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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/43374?offset=110</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44902/hite-a-fast-and-accurate-dynamic-boundary-adjustment-approach-for-full-length-transposable-elements-detection-and-annotation-in-genome-assemblies</guid>
	<pubDate>Sat, 20 Sep 2025 09:34:04 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44902/hite-a-fast-and-accurate-dynamic-boundary-adjustment-approach-for-full-length-transposable-elements-detection-and-annotation-in-genome-assemblies</link>
	<title><![CDATA[HiTE: a fast and accurate dynamic boundary adjustment approach for full-length Transposable Elements detection and annotation in Genome Assemblies]]></title>
	<description><![CDATA[<p dir="auto"><code>HiTE</code>&nbsp;is a Python software that uses a dynamic boundary adjustment approach to detect and annotate full-length Transposable Elements in Genome Assemblies. In comparison to other tools, HiTE demonstrates superior performance in detecting a greater number of full-length TEs.</p>
<div dir="auto">
<h2 dir="auto">panHiTE</h2>
<a href="https://github.com/CSU-KangHu/HiTE#panhite"></a></div>
<p dir="auto">We have developed panHiTE, a comprehensive and accurate pipeline for TE detection in large-scale population genomes. It has been successfully applied to hundreds of plant population genomes, demonstrating its effectiveness and scalability.</p>
<p dir="auto">For detailed instructions, please refer to the&nbsp;<a href="https://github.com/CSU-KangHu/HiTE/wiki/panHiTE-tutorial">panHiTE tutorial</a>.</p><p>Address of the bookmark: <a href="https://github.com/CSU-KangHu/HiTE" rel="nofollow">https://github.com/CSU-KangHu/HiTE</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/45273/a-subway-map-for-the-genome</guid>
	<pubDate>Fri, 04 Sep 2026 04:14:14 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/45273/a-subway-map-for-the-genome</link>
	<title><![CDATA[A Subway Map for the Genome]]></title>
	<description><![CDATA[<p>Maya was looking at a genome graph on her computer. There were nodes, branches, and paths going in every direction.</p><p>&ldquo;It&rsquo;s like a subway map,&rdquo; she said, &ldquo;but for DNA.&rdquo;</p><p>That idea is exactly what Sequence Tube Map brings to life.</p><p>Instead of showing a genome as one long, confusing sequence, it displays different genomic paths like routes on a metro map. Where sequences are shared, the paths travel together. Where genetic variation occurs, they branch into different routes and may join again later.</p><p>This makes complex genome graphs much easier to explore.</p><p>Maya could now see how different haplotypes moved through the same genomic region and where they took different paths. She could even view sequencing reads mapped onto the graph, helping her understand what was happening in regions with genetic variation.</p><p>What once looked like a tangled network now looked like a journey.</p><p>Sequence Tube Map turns the complexity of graph genomes into a visual story&mdash;making it easier for scientists to see how DNA can take different routes.</p><p>Read more at&nbsp; https://github.com/vgteam/sequencetubemap&nbsp;</p><p>and</p><p>https://academic.oup.com/bioinformatics/article/35/24/5318/5542397</p><p>Demo at&nbsp;https://vgteam.github.io/sequenceTubeMap/</p>]]></description>
	<dc:creator>LEGE</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/45306/the-genes-that-travel-together-a-genomic-detective-story</guid>
	<pubDate>Fri, 11 Sep 2026 13:00:36 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/45306/the-genes-that-travel-together-a-genomic-detective-story</link>
	<title><![CDATA[The Genes That Travel Together: A Genomic Detective Story]]></title>
	<description><![CDATA[<p>Imagine looking through thousands of microbial genomes and discovering two genes that repeatedly appear together. The obvious conclusion is that they must somehow be connected&mdash;that perhaps they work together, participate in the same pathway, or depend on each other. But evolution has a way of leaving misleading clues. What if these two genes are found together simply because they were inherited from the same ancient ancestor? In that case, their apparent association may have little to do with their biological function; it may simply be a reflection of shared evolutionary history. This is the intriguing problem addressed by CORGIAS (https://github.com/ynishimuraLv/corgias), a computational framework developed to distinguish genuine gene associations from correlations created by common ancestry.</p><p>Instead of asking only whether two genes occur together across genomes, CORGIAS asks a deeper question: did these genes actually evolve together, repeatedly gaining or losing their presence in concert, or are they merely travelling together because of their inherited history? The framework introduces two complementary approaches, Ancestral State Adjustment (ASA) and the Simultaneous EVolution test (SEV), which incorporate evolutionary information into the search for gene associations. This distinction becomes increasingly important as genome sequencing continues to uncover enormous numbers of microbial genomes, many from organisms that have never been cultured and whose genes remain functionally mysterious. In this growing genomic landscape, simply finding a gene is no longer enough&mdash;we need clues about what that gene might be doing. CORGIAS approaches this problem almost like a genomic detective: rather than treating every association as evidence, it reconstructs the evolutionary story behind the association and asks whether the evidence survives that history. By doing so, it offers a way to separate coincidence from biological connection and potentially uncover functional relationships hidden within the vast microbial genomic landscape. The broader message is beautifully simple: genes do not evolve in isolation, and understanding what they do may require us to look beyond where they are today and reconstruct how they arrived there. Sometimes, the most important clue in a genome is not the gene itself, but the evolutionary journey it has taken.</p><p>More at&nbsp;https://academic.oup.com/nargab/article/7/4/lqaf182/8377910?</p>]]></description>
	<dc:creator>BioStar</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/file/view/5685/systems-biology-approach-to-model-inflammation-in-human-2pptx</guid>
	<pubDate>Thu, 17 Oct 2013 00:54:13 -0500</pubDate>
	<link>https://bioinformaticsonline.com/file/view/5685/systems-biology-approach-to-model-inflammation-in-human-2pptx</link>
	<title><![CDATA[SYSTEMS BIOLOGY APPROACH TO MODEL INFLAMMATION IN HUMAN (2).pptx]]></title>
	<description><![CDATA[]]></description>
	<dc:creator>ROSHNI</dc:creator>
	<enclosure url="https://bioinformaticsonline.com/file/download/5685" length="" type="application/vnd.openxmlformats-officedocument.presentationml.presentation" />
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/45231/the-giant-who-walked-across-ancient-taiwan</guid>
	<pubDate>Sat, 15 Aug 2026 14:48:45 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/45231/the-giant-who-walked-across-ancient-taiwan</link>
	<title><![CDATA[The Giant Who Walked Across Ancient Taiwan]]></title>
	<description><![CDATA[<p>Thousands of years ago, long before Taiwan became the island we know today, a large-bodied human walked across the landscape. We will probably never know what this individual looked like or where they travelled. But part of their story survived&mdash;in two ancient leg bones recovered from the seabed of the Penghu Channel.</p><p>The bones sat quietly in a fossil collection for years. One was part of a femur; the other, a tibia. They looked like the remains of an unusually large ancient human. But who did they belong to?</p><p>The answer came from an unexpected source: ancient proteins.</p><p>Using palaeoproteomic analysis, researchers found a molecular signature in both bones that matches the Denisovan lineage. The discovery identifies the two Penghu fossils as Denisovan and, more importantly, gives scientists their first substantial glimpse of the Denisovans' body size.</p><p>And the picture is striking.</p><p>One individual is estimated to have been about 1.8 metres tall and weighed around 83 kilograms. The other may have reached 1.9 metres and about 91 kilograms. Their leg bones rank among the largest known from Pleistocene Homo.</p><p>These were not small or fragile people.</p><p>They were powerful, heavily built humans moving through Ice Age eastern Asia.</p><p>But the bones tell an even more intriguing story. The femur has a pronounced ridge called a femoral pilaster&mdash;a feature particularly associated with modern human hunter-gatherers and increased mechanical strength during terrestrial movement. Why would a Denisovan, with an otherwise strongly archaic skeleton, possess this modern-looking feature?</p><p>Perhaps these Denisovans travelled extensively across the landscape. Perhaps their bodies were shaped by a demanding hunting lifestyle. Or perhaps, the researchers suggest, genetic exchange with early modern humans contributed to some of these features.</p><p>Their extraordinary size raises another mystery. A common expectation in human evolution is that populations living closer to the tropics tend to be smaller. Yet these Denisovans lived around 23&deg;N latitude and were exceptionally large. The researchers argue that cold climate alone cannot explain their size, pointing instead toward lifestyle and diet&mdash;including evidence that at least one Penghu individual relied heavily on meat.</p><p>So, piece by piece, the Denisovans are becoming less mysterious.</p><p>What was once a shadowy population known mainly from DNA is beginning to take physical form: large, robust, mobile humans who lived across eastern Asia and whose bodies carried a fascinating mixture of ancient and modern traits.</p><p>And perhaps the most remarkable part of this story is where it began&mdash;not in a spectacular cave discovery, but with two weathered bones lying among thousands of fossils.</p><p>The Denisovans may have left no written history. But their bones are beginning to tell one.</p><p>*Note: This research is currently a bioRxiv preprint and has not yet undergone peer review.&nbsp; Detail at&nbsp;https://www.biorxiv.org/content/10.64898/2026.08.07.743438v1.full.pdf</p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34746/trrust-v2-an-expanded-reference-database-of-human-and-mouse-transcriptional-regulatory-interactions</guid>
	<pubDate>Thu, 21 Dec 2017 17:01:44 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34746/trrust-v2-an-expanded-reference-database-of-human-and-mouse-transcriptional-regulatory-interactions</link>
	<title><![CDATA[TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions]]></title>
	<description><![CDATA[<p>TRRUST contains 8,444 and 6,552 TF-target regulatory relationships of 800 human TFs and 828 mouse TFs, respectively. They have been derived from 11,237 pubmed articles, which describe small-scale experimental studies of transcriptional regulations. To efficiently search for regulatory relationships from over 20 million pubmed articles, we used sentence-based text mining approach.</p>
<p>TRRUST database also provides information of mode of regulation (activation or repression). Currently 8,972 (59.8%) regulatory relationships are known for mode of regulation.</p>
<p>Search at :&nbsp;http://www.grnpedia.org/trrust/Network_search_form.php</p><p>Address of the bookmark: <a href="http://www.grnpedia.org/trrust/" rel="nofollow">http://www.grnpedia.org/trrust/</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/21685/uiar-short-term-trainingfinal-year-dissertation-project-in-life-sciencesbioinformaticsbiotech</guid>
  <pubDate>Mon, 16 Mar 2015 23:56:25 -0500</pubDate>
  <link></link>
  <title><![CDATA[UIAR Short-Term Training/Final Year Dissertation Project in Life Sciences/Bioinformatics/Biotech]]></title>
  <description><![CDATA[
<p>Short-term training/Final year dissertation project</p>

<p>Candidates desirous of doing a short-term training / final year dissertation project for MSc (Life Sciences/Bioinformatics/Biotechnology or any science discipline) at UIAR Biophysics and Bioinformatics department may please drop an email atanju@iiar.res.in along with their resume.</p>

<p>Selected candidates will be further intimated. There will be a fees charged for doing the project at UIAR. The projects will be experimental or computational or involve both.</p>

<p>The training scope will be in the following areas but not limited to:</p>

<p>Bioinformatics analysis, Docking and Virtual screening, Molecular Dynamics simulation, Cloning, expression and purification of proteins, Biophysical and Biochemical characterisation of proteins, Crystallization and Structural Studies.</p>

<p>Advertisement: www.iiar.res.in/?q=node/450</p>
]]></description>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/28889/project-scientist-at-national-agri-food-biotechnology-institute-nabi</guid>
  <pubDate>Thu, 25 Aug 2016 05:49:36 -0500</pubDate>
  <link></link>
  <title><![CDATA[Project Scientist at National Agri-Food Biotechnology Institute (NABI)]]></title>
  <description><![CDATA[
<p>Advt. No. NABI/8(18)/2012-PME-3<br />Project Scientist recruitment in National Agri-Food Biotechnology Institute (NABI)<br />Project Title : “Transfer and Evaluation of Indian Banana with Pro-Vitamin A (PVA) Constructs”<br />Essential qualifications:  Ph.D. thesis submitted/awarded in any branch of life/plant sciences. Desirable qualification: a) Excellent academic record with research experience in area relevant to plant metabolic engineering, molecular biology and bioinformatics supported with high quality publications. b) Knowledge and experience of Chromatography and Mass Spectrometry based technological analysis of samples. c) Knowledge and experience of in-silico analysis such as trascriptomics, proteomics and genomics. c) Relevant research publications in reputed international journals with high impact factors.<br />No. of Post : 01<br />Age: 35 years<br />Emoluments:  Rs.40,000/- per month.<br />How to apply<br />Walk-In-Interview on 29/08/2016 at National Agri-Food Biotechnology Institute, C-127, Industrial Area, Phase VIII, S.A.S. Nagar, Mohali-160 071 Email: siddharth@nabi.res.in</p>

<p>More at http://www.nabi.res.in/Vacancies/NABI/ResearchFellowships/JRFSRFRA/2016/NABI8(18)2012-PME-3/Advt.pdf</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44635/1000-genomes-chile-project</guid>
	<pubDate>Thu, 08 Aug 2024 01:24:13 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44635/1000-genomes-chile-project</link>
	<title><![CDATA[1000 Genomes Chile Project]]></title>
	<description><![CDATA[<p>Welcome to Chile Sequence to Chile: A Genomic Exploration Project for the Future Genomics, the science that deciphers the complexity of DNA, immerses us in the world of life at its most basic level. On this journey into the depths of genetic information, we find the 1000 Genomes Chile Project, an initiative that seeks to explore and understand the genetic wealth of our country.</p>
<p>Deciphering Life at the Molecular Level DNA sequencing is the key that opens the door to invaluable knowledge. By understanding the genes that make up Chilean species, we unravel the secrets of their evolution, their resistance and their adaptation to the environment. In a world where biodiversity faces constant threats, sequencing becomes crucial for the conservation and understanding of our natural heritage.</p>
<p>Involving Everyone: A Nationwide Effort The 1000 Genomes Chile Project is not just a task for scientists. It is a country-wide effort that seeks the participation of everyone: from citizens to the government to the private sector. We believe in the importance of sharing knowledge, involving society in the selection of species to sequence, in monitoring progress and in applying the results to preserve our environment.</p><p>Address of the bookmark: <a href="https://1000genomas.cl/" rel="nofollow">https://1000genomas.cl/</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42572/the-breeding-api-brapi-project</guid>
	<pubDate>Wed, 06 Jan 2021 19:51:17 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42572/the-breeding-api-brapi-project</link>
	<title><![CDATA[The Breeding API (BrAPI) project]]></title>
	<description><![CDATA[<p><span>The Breeding API (BrAPI) project is an effort to enable interoperability among plant breeding databases. BrAPI is a standardized RESTful web service API specification for communicating plant breeding data. This community driven standard is free to be used by anyone interested in plant breeding data management.</span></p><p>Address of the bookmark: <a href="https://brapi.org/" rel="nofollow">https://brapi.org/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
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