2015.</p>

<p>NIB Vacancy 2015 Details<br />1. Scientist Grade III – 06<br />Qualification: PG degree in the concern field.<br />Age Limit: 35 Years</p>

<p>2. Junior Scientist – 07<br />Qualification: M.Sc. in Microbiology / Clinical Microbiology / Biotechnology/ Bioinformatics/ Biochemistry/Bacteriology/Pharmacology/ Serology / Molecular Biology/Physiology from any recognized University with at least 60% marks.<br />Age Limit: 30 Years</p>

<p>How to Apply: Duly filled-in applications in prescribed application format along with copies of required documents should be reach to: Administrative Officer, National Institute of Biologicals (Ministry of Health &amp; Family Welfare), A-32, Sector-62, Institutional Area, Noida-201309. Click here to obtain application form.</p>

<p>The Last Date to apply to NIB Job is 01 July 2015.</p>

<p>Click here to view details http://nib.gov.in/Advt%20%20%2824.04.2015%29.pdf</p>
]]></description>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/22429/walk-ins-for-jrf-ans-srf-post-in-nirrh-mumbai</guid>
  <pubDate>Thu, 28 May 2015 19:04:57 -0500</pubDate>
  <link></link>
  <title><![CDATA[Walk-ins for JRF ans SRF post in NIRRH, Mumbai]]></title>
  <description><![CDATA[
<p>Title of project- "EXPLORING THE HINGE AND TRANSMEMBRANE REGION OF HUMAN FSHR FOR DESIGN OF SMALL MOLECULE AND PEPTIDOMIMETIC MODULATORS"<br />Name of the Post- Junior Research Fellow<br />No. of vacancy- One<br />Stipend- Rs. 25000/ +30% HRA<br />Essential qualification- Candidate should be Post Graduate Degree in Life Sciences / Bioinformatics /Pharmacology/ Chemistry or any other relevant area of Biology or Graduate Degree in Professional Course with NET qualification or Post Graduate Degree in Professional Course.<br />Desirable- Candidate with Good knowledge of protein structures, docking, MD simulations will be preferred.<br />Age Limit- Not exceeding 28 Years<br />Duration of project- Upto May 2018</p>

<p>Title of project- "Analysis of the structures of known antimicrobial peptides using machine learning algorithms and molecular dynamics simulations".<br />Name of the Post- Senior Research Fellow<br />No. of vacancy- One<br />Stipend- Rs. 14000/ +30% HRA<br />Essential qualification- Candidate should be having M.Sc. degree in Life Sciences / Bioinformatics / Pharmacology/ Chemistry or any other relevant area of Biology and 2 years of research experience.<br />Desirable- Candidate with Good knowledge of protein structures, docking, MD simulations will be preferred.<br />Age Limit- Not exceeding 35 Years<br />Duration of project- Upto April 2016<br />How to Apply- Interested candidates can download the application form from below mentioned link- http://www.nirrh.res.in/links/BiodataForm.pdf<br />Candidate must bring the filled up application form along with all the relevant documents in original and one set of attested photocopies of the same and one passport size recent colour photograph.</p>

<p>Ref. - http://www.nirrh.res.in/links/job_jrf-srf.htm</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43369/a-guide-to-machine-learning-for-biologists</guid>
	<pubDate>Wed, 15 Sep 2021 13:21:08 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43369/a-guide-to-machine-learning-for-biologists</link>
	<title><![CDATA[A guide to machine learning for biologists]]></title>
	<description><![CDATA[<p><span>We aim to provide readers with a gentle introduction to a few key machine learning techniques, including the most recently developed and widely used techniques involving deep neural networks. We describe how different techniques may be suited to specific types of biological data, and also discuss some best practices and points to consider when one is embarking on experiments involving machine learning. Some emerging directions in machine learning methodology are also&nbsp;discussed.</span></p><p>Address of the bookmark: <a href="https://www.nature.com/articles/s41580-021-00407-0" rel="nofollow">https://www.nature.com/articles/s41580-021-00407-0</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/22286/jrf-bioinformatics-pune-university</guid>
  <pubDate>Wed, 06 May 2015 06:21:09 -0500</pubDate>
  <link></link>
  <title><![CDATA[JRF Bioinformatics @ Pune University]]></title>
  <description><![CDATA[
<p>Pune University is inviting applications from indian citizens for recruiting following posts:</p>

<p>Vacancies:<br />Junior Research Fellow-04<br />Age Limits:<br />Candidates age should be not more than 28 years.<br />Qualification:<br />Candidates should possess M.Sc in Microbiology/ Marine microbiology/Marine Biotechnology/ Bioinformatics/Zoology or equivalent degree with minimum 60% marks.<br />Selection Process:<br />Shortlisted candidates will be called for interview.<br />How to Apply:<br />Eligible candidates may send their applications in the prescribed format along with CV in an envelope should be superscribed as the "Application for the post of JRF" to Head Department of Zoology, Savitribai Phule Pune University on or before 10-05-2015.<br />Last Date:<br />10-05-2015</p>
]]></description>
</item>

</channel>
</rss>