<?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/44773?offset=280</link>
	<atom:link href="https://bioinformaticsonline.com/related/44773?offset=280" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	
<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/44639/the-sheppard-lab</guid>
  <pubDate>Fri, 09 Aug 2024 02:48:34 -0500</pubDate>
  <link></link>
  <title><![CDATA[The Sheppard Lab]]></title>
  <description><![CDATA[
<p>Ineos Oxford Institute of Antimicrobial Research – Department of Biology – University of Oxford</p>

<p>Our research centres on the use of genetics/genomics and phenotypic studies to address complex questions in the ecology, epidemiology and evolution of microbes. Our most recent interest focuses upon comparative genome analysis to describe the core and flexible genome of pathogenic bacteria (Campylobacter, Acinetobacter, Escherichia coli, Helicobacter, Staphylococcus and Streptococcus suis) and how this is related to population genetic structuring, the maintenance of species, and the evolution of host/niche adaptation and virulence.</p>

<p>More at https://sheppardlab.com/research/</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/45235/the-sweet-side-of-human-evolution-did-sugar-help-build-the-human-brain</guid>
	<pubDate>Mon, 17 Aug 2026 01:47:36 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/45235/the-sweet-side-of-human-evolution-did-sugar-help-build-the-human-brain</link>
	<title><![CDATA[The Sweet Side of Human Evolution: Did Sugar Help Build the Human Brain?]]></title>
	<description><![CDATA[<p>For a long time, people have focused on meat when talking about human evolution. The common idea is that our ancestors ate more animal foods, which gave them lots of energy and nutrients, helping their brains grow bigger and more demanding than those of other primates.</p><p>A new study in Science offers a different and surprisingly sweet perspective. Researchers Jennie Brand-Miller, Karen Hardy, David Raubenheimer, and Les Copeland suggest that sugars from fruits and honey, and later starch from cooked plants, may have been key in helping the human brain evolve.</p><p><strong>The brain&rsquo;s carbohydrate problem</strong></p><p>The human brain uses a lot of energy. Even though it is only about 2% of an adult&rsquo;s body weight, it takes up a large share of the body&rsquo;s resting energy, mainly using glucose as fuel. Over millions of years, as our ancestors&rsquo; brains grew from about 300 grams in Australopithecus afarensis to around 1,500 grams in modern humans, the need for easy-to-access glucose would have gone up a lot.</p><p>The researchers, therefore, asked a simple question: where did all that glucose come from?</p><p>Their analysis suggests that early hominins may have gotten much of their glucose from naturally sweet foods. Fruit and honey could give them easy access to sugars before they learned to digest cooked starch well. The study&rsquo;s models show that sugars may have made up a big part of their diet.</p><p>From fruit and honey to cooked starch</p><p>The story did not end with fruit.</p><p>The researchers suggest that humans shifted from eating sweet foods to eating cooked starchy foods. Once people learned to control fire and process food, cooking made starchy plants much easier to digest and turned them into a key source of glucose.</p><p>From this perspective, new ways of preparing food, like pounding, processing, and cooking, were not just about making meals softer or tastier. These changes may have given our ancestors access to much more carbohydrate energy.</p><p>This gives us a more detailed view of how the human diet evolved. Animal foods were clearly important, but carbohydrates may have been just as important for meeting the high energy needs of a growing brain.</p><p>A different way to think about the human diet</p><p>The study does not claim that our ancestors ate sugar the way we do today. There is a big difference between eating whole fruits or natural honey and eating the highly processed, refined sugars common now.</p><p>Instead, the research points to something bigger. Human evolution may have depended on our ability to find, process, and get energy from many different foods.</p><p>Seasonal fruit, honey, underground plant foods, and later cooked starches could have been important sources of glucose. The researchers think that changes in how available these foods were may have shaped how our ancestors searched for food and even affected human evolution.</p><p>What does this mean today?</p><p>The findings should not be interpreted as a license to eat more refined sugar. Instead, they challenge the simple idea that carbohydrates were less important than meat in human evolution. Our ancestors survived and evolved by exploiting different nutritional opportunities&mdash;and by developing technologies that made previously difficult foods more useful.</p><p>So perhaps the history of the human brain was not written by meat alone. ISo maybe the story of the human brain is not just about meat. It could also be about fruit, honey, roots, grains, and our unique ability to turn plants into energy. sweeter than we once imagined.<br /><br />Read more about it at&nbsp;https://www.science.org/doi/epdf/10.1126/science.aed8437</p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/45310/when-genes-leave-clues-reading-the-evolutionary-footprints-of-life</guid>
	<pubDate>Fri, 11 Sep 2026 13:47:07 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/45310/when-genes-leave-clues-reading-the-evolutionary-footprints-of-life</link>
	<title><![CDATA[When Genes Leave Clues: Reading the Evolutionary Footprints of Life]]></title>
	<description><![CDATA[<p>Imagine walking through a forest and finding two sets of footprints that always appear, disappear, and reappear together. You might wonder whether the animals are connected. In genomics, genes leave similar footprints across evolution. When two genes repeatedly occur together&mdash;or disappear together&mdash;across different species, their shared evolutionary history can provide clues about their function.</p><p>This idea, known as phylogenetic profiling, is the foundation of Profylo (https://github.com/MartinSchoenstein/Profylo), an open-source Python toolkit developed for comparing evolutionary profiles and discovering co-evolving genes. Profylo brings together seven profile-comparison methods and four approaches for identifying co-evolving gene clusters, making it easier to explore hidden functional relationships within genomes.</p><p>The study demonstrates that even a simple matrix of gene presence and absence can reveal meaningful biological patterns. By following these evolutionary footprints, researchers can generate new hypotheses about unknown genes, pathways, and molecular interactions. In a world where genomes are being sequenced faster than ever, tools like Profylo offer an exciting way to let evolution itself become a guide to understanding gene function.</p><p>More at&nbsp;https://link.springer.com/article/10.1007/s00239-025-10280-6&nbsp;</p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/35752/hejnol-group</guid>
  <pubDate>Thu, 22 Feb 2018 16:02:53 -0600</pubDate>
  <link></link>
  <title><![CDATA[Hejnol Group]]></title>
  <description><![CDATA[
<p>The group studies a broad range of animal taxa using morphological and molecular tools to unravel the evolution and development of animal organ systems.</p>

<p>To understand the evolution of the biodiversity seen on planet earth is one of the major goals in biology. How animals explored new habitats from only being confined to the marine environment and the how the forms diversified is still one of the most tremendous questions to be answered.</p>

<p>http://www.sars.no/research/HejnolGrp.php</p>
]]></description>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/38551/gupta-lab</guid>
  <pubDate>Sat, 29 Dec 2018 13:18:31 -0600</pubDate>
  <link></link>
  <title><![CDATA[Gupta Lab]]></title>
  <description><![CDATA[
<p>Work include (i) understanding the evolutionary relationships among different prokaryotic and eukaryotic organisms; (ii) Understanding the cellular functions of these lineage-specific signature proteins as well as lineage-specific conserved inserts and deletions in important housekeeping proteins by genetic and biochemical studies; (iii) Development of novel diagnostic methods (PCR based and immunological) for identification of different groups of organisms based upon these signature proteins and conserved indels; (iv) The use of these lineage-specific probes with predicitive ability to identify/explore the presence of different groups of organisms in metagenomic sequences from various environments.</p>

<p>https://fhs.mcmaster.ca/gupta-lab/index.html</p>
]]></description>
</item>
<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>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33586/genetic-mapper-svg-genetic-map-drawer</guid>
	<pubDate>Sun, 18 Jun 2017 14:11:10 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33586/genetic-mapper-svg-genetic-map-drawer</link>
	<title><![CDATA[Genetic-mapper: SVG Genetic Map Drawer]]></title>
	<description><![CDATA[<p><span>Genetic-mapper is a perl script able to draw publication-ready vectorial genetic maps.</span></p>
<p>Perl script for creating a publication-ready vectorial genetic/linkage map in Scalable Vector Graphics (SVG) format. The resulting file can either be submitted for publication and edited with any vectorial drawing software like&nbsp;<a href="https://inkscape.org/">Inkscape</a>&nbsp;and&nbsp;<a href="http://www.adobe.com/uk/products/illustrator.html">Abobe Illustrator(R)</a>.</p>
<p>The input file must be a text file with at least the marker name (ID), linkage group (LG) and the position (POS) separeted by tabulations. Additionally a logarithm of odds (LOD score) can be provided. Any extra parameter will be ignored.</p>
<pre><code>map.tsv

ID&lt;tab&gt;LG&lt;tab&gt;POS&lt;tab&gt;LOD
13519  12     0       0.250840894
2718   12     1.0     0.250840893
11040  12     1.6     0.252843341
...</code></pre>
<p>https://github.com/pseudogene/genetic-mapper</p><p>Address of the bookmark: <a href="https://github.com/pseudogene/genetic-mapper" rel="nofollow">https://github.com/pseudogene/genetic-mapper</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44387/creating-genetic-maps-from-gbs-data</guid>
	<pubDate>Fri, 08 Sep 2023 06:31:24 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44387/creating-genetic-maps-from-gbs-data</link>
	<title><![CDATA[Creating Genetic Maps from GBS data]]></title>
	<description><![CDATA[<p><span>Genetic map, as the name suggest is simply knowing the relative positions of specific sequences across the genome. There are various methods to generate them, but most popular method is to use a cross between the known parents and examining their progenies. These kinds of crosses to create specific group of individuals of known ancestry is called as mapping population. Many types of mapping population exist. Here we will use the data collected from a Recombinant Inbred Line (RIL) (through selfing) to create a genetic map.</span></p><p>Address of the bookmark: <a href="https://bioinformaticsworkbook.org/dataAnalysis/GenomeAssembly/GeneticMaps/creating-genetic-maps.html" rel="nofollow">https://bioinformaticsworkbook.org/dataAnalysis/GenomeAssembly/GeneticMaps/creating-genetic-maps.html</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41125/chromonomer-a-tool-set-for-repairing-and-enhancing-assembled-genomes-through-integration-of-genetic-maps-and-conserved-synteny</guid>
	<pubDate>Mon, 17 Feb 2020 05:38:46 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41125/chromonomer-a-tool-set-for-repairing-and-enhancing-assembled-genomes-through-integration-of-genetic-maps-and-conserved-synteny</link>
	<title><![CDATA[Chromonomer: a tool set for repairing and enhancing assembled genomes through integration of genetic maps and conserved synteny]]></title>
	<description><![CDATA[<p>Chromonomer is a program designed to integrate a genome assembly with a genetic map. Chromonomer tries very hard to identify and remove markers that are out of order in the genetic map, when considered against their local assembly order; and to identify scaffolds that have been incorrectly assembled according to the genetic map, and split those scaffolds.</p><p>Address of the bookmark: <a href="http://catchenlab.life.illinois.edu/chromonomer/" rel="nofollow">http://catchenlab.life.illinois.edu/chromonomer/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/4288/new-born-babies-get-ready-to-know-their-whole-genome-soon</guid>
	<pubDate>Thu, 05 Sep 2013 07:24:02 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/4288/new-born-babies-get-ready-to-know-their-whole-genome-soon</link>
	<title><![CDATA[New born babies get ready to know their whole genome soon!!!]]></title>
	<description><![CDATA[<p>USA launch a pilot projects to examine medical information of newborn baby, which are being funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) and the National Human Genome Research Institute (NHGRI), both parts of the National Institutes of Health.</p><p>Awards of $5 million to four grantees have been made in fiscal year 2013 under the Genomic Sequencing and Newborn Screening Disorders research program. The program will be funded at $25 million over five years, as funds are made available.</p><p>"Hundreds of US babies will be pioneers in genomic medicine through a&nbsp;US$25-million programme to sequence their genomes&nbsp;soon after they are born."</p><p><strong>Source</strong>:</p><p><a href="http://blogs.nature.com/news/2013/09/scientists-to-sequence-hundreds-of-newborns-genomes.html">http://blogs.nature.com/news/2013/09/scientists-to-sequence-hundreds-of-newborns-genomes.html</a></p><p><a href="http://www.genome.gov/27554919">http://www.genome.gov/27554919</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>

</channel>
</rss>