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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/45244?offset=80</link>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/38383/sidow-lab</guid>
  <pubDate>Fri, 07 Dec 2018 09:06:30 -0600</pubDate>
  <link></link>
  <title><![CDATA[Sidow Lab]]></title>
  <description><![CDATA[
<p>We study mechanisms of cancer evolution by using state-of-the-art genomic approaches at the bench and in analysis. Accurate genome reconstruction is our other major area of interest. We also collaborate on important questions for which our expertise in genomics and computation is relevant. Arend's biosketch highlights some of our past contributions.</p>

<p>http://www.sidowlab.org/</p>
]]></description>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42794/tmrca-calculator</guid>
	<pubDate>Wed, 03 Feb 2021 05:07:30 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42794/tmrca-calculator</link>
	<title><![CDATA[TMRCA Calculator]]></title>
	<description><![CDATA[<p><span>This program calculates the probability that two people have a certain number of generations between them, based on the standard&nbsp;</span><em>infinite alleles</em><span>&nbsp;formula of Walsh. It calculates both the probability of being at an exact number of generations back to the Most Recent Common Ancestor (MRCA) of a certain pair of people and the cumulative probability that the actual number of generations is less than a certain value. Note that the convention using generations is changed from an earlier version of this calculator which used "transmission events". It can list both result types in a table or graph. In either case the horizontal axis stops at the point where the cumulative probability reaches 95% or 10 generations, whichever is longer, or an absolute max of 50,000. Beyond 90% the calculation becomes inaccurate.</span></p>
<p>https://clandonaldusa.org/index.php/tmrca-calculator</p><p>Address of the bookmark: <a href="https://clandonaldusa.org/index.php/tmrca-calculator" rel="nofollow">https://clandonaldusa.org/index.php/tmrca-calculator</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43806/genomicus-genome-browser-that-enables-users-to-navigate-in-genomes-in-several-dimensions</guid>
	<pubDate>Mon, 28 Feb 2022 23:27:37 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43806/genomicus-genome-browser-that-enables-users-to-navigate-in-genomes-in-several-dimensions</link>
	<title><![CDATA[Genomicus: genome browser that enables users to navigate in genomes in several dimensions]]></title>
	<description><![CDATA[<p>Genomicus is a genome browser that enables users to navigate in genomes in several dimensions: linearly along chromosome axes, transversaly across different species, and chronologicaly along evolutionary time.</p>
<p>Once a query gene has been entered, it is displayed in its genomic context in parallel to the genomic context of all its orthologous and paralogous copies in all the other sequenced metazoan genomes. Moreover, Genomicus stores and displays the predicted ancestral genome structure in all the ancestral species within the phylogenetic range of interest.</p>
<p>All the data on extant species displayed in this browser are from&nbsp;<a href="http://www.ensembl.org/">Ensembl</a>.</p>
<p><br><strong>Summary statistics of Genomicus version 105.01:</strong><span>&nbsp;(view species tree in&nbsp;</span><a href="https://www.genomicus.bio.ens.psl.eu/genomicus-105.01/data/SpeciesTree.pdf">pdf</a><span>&nbsp;or&nbsp;</span><a href="https://www.genomicus.bio.ens.psl.eu/genomicus-105.01/data/SpeciesTree.nwk">newick</a><span>)</span><br><br></p>
<table id="introstats">
<tbody>
<tr><th>Number of extant species</th>
<td>200</td>
</tr>
<tr><th>Number of extant genes</th>
<td>4303993</td>
</tr>
<tr><th>&nbsp;</th></tr>
<tr><th>Number of ancestral species</th>
<td>196</td>
</tr>
<tr><th>Number of ancestral genes</th>
<td>4624213</td>
</tr>
<tr><th>Number of ancestral synteny blocks</th>
<td>83342<br><br></td>
</tr>
</tbody>
</table><p>Address of the bookmark: <a href="https://www.genomicus.bio.ens.psl.eu/genomicus-105.01/cgi-bin/search.pl" rel="nofollow">https://www.genomicus.bio.ens.psl.eu/genomicus-105.01/cgi-bin/search.pl</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44773/genetic-basis-of-tail-loss-evolution</guid>
	<pubDate>Tue, 04 Mar 2025 12:12:36 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44773/genetic-basis-of-tail-loss-evolution</link>
	<title><![CDATA[Genetic basis of tail-loss evolution]]></title>
	<description><![CDATA[<p>The paper <em>"On the genetic basis of tail-loss evolution in humans and apes (https://www.nature.com/articles/s41586-024-07095-8)"</em>, published in <em>Nature</em>, investigates the genetic mechanisms that led to the loss of tails in humans and apes. The study suggests that a specific genetic mutation, involving the insertion of an <em>Alu</em> element (a type of transposable DNA sequence), played a critical role in the evolutionary transition from tailed primates to tailless hominoids.</p><h3><strong>Key Findings of the Study:</strong></h3><ol>
<li>
<p><strong>Alu Insertion and Tail Loss:</strong><br /> The researchers discovered an <em>Alu</em>-mediated genetic change in a common ancestor of modern apes and humans. This change disrupted the normal function of a gene involved in tail development, leading to the suppression of tail formation.</p>
</li>
<li>
<p><strong>Gene Disruption Mechanism:</strong><br /> The <em>Alu</em> insertion was found within a regulatory region of the <em>TBXT</em> gene (also known as <em>T</em> or <em>Brachyury</em>), which is crucial for tail development in vertebrates. This insertion likely altered the gene's expression patterns, leading to tail reduction over evolutionary time.</p>
</li>
<li>
<p><strong>Functional Evidence from Model Organisms:</strong><br /> To test their hypothesis, the researchers introduced similar genetic modifications in mice. The modified mice exhibited shortened or absent tails, supporting the idea that the identified mutation played a role in tail loss in hominoids.</p>
</li>
<li>
<p><strong>Evolutionary Implications:</strong><br /> The findings suggest that small, random genomic changes&mdash;such as transposable element insertions&mdash;can have profound effects on body morphology. This study provides evidence that mobile DNA elements (like <em>Alu</em>) can drive major evolutionary transitions.</p>
</li>
<li>
<p><strong>Relevance to Human Evolution:</strong><br /> Understanding the genetic basis of tail loss helps in reconstructing the evolutionary history of hominins (the lineage that includes humans and our extinct relatives). It also sheds light on how genetic variations contribute to anatomical diversity among primates.</p>
</li>
</ol><h3><strong>Significance of the Study:</strong></h3><p>This research highlights the role of transposable elements in shaping evolutionary traits and provides a concrete genetic explanation for a defining characteristic of humans and great apes. It also demonstrates how mutations in regulatory regions of developmental genes can lead to significant anatomical changes.</p>]]></description>
	<dc:creator>LEGE</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/bookmarks/view/43374/reference-sequence-resource</guid>
	<pubDate>Wed, 15 Sep 2021 21:15:22 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43374/reference-sequence-resource</link>
	<title><![CDATA[Reference Sequence Resource!]]></title>
	<description><![CDATA[<p><span>The ENCODE project uses Reference Genomes from&nbsp;</span><a href="http://www.ncbi.nlm.nih.gov/genome/browse/reference/">NCBI</a><span>&nbsp;or&nbsp;</span><a href="http://hgdownload.cse.ucsc.edu/downloads.html">UCSC</a><span>&nbsp;to provide a consistent framework for mapping high-throughput sequencing data.&nbsp;In general, ENCODE data are mapped consistently to 2 human (GRCH38, hg19) and 2 mouse (mm9/mm10) genomes for historical comparability.&nbsp;</span><em>Drosophia melanogaster</em><span>&nbsp;experiments are mapped to either dm3 or dm6 and&nbsp;</span><em>Caenorhabdilis elegans&nbsp;</em><span>experiments are mapped to ce10 or ce11.&nbsp;T</span></p><p>Address of the bookmark: <a href="https://www.encodeproject.org/data-standards/reference-sequences/" rel="nofollow">https://www.encodeproject.org/data-standards/reference-sequences/</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/45289/the-atlas-of-nine-billion-possibilities</guid>
	<pubDate>Wed, 09 Sep 2026 02:07:58 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/45289/the-atlas-of-nine-billion-possibilities</link>
	<title><![CDATA[The Atlas of Nine Billion Possibilities]]></title>
	<description><![CDATA[<p>Imagine a book that holds all the instructions for building a human, made up of billions of letters. What if you changed just one letter? Maybe nothing would happen. Or that tiny change could affect how a gene works, quietly shaping a cell&rsquo;s biology or even helping cause disease.</p><p>Scientists face a big challenge with the human genome. They can read its letters, but understanding their roles is much harder. Only about 2 percent of the genome codes for proteins. The rest acts like a huge control panel, deciding when and where genes turn on. With about 9 billion possible single-letter changes, testing them all in a lab just isn&rsquo;t possible.</p><p>So, Google DeepMind asked a new question: what if we could predict what those changes might do?</p><p>This question led to the AlphaGenome Atlas (https://deepmind.google.com/science/alphagenome/atlas?), a detailed map of nearly every possible single-letter change in the human genome. Instead of checking each change one by one, researchers can use the Atlas to spot the ones most likely to matter. The AlphaGenome Variant Impact (AVI) score works like a trail marker, pointing scientists toward the changes worth a closer look.</p><p>This is where the Atlas gets especially useful. Much of the genome lies outside the protein-coding regions, where DNA acts as a switch or controller for genes. AlphaGenome lets researchers explore these areas and see how small changes could affect gene activity.</p><p>An atlas isn't the destination; it's a guide.</p><p>The AlphaGenome Atlas doesn&rsquo;t replace experiments or solve every mystery. Instead, it helps scientists decide where to begin. From billions of possibilities, it turns the vast genetic landscape into something researchers can start to explore.</p><p>There are nine billion possibilities, a vast map, and maybe among them therWith nine billion possibilities and a huge map to explore, there may be clues hidden here to some of medicine&rsquo;s toughest mysteries.</p><p>More at&nbsp;https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/alphagenome-atlas-a-predictive-map-of-every-possible-dna-letter-change-in-the-human-genome/alphagenome-atlas.pdf</p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>
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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>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/40416/5700-year-old-human-genome</guid>
	<pubDate>Thu, 19 Dec 2019 11:22:18 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/40416/5700-year-old-human-genome</link>
	<title><![CDATA[5700 year-old human genome !]]></title>
	<description><![CDATA[<p>A Landmark in genomics, scientists have done something that hasn't been done ever.</p><p>Scientists have reconstructed the genome of an ancient human who lived nearly 5,700 years ago in Southern Denmark from the birch pitch- an ancient tar-like substance.</p><p>By sequencing the sample, researchers not only discovered the ancient human DNA but also microbial DNA reflecting the oral microbiome of the person who chewed the pitch, along with plant and animal DNA that could be the recent<span> meal she might have consumed.</span></p><p><span style="font-size: 12.8px;">The DNA sample is comparable in quality to well-preserved teeth and skull bones. The DNA suggests that the chewer was a female, most likely with dark skin, dark brown hair and blue eyes.</span></p><div><p><a href="https://www.nature.com/articles/s41467-019-13549-9?fbclid=IwAR0FPk0Cl25YjHVdcfK4tqFhCsPx00SCSMUwlU6zNwMDNrKi1QynwtJKDfE" target="_blank">https://www.nature.com/articles/s41467-019-13549-9</a></p><p><img src="https://i.kinja-img.com/gawker-media/image/upload/c_scale,f_auto,fl_progressive,q_80,w_800/ykcvh491evenyvlrjb9r.jpg" width="800" height="450" alt="image" style="border: 0px;"></p><p>Artistic reconstruction. (Tom Bj&ouml;rklund)</p><p>More at&nbsp;<a href="https://gizmodo.com/scientists-reconstruct-lola-after-finding-her-dna-in-1840481633">https://gizmodo.com/scientists-reconstruct-lola-after-finding-her-dna-in-1840481633</a></p></div>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/file/view/989/bioinformatics-approach-to-boar-taint</guid>
	<pubDate>Wed, 17 Jul 2013 15:50:37 -0500</pubDate>
	<link>https://bioinformaticsonline.com/file/view/989/bioinformatics-approach-to-boar-taint</link>
	<title><![CDATA[Bioinformatics approach to Boar Taint]]></title>
	<description><![CDATA[<p><span>Meat products obtained from intact male pigs often produce offensive smell or odour which is recognized as a complex genetic trait called boar taint.Androstenone and Skatole&nbsp;in the fat primarily cause boar taint. Metabolism of androstenone and sex steroids share a common pathway which makes removal of boar taint a very challenging task. Castration is a traditional solution to remove boar taint but it also results in bad quality of meat due to low level of steroids which is objectionable to many consumers. Detected functional variant(s) underlying boar taint compounds can be used as genetic markers in selection of male pigs with reduced boar taint levels. Resequencing of a total of 47 samples belong to Norwegian Landrace (NL) and Duroc (D) pigs with varied boar taint levels were done in Illumina HiSeq2000 to &gt;10X average coverage. Short reads generated from these samples mapped to&nbsp;<em>Sus Scrofa</em>&nbsp;version 10.2 reference assembly using Bowtie2. Alignment file then used for calling SNPs and InDels inside previousy identified QTL regions on SSC5,13, and 7 with the aid of FreeBayes , a variant caller tool. A final list of SNPs was prepared after filtering SNPs on the basis of SNP quality, coverage of SNP allele, functional and structural annotation, and repeats, etc. Selected SNPs will be genotyped in sample population for validation and then used for constructing SNPs haplotypes in close linkage disequilibrium with QTLs and fine mapping of QTLs through association mapping of genotyped SNPs.</span><span>&nbsp;</span></p><p><span>&nbsp;</span></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
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