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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/26573?offset=590</link>
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	<description><![CDATA[]]></description>
	
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43799/kast</guid>
	<pubDate>Wed, 23 Feb 2022 08:28:36 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43799/kast</link>
	<title><![CDATA[KAST]]></title>
	<description><![CDATA[<p><span>Perform Alignment-free k-tuple frequency comparisons from sequences. This can be in the form of two input files (e.g. a reference and a query) or a single file for pairwise comparisons to be made.</span></p><p>Address of the bookmark: <a href="https://github.com/martinjvickers/KAST" rel="nofollow">https://github.com/martinjvickers/KAST</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/file/view/22053/binc-sample-question-paper</guid>
	<pubDate>Thu, 16 Apr 2015 09:16:18 -0500</pubDate>
	<link>https://bioinformaticsonline.com/file/view/22053/binc-sample-question-paper</link>
	<title><![CDATA[BINC Sample Question Paper !!!]]></title>
	<description><![CDATA[<p>BINC sample question paper. Wish you all the best for BINC examination.</p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
	<enclosure url="https://bioinformaticsonline.com/file/download/22053" length="4041" type="text/plain" />
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44223/ale-assembly-likelihood-estimator</guid>
	<pubDate>Wed, 08 Mar 2023 01:39:33 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44223/ale-assembly-likelihood-estimator</link>
	<title><![CDATA[ALE: Assembly Likelihood Estimator]]></title>
	<description><![CDATA[<p>Just import the assembly, bam and ALE scores. You can convert the .ale file to a set of .wig files with ale2wiggle.py and IGV can read those directly.&nbsp; Depending on your genome size you may want to convert the .wig files to the BigWig format.</p><p>Address of the bookmark: <a href="https://github.com/sc932/ALE" rel="nofollow">https://github.com/sc932/ALE</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/44371/steps-to-find-all-the-repeats-in-the-genome</guid>
	<pubDate>Thu, 31 Aug 2023 02:43:28 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/44371/steps-to-find-all-the-repeats-in-the-genome</link>
	<title><![CDATA[Steps to find all the repeats in the genome !]]></title>
	<description><![CDATA[<div><p>To find repeats in a genome from 2 to 9 length using a Perl script, you can use the RepeatMasker tool with the "--length" option<a href="https://mobilednajournal.biomedcentral.com/articles/10.1186/1759-8753-5-13" target="_blank">[0]</a>. Here's a step-by-step guide:</p></div><div><ol>
<li>Install RepeatMasker: First, you need to install RepeatMasker on your system. You can download it from the RepeatMasker website<a href="https://mobilednajournal.biomedcentral.com/articles/10.1186/1759-8753-5-13" target="_blank">[0]</a>.</li>
</ol></div><div><ol>
<li>Prepare the genome sequence: Make sure you have the genome sequence in a FASTA file format. Let's assume the file is named "genome.fasta".</li>
</ol><blockquote><p>./RepeatMasker -pa &lt;number_of_processors&gt; -nolow -norna -no_is -div &lt;divergence_value&gt; -lib RepeatMaskerLib.embl -gff -xsmall -small -poly -species &lt;species_name&gt; -dir &lt;output_directory&gt; -length &lt;min_length&gt;-&lt;max_length&gt; genome.fasta</p></blockquote><div><p>Replace the following placeholders with appropriate values:</p><ul>
<li><code>&lt;number_of_processors&gt;</code>: The number of processors/threads you want to use for parallel processing.</li>
<li><code>&lt;divergence_value&gt;</code>: The divergence value for the species you are analyzing. You can find divergence values for different species in the RepeatMasker documentation<a href="https://mobilednajournal.biomedcentral.com/articles/10.1186/1759-8753-5-13" target="_blank">[0]</a>.</li>
<li><code>&lt;species_name&gt;</code>: The name of the species you are analyzing.</li>
<li><code>&lt;output_directory&gt;</code>: The directory where you want the output files to be saved.</li>
<li><code>&lt;min_length&gt;</code>&nbsp;and&nbsp;<code>&lt;max_length&gt;</code>: The minimum and maximum lengths of the repeats you want to find (in this case, 2 and 9).</li>
</ul></div><div><ol>
<li>Analyze the output: RepeatMasker will generate several output files, including a .out file. You can parse this file to extract the information you need. There is a Perl tool called "one_code_to_find_them_all.pl" that can help you parse RepeatMasker output files<a href="https://mobilednajournal.biomedcentral.com/articles/10.1186/1759-8753-5-13" target="_blank">[0]</a>. You can download it from the source provided.</li>
</ol></div><div><ol>
<li>Use the provided Perl script: Once you have the "one_code_to_find_them_all.pl" script, you can run it to conveniently parse the RepeatMasker output files. Here's an example of how to use it:</li>
</ol><blockquote><p>perl one_code_to_find_them_all.pl --rm &lt;RepeatMasker_out_file&gt; --length &lt;length_file&gt;</p></blockquote></div><p>&nbsp;</p></div><div><div><p>Replace&nbsp;<code>&lt;RepeatMasker_out_file&gt;</code>&nbsp;with the path to your RepeatMasker .out file, and&nbsp;<code>&lt;length_file&gt;</code>&nbsp;with the path to a file containing the lengths of the reference elements.</p></div><div><p>This script will generate several output files, including .log.txt and .copynumber.csv, which contain quantitative information about the identified repeat elements.</p></div><div><p>Remember to adjust the parameters and options according to your specific needs and the characteristics of your genome.</p></div></div>]]></description>
	<dc:creator>Neel</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/22239/jrf-bioinformatics-at-national-institute-of-high-security-animal-diseases-icar</guid>
  <pubDate>Tue, 28 Apr 2015 02:21:15 -0500</pubDate>
  <link></link>
  <title><![CDATA[JRF Bioinformatics at National Institute of High Security Animal Diseases (ICAR)]]></title>
  <description><![CDATA[
<p>F.No. 4-59/2013-NIHSAD Dated: 21st April, 2015</p>

<p>SRF/ JRF job vacancies in National Institute of High Security Animal Diseases (ICAR)</p>

<p>Name of Post : JRF</p>

<p>No. of Post : 01</p>

<p>Qualification : M.V.Sc or M.Tech./M.Sc. (preferably with NET qualification) in one of following disciplines. (Biotechnology/Molecular Biology/Genetics/Microbiology/Bioinformatics or equivalent Life Sciences discipline). Desirable: Working Knowledge in the areas of Recombinant DNA Techniques, Cell Culture, Handling Laboratory Animals, Genomics.</p>

<p>Emolument : Rs.16,000/-</p>

<p>Age Limit : Up to 30 years</p>

<p>Name of Post : Project Assistant</p>

<p>No of Post : 01</p>

<p>Qualifications : First class M.Sc. /B.E/B.Tech. in one of the following disciplines Biotechnology/ Bioinformatics/ Microbiology or equivalent Life Sciences discipline). Desirable : Exposure of working in research environment, Good command over written/spoken English and computer applications.</p>

<p>Emolument : Rs.8000/-</p>

<p>Age Limit : Upto 28 years</p>

<p>Name of Post : Project Assistant</p>

<p>No of Post : 01</p>

<p>Qualification : First class M.Sc. /B.V.Sc. and A.H., B.Tech. /B.E. in Life Sciences and related areas. Desirable: Exposure of working in research environment, Good command over written/type written/spoken English and computer applications. </p>

<p>Emoluments : Rs. 8000/-</p>

<p>Age Limit : Up to 28 years<br />How to apply</p>

<p>Desirous candidates may send their applications by e-mail (techcell@hsadl.nic.in ) followed by post in the prescribed proforma latest by 11/05/2015. Walk-in-Interview will be held at NIHSAD, Kokta Road, Anand Nagar, Bhopal-462022.</p>

<p>http://www.nihsad.nic.in/pdf/Advt.pdf</p>
]]></description>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/44637/tools-to-access-the-quality-of-your-assembled-genome</guid>
	<pubDate>Thu, 08 Aug 2024 23:31:18 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/44637/tools-to-access-the-quality-of-your-assembled-genome</link>
	<title><![CDATA[Tools to access the quality of your assembled genome !]]></title>
	<description><![CDATA[<ul dir="auto">
<li><a href="https://github.com/linsalrob/fasta_validator">FASTA VALIDATOR</a>&nbsp;+&nbsp;<a href="https://github.com/shenwei356/seqkit">SEQKIT RMDUP</a>: FASTA validation</li>
<li><a href="https://genometools.org/tools/gt_gff3validator.html">GENOMETOOLS GT GFF3VALIDATOR</a>: GFF3 validation</li>
<li><a href="https://github.com/PlantandFoodResearch/assemblathon2-analysis/blob/a93cba25d847434f7eadc04e63b58c567c46a56d/assemblathon_stats.pl">ASSEMBLATHON STATS</a>: Assembly statistics</li>
<li><a href="https://genometools.org/tools/gt_stat.html">GENOMETOOLS GT STAT</a>: Annotation statistics</li>
<li><a href="https://github.com/ncbi/fcs">NCBI FCS ADAPTOR</a>: Adaptor contamination pass/fail</li>
<li><a href="https://github.com/ncbi/fcs">NCBI FCS GX</a>: Foreign organism contamination pass/fail</li>
<li><a href="https://gitlab.com/ezlab/busco">BUSCO</a>: Gene-space completeness estimation</li>
<li><a href="https://github.com/tolkit/telomeric-identifier">TIDK</a>: Telomere repeat identification</li>
<li><a href="https://github.com/oushujun/LTR_retriever/blob/master/LAI">LAI</a>: Continuity of repetitive sequences</li>
<li><a href="https://github.com/DerrickWood/kraken2">KRAKEN2</a>: Taxonomy classification</li>
<li><a href="https://github.com/igvteam/juicebox.js">HIC CONTACT MAP</a>: Alignment and visualisation of HiC data</li>
<li><a href="https://github.com/mummer4/mummer">MUMMER</a>&nbsp;&rarr;&nbsp;<a href="http://circos.ca/documentation/">CIRCOS</a>&nbsp;+&nbsp;<a href="https://plotly.com/">DOTPLOT</a>&nbsp;&amp;&nbsp;<a href="https://github.com/lh3/minimap2">MINIMAP2</a>&nbsp;&rarr;&nbsp;<a href="https://github.com/schneebergerlab/plotsr">PLOTSR</a>: Synteny analysis</li>
<li><a href="https://github.com/marbl/merqury">MERQURY</a>: K-mer completeness, consensus quality and phasing assessment</li>
</ul>]]></description>
	<dc:creator>LEGE</dc:creator>
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  <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>
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	<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>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/22352/affy-has-acquired-eureka-genomics-for-15m</guid>
	<pubDate>Wed, 20 May 2015 15:11:20 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/22352/affy-has-acquired-eureka-genomics-for-15m</link>
	<title><![CDATA[Affy has acquired Eureka Genomics for 15M $]]></title>
	<description><![CDATA[<p>Affymetrix Acquires Assets Of Eureka Genomics Corporation To Provide High Throughput And Economical Crop And Animal Genotyping</p><p>http://www.thestreet.com/story/13151062/1/affymetrix-acquires-assets-of-eureka-genomics-corporation-to-provide-high-throughput-and-economical-crop-and-animal-genotyping.html</p>]]></description>
	<dc:creator>Martin Jones</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/120/user</guid>
	<pubDate>Wed, 10 Jul 2013 14:41:49 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/120/user</link>
	<title><![CDATA[useR!]]></title>
	<description><![CDATA[<p><span>The R project actively supports two conference series, organized regularly by members from the R community: useR! - providing a forum to the R user community - and DSC - a platform for developers of statistical software.</span></p><p><span>Recently useR! conference have been organized&nbsp;<span>University of Castilla-La Mancha, Albacete, Spain.</span></span></p><p><a href="http://www.edii.uclm.es/~useR-2013//">http://www.edii.uclm.es/~useR-2013//</a></p><p>&nbsp;</p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
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