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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/41009?offset=140</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/45351/ai-uncovers-hidden-secrets-in-bacterial-dna-opening-new-frontiers-in-genomic-research</guid>
	<pubDate>Fri, 25 Sep 2026 22:38:42 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/45351/ai-uncovers-hidden-secrets-in-bacterial-dna-opening-new-frontiers-in-genomic-research</link>
	<title><![CDATA[AI Uncovers Hidden Secrets in Bacterial DNA, Opening New Frontiers in Genomic Research]]></title>
	<description><![CDATA[<div style="margin-top: 0.5em; margin-bottom: 0.5em;">Scientists are now using artificial intelligence in order to examine sections of bacterial DNA that have not been looked at before. This method is showing potential RNA interactions and previously unknown genetic systems which could alter our understanding of microbes. A new study presents Minerva, a genome language model, demonstrating that AI can assist researchers in identifying biological patterns that traditional techniques might fail to detect.</div><div style="margin-top: 0.5em; margin-bottom: 0.5em;">The study, which was made available as a preprint on bioRxiv on 23 September 2026, focuses on the non-coding sections of DNA that are still largely unknown. Although these areas do not produce proteins, they can contain important signals and instructions which have an effect on cell function. The research presents a novel approach to investigating how bacteria handle and control their genetic information.</div><div style="margin-top: 0.5em; margin-bottom: 0.5em;"><span style="font-weight: bold;">AI maps previously unexplored genomic regions</span></div><div style="margin-top: 0.5em; margin-bottom: 0.5em;">The team developed Minerva in order to identify possible interactions between different regions in microbial genomes; rather than depending on similarities with known sequences, Minerva predicts these relationships directly from the DNA by using patterns learned by a genome language model.</div><div style="margin-top: 0.5em; margin-bottom: 0.5em;">On 150 bacterial genomes, Minerva identified a large number of interactions that were not included in the existing annotations. The researchers stated that 84.3 per cent of the predicted intergenic base-pairing interactions were not present in the current annotations, which demonstrates that AI can be of help in generating new ideas in biology.</div><div style="margin-top: 0.5em; margin-bottom: 0.5em;"><span style="font-weight: bold;">Unusual RNA structures and viral genetic systems identified</span></div><div style="margin-top: 0.5em; margin-bottom: 0.5em;">The study also examined a bacterial RNA family known as TwoAYGGAY in Pseudomonas; the model anticipated longer RNA structures and identified associations with repeated DNA sequences, thus providing new insights into how these non-coding elements are organised and how they have evolved.</div><div style="margin-top: 0.5em; margin-bottom: 0.5em;">In a separate section of the study, the researchers examined reverse transcriptase systems associated with bacteriophages, which are viruses that infect bacteria. They identified RNA arrays that maintain their structure but have different sequences and were linked to Unknown Group 27 reverse transcriptases. The findings indicate that these RNAs could function as templates for the production of complementary DNA that is capable of forming hairpin shapes.</div><div style="margin-top: 0.5em; margin-bottom: 0.5em;">The researchers also observed that Minerva was able to detect patterns associated with protein-coding areas, even though it had not been trained to do so. This indicates that genome language models may pick up on biological signals that go beyond what they were intended to identify.</div><div style="margin-top: 0.5em; margin-bottom: 0.5em;"><span style="font-weight: bold;">Implications for future genomic research</span></div><div style="margin-top: 0.5em; margin-bottom: 0.5em;">The fact that artificial intelligence is becoming increasingly important in the field of microbial genomics is shown by the fact that models such as Minerva are able to predict interactions and identify patterns in areas which have not been extensively studied, thus helping researchers to decide what to study next and enabling them to gain a better understanding of biological systems that are still not well understood.</div><div style="margin-top: 0.5em; margin-bottom: 0.5em;">Yet the predictions do not reveal the exact function of each element identified. In order to verify which of the predicted interactions actually take place in living cells and the way in which they affect the microbes, experiments will be necessary. Although the study has undergone peer review, it does nonetheless offer a promising illustration of how machine learning can complement traditional genomics and assist scientists in moving from the identification of known genes to the exploration of the complex relationships that shape microbial life.</div><div style="margin-top: 0.5em; margin-bottom: 0.5em;">More at https://www.biorxiv.org/content/10.64898/2026.09.22.753630v2.full.pdf</div><div style="color: #000000; font-size: medium;">&nbsp;</div>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/3885/precision-medicine</guid>
	<pubDate>Sat, 24 Aug 2013 15:47:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/3885/precision-medicine</link>
	<title><![CDATA[Precision Medicine]]></title>
	<description><![CDATA[<p>Coupling established clinical&ndash;pathological indexes with state-of-the-art molecular profiling to create diagnostic, prognostic, and therapeutic strategies precisely tailored to each patient's requirements &mdash; hence the term &ldquo;Precision medicine&rdquo;&nbsp;</p>
<p>Source:<a href="http://www.nejm.org/doi/full/10.1056/NEJMp1114866">http://www.nejm.org/doi/full/10.1056/NEJMp1114866</a></p>
<p><strong>Another video on precision medicine</strong>:</p>
<p><a href="http://www.youtube.com/watch?v=Pi8W0yOXnzE">http://www.youtube.com/watch?v=Pi8W0yOXnzE</a></p>
<p>Precision Medicine basically intergrates bioinformatics, genomics , genetics, molecular biology and nanotechnology to deliver precise cure/diagnotics to a specific patient.</p>
<p>Examples:</p>
<ul>
<li><span>The drug imatinib (Gleevec) designed to inhibit an altered enzyme produced by a fused version of two genes found in chronic myelogenous leukemia.</span></li>
<li><span>The breast cancer drug trastuzumab (Herceptin) works only for women whose tumors have a particular genetic profile called HER-2 positive.</span></li>
</ul>
<p><span>E.g. source :</span></p>
<p><span><a href="http://www.bionews-tx.com/news/2013/08/15/how-the-impact-of-cancer-genomics-on-precision-medicine-is-revolutionizing-cancer-treatment/">http://www.bionews-tx.com/news/2013/08/15/how-the-impact-of-cancer-genomics-on-precision-medicine-is-revolutionizing-cancer-treatment/</a></span></p><p>Address of the bookmark: <a href="http://www.cbsnews.com/video/watch/?id=50149783n" rel="nofollow">http://www.cbsnews.com/video/watch/?id=50149783n</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/3031/following-the-scientific-literature-a-personal-practical-guide-for-young-computational-biologists</guid>
	<pubDate>Fri, 23 Aug 2013 07:18:51 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/3031/following-the-scientific-literature-a-personal-practical-guide-for-young-computational-biologists</link>
	<title><![CDATA[Following the scientific literature: A personal practical guide for young computational biologists]]></title>
	<description><![CDATA[<p><span>The goal of this guide is to describe&nbsp;</span><strong>why</strong><span>,&nbsp;</span><strong>when</strong><span>,&nbsp;</span><strong>where</strong><span>&nbsp;and&nbsp;</span><strong>how</strong><span>&nbsp;can you follow the most up-to-date science of interest and&nbsp;</span><strong>what</strong><span>&nbsp;papers/journals you should follow. The guide is biased towards the fields of genomics/systems biology.(from article)</span></p>
<p><span>Source:&nbsp;<strong><span>&nbsp;<a href="http://www.cs.tau.ac.il/~ulitskyi/">Igor Ulitsky</a>&nbsp;&amp;&nbsp;<a href="http://www.cs.tau.ac.il/~rshamir/">Ron Shamir</a></span></strong></span></p><p>Address of the bookmark: <a href="http://acgt.cs.tau.ac.il/guides/LiteratureGuide.htm" rel="nofollow">http://acgt.cs.tau.ac.il/guides/LiteratureGuide.htm</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/5402/key-bioinformatics-scientists</guid>
	<pubDate>Wed, 09 Oct 2013 13:37:07 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/5402/key-bioinformatics-scientists</link>
	<title><![CDATA[Key Bioinformatics Scientists]]></title>
	<description><![CDATA[<p>Address of the bookmark: <a href="http://www.iscb.org/iscb-leadership-a-staff-/officers-and-board-directors" rel="nofollow">http://www.iscb.org/iscb-leadership-a-staff-/officers-and-board-directors</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/9586/list-of-bioinformatics-companies-and-genomics-service-providers</guid>
	<pubDate>Wed, 02 Apr 2014 06:52:28 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/9586/list-of-bioinformatics-companies-and-genomics-service-providers</link>
	<title><![CDATA[List of bioinformatics companies and genomics service providers]]></title>
	<description><![CDATA[<p>Plz check out link for bioinformatics and genomics companies.&nbsp;</p><p>Address of the bookmark: <a href="http://grouthbio.com/Genome_Software_Service.php" rel="nofollow">http://grouthbio.com/Genome_Software_Service.php</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>

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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/17966/internship-program-for-bioinformatics-biotechnology-professionals-no-of-vacancy-2</guid>
  <pubDate>Wed, 08 Oct 2014 01:10:08 -0500</pubDate>
  <link></link>
  <title><![CDATA[Internship Program for Bioinformatics / Biotechnology Professionals (No. Of Vacancy: 2)]]></title>
  <description><![CDATA[
<p>ArrayGen is offering an Internship Program for Post graduate Bioinformatics / Biotechnology students and professionals. ArrayGen Technologies provide an excellent opportunity to gain research experience and explore if a scientific career is right for you. Currently we offer positions to outstanding students interested in Next Generation Sequencing (NGS) data analysis. Applications are accepted throughout the year. Accepted students will be listed on web with their schedules. Accepted students can attend our future workshops and trainings freely at the specified venue.</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34485/phyloxml-xml-for-evolutionary-biology-and-comparative-genomics</guid>
	<pubDate>Wed, 29 Nov 2017 10:04:48 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34485/phyloxml-xml-for-evolutionary-biology-and-comparative-genomics</link>
	<title><![CDATA[phyloXML:  XML for evolutionary biology and comparative genomics]]></title>
	<description><![CDATA[<p><a href="http://www.biomedcentral.com/1471-2105/10/356/">phyloXML</a><span>&nbsp;(</span><a href="http://www.phyloxml.org/examples_syntax/phyloxml_syntax_example_1.html">example</a><span>) is an&nbsp;</span><a href="http://en.wikipedia.org/wiki/XML">XML</a><span>&nbsp;language designed to describe phylogenetic trees (or networks) and associated data. PhyloXML provides elements for commonly used features, such as taxonomic information, gene names and identifiers, branch lengths, support values, and gene duplication and speciation events. Using these standardized elements allows interoperability between various applications and databases. Furthermore, both due to extensible nature of XML itself and the provision of &lt;property&gt; elements by phyloXML, extensibility as well as domain specific applications are ensured. The structure of phyloXML is described by&nbsp;</span><a href="http://en.wikipedia.org/wiki/XML_Schema_%28W3C%29">XML Schema Definition (XSD)</a><span>&nbsp;language.</span></p><p>Address of the bookmark: <a href="http://www.phyloxml.org/" rel="nofollow">http://www.phyloxml.org/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36267/pspairwise-sequentially-markovian-coalescent-psmc-model</guid>
	<pubDate>Thu, 19 Apr 2018 05:29:23 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36267/pspairwise-sequentially-markovian-coalescent-psmc-model</link>
	<title><![CDATA[PSPairwise Sequentially Markovian Coalescent (PSMC) model]]></title>
	<description><![CDATA[<p><span>Implementation of the Pairwise Sequentially Markovian Coalescent (PSMC) model</span></p><p>Address of the bookmark: <a href="https://github.com/lh3/psmc" rel="nofollow">https://github.com/lh3/psmc</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/38649/ngs-platforms-launched-by-bgi%E2%80%99s-mgi-tech</guid>
	<pubDate>Thu, 10 Jan 2019 04:42:06 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/38649/ngs-platforms-launched-by-bgi%E2%80%99s-mgi-tech</link>
	<title><![CDATA[NGS Platforms launched by BGI’s MGI Tech]]></title>
	<description><![CDATA[<p>MGI Tech Co., Ltd. (MGI), a subsidiary of BGI Group, is committed to enabling effective and affordable healthcare solutions for all. Based on its proprietary technology, MGI produces sequencing devices, equipment, consumables and reagents to support life science research, medicine and healthcare. MGI's multi-omics platforms include genetic sequencing, mass spectrometry and medical imaging. Providing real-time, comprehensive, life-long solutions, its mission&nbsp;is to&nbsp;develop and promote advanced life science tools for future healthcare.</p><p>MGI, a subsidiary of global genomics leader BGI Group, announced pricing and its first early access customer for the new ultra high-throughput sequencer, MGISEQ-T7, saying it has driven down sequencing cost to&nbsp;$5&nbsp;per gigabyte, with exceptionally high accuracy. Such innovations are helping more people to realize the benefits of genomic information.</p><p>In October, MGI launched the MGISEQ-T7, a highly flexible production-scale platform that is the most powerful sequencer to date. It can produce as many as 60 whole human genomes in one day. The instrument sells for&nbsp;$1 million.</p><p>The T7 enables simultaneous but independent operation of up to four flow cells, which means different applications such as single-cell RNA sequencing, whole exome sequencing and whole genome sequencing can be run in different flow cells at the same time. This helps to reduce costs, allowing MGI to offer the most competitive sequencing price in the market.</p><p><span>Powered by DNBseq&trade;, MGISEQ delivers quality data with accuracy for SNP and Indel calling rate of 99.9% and 99%, respectively, along with decreased duplication rate down to less than 2 percent, and almost zero Index mis-assignment rate.</span></p><p><span><span>SOURCE MGI</span></span></p><p>https://www.bgi.com/global/company/news/bgis-mgi-tech-launches-two-new-ngs-platforms/</p><p>http://en.mgitech.cn/</p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40834/nucleus-python-and-c-code-for-reading-and-writing-genomics-data</guid>
	<pubDate>Sun, 02 Feb 2020 08:14:19 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40834/nucleus-python-and-c-code-for-reading-and-writing-genomics-data</link>
	<title><![CDATA[Nucleus: Python and C++ code for reading and writing genomics data.]]></title>
	<description><![CDATA[<p>Nucleus is a library of Python and C++ code designed to make it easy to read, write and analyze data in common genomics file formats like SAM and VCF. In addition, Nucleus enables painless integration with the TensorFlow machine learning framework, as anywhere a genomics file is consumed or produced, a TensorFlow tfrecords file may be used instead.</p><p>Address of the bookmark: <a href="https://github.com/google/nucleus" rel="nofollow">https://github.com/google/nucleus</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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