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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/26322?offset=1300</link>
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	<description><![CDATA[]]></description>
	
	
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  <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>
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	<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>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/view/1906</guid>
	<pubDate>Sun, 11 Aug 2013 11:13:58 -0500</pubDate>
	<link>https://bioinformaticsonline.com/view/1906</link>
	<title><![CDATA[Compressive Genomics]]></title>
	<description><![CDATA[<p>The key to finding a solution is to notice that most&nbsp;<a href="http://www.i-programmer.info/news/181-algorithms/4537-a-new-dna-sequence-search-compressive-genomics.html">genomic</a>sequences differ by very little. It may well be that the number of complete genome sequences being stored is increasing rapidly, but the actual amount of new data is very small. In other words, a single DNA sequence isn't particularly compressible but a set of sequences shares so much in common that the redundancy can be used to store them in a much smaller storage space. (Source:e-article from&nbsp;Alex Armstrong)</p><p><a href="http://www.i-programmer.info/news/181-algorithms/4537-a-new-dna-sequence-search-compressive-genomics.html">http://www.i-programmer.info/news/181-algorithms/4537-a-new-dna-sequence-search-compressive-genomics.html</a></p><p><a href="http://en.wikipedia.org/wiki/Compression_of_Genomic_Re-Sequencing_Data">http://en.wikipedia.org/wiki/Compression_of_Genomic_Re-Sequencing_Data</a></p><p><a href="http://www.nature.com/nbt/journal/v30/n7/full/nbt.2241.html">http://www.nature.com/nbt/journal/v30/n7/full/nbt.2241.html</a></p><p><a href="http://bioinformatics.oxfordjournals.org/content/29/13/i283.full">http://bioinformatics.oxfordjournals.org/content/29/13/i283.full</a></p><p><a href="http://groups.csail.mit.edu/cb/cast/">http://groups.csail.mit.edu/cb/cast/</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/26290/webinar-on-streamlining-large-scale-analysis-using-the-strand-ngs-pipeline-manager-on-24-feb-2016</guid>
	<pubDate>Fri, 05 Feb 2016 06:43:28 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/26290/webinar-on-streamlining-large-scale-analysis-using-the-strand-ngs-pipeline-manager-on-24-feb-2016</link>
	<title><![CDATA[Webinar on Streamlining large scale analysis using the Strand NGS Pipeline Manager on 24 Feb 2016]]></title>
	<description><![CDATA[<p><a href="http://www.strand-ngs.com/webinar_registration" title="webinar"><strong>Live Webinar on Streamlining large scale NGS data analysis using the Strand NGS Pipeline Manager on 24 Feb 2016</strong></a></p><p><strong>Abstract:</strong> Strand NGS includes comprehensive workflows for DNA-Seq, RNA-Seq, Small RNA-Seq, ChIP-Seq, MeDIP-Seq, and Methyl-Seq analysis. Each workflow includes a quality assessment and filter section, followed by a workflow-specific analysis section. The pipeline functionality in Strand NGS allows users to execute a sequence of analysis steps with specific parameters - all without any manual intervention. This simplifies the analysis in large scale sequencing projects where every sample needs to be processed identically.</p><p>In this webinar we will discuss the pre-packaged pipelines present in Strand NGS. The packaged pipelines have well-chosen default parameters and are suitable for users analyzing data for the first time in the tool. We will also show how advanced users can customize pipelines and share them with other Strand NGS users. Finally, we will show a brief glimpse of an elaborate pipeline that aligns reads, filters poor-quality matches, computes coverage metrics, identifies variants, checks for sample cross-contamination, and emails quality reports - all from within Strand NGS.</p><p><strong>Speaker:</strong> Dr. Vamsi Veeramachaneni, Vice President - Bioinformatics, Strand Life Sciences</p><p><strong>Details:</strong> Session 1: 2:30 PM IST, Session 2 : 10:30 PM IST<br /><strong>Register here:</strong> http://www.strand-ngs.com/webinar_registration</p><h3>&nbsp;</h3>]]></description>
	<dc:creator>Yeshodari</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/34912/list-of-cancer-genomics-research-web-resources</guid>
	<pubDate>Wed, 27 Dec 2017 20:33:09 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/34912/list-of-cancer-genomics-research-web-resources</link>
	<title><![CDATA[List of cancer genomics research web resources !]]></title>
	<description><![CDATA[<p>Major web resources for cancer genomics research</p><p>CGHub <br />https://cghub.ucsc.edu/ <br />Comprehensive data repository; huge data size</p><p>EGA <br />https://www.ebi.ac.uk/ega/ <br />Comprehensive data repository; huge data size</p><p>COSMIC <br />http://cancer.sanger.ac.uk <br />Largest somatic mutation database; genome sequencing paper curation</p><p>CPRG <br />http://www.broadinstitute.org/software/cprg <br />Interface for cancer program resources</p><p>GDAC <br />http://gdac.broadinstitute.org/ <br />Data analysis; automatic pipelines; user-friendly reports</p><p>SNP500Cancer <br />http://snp500cancer.nci.nih.gov <br />Sequence and genotype verification of SNPs</p><p>canEvolve <br />www.canevolve.org/ <br />Comprehensive analysis of tumor profile; Data from 90 studies involving more than 10,000 patients</p><p>MethyCancer <br />http://methycancer.psych.ac.cn <br />Relationship among DNA methylation, gene expression and cancer</p><p>SomamiR <br />http://compbio.uthsc.edu/SomamiR/ <br />Correlation between somatic mutation and microRNA; genome-wide displaying</p><p>cBioPortal <br />http://www.cbioportal.org/public-portal/ <br />Graphical summaries; gene alteration; processed data; visualization</p><p>UCSC Cancer Genomics Browser <br />https://genome-cancer.soe.ucsc.edu/ <br />Clinical information; gene expression; copy number variation; visualization</p><p>CGWB <br />https://cgwb.nci.nih.gov/ <br />Visualization; gene mutation and variation; automated analysis pipeline</p><p>GDSC <br />http://www.cancerrxgene.org <br />Drug sensitivity information; drug response information</p><p>canSAR <br />https://cansar.icr.ac.uk/ <br />Multidisciplinary information; drug discovery</p><p>NONCODE <br />http://www.noncode.org/ ncRNAs; <br />lncRNAs; up-to-date and comprehensive resource</p>]]></description>
	<dc:creator>biogeek</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38577/genoviz-visualization-software-for-genomics</guid>
	<pubDate>Wed, 02 Jan 2019 04:07:57 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38577/genoviz-visualization-software-for-genomics</link>
	<title><![CDATA[GenoViz: Visualization software for genomics]]></title>
	<description><![CDATA[<p><span>GenoViz provides software applications and re-usable components for data visualization and data sharing in genomics. Our flagship product is Integrated Genome Browser (IGB).</span><br><br><span>For more information about IGB, visit&nbsp;</span><a href="http://bioviz.org/" target="_blank">http://bioviz.org<span></span></a><span>.</span><br><br><span>Source code for the project was hosted here for many years. In 2014, we moved to a new git repository at&nbsp;</span><a href="http://www.bitbucket.org/lorainelab/integrated-genome-browser" target="_blank">http://www.bitbucket.org/lorainelab/integrated-genome-browser<span></span></a><span>. We are still using SourceForge to distribute new releases of IGB as compiled code (igb.zip) you can use to run IGB on your computer.&nbsp;</span><br><br><span>If you have questions, feel free to get in touch. Contact project head Ann Loraine (</span><a href="mailto:aloraine@uncc.edu" target="_blank">aloraine@uncc.edu<span></span></a><span>) or lead developer David Norris (</span><a href="mailto:dcnorris@uncc.edu" target="_blank">dcnorris@uncc.edu<span></span></a><span>&gt;).</span></p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/genoviz/" rel="nofollow">https://sourceforge.net/projects/genoviz/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40476/libsdyogen-libibrary-for-comparative-genomics</guid>
	<pubDate>Wed, 25 Dec 2019 01:32:39 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40476/libsdyogen-libibrary-for-comparative-genomics</link>
	<title><![CDATA[LibsDyogen: Libibrary for comparative genomics]]></title>
	<description><![CDATA[<p>Library of usual classes and functions written in python and used in the Dyogen team for comparative genomics applications.</p>
<p>Collaborative python library used in the<span>&nbsp;</span><a href="http://www.ibens.ens.fr/?rubrique43&amp;lang=fr">DYOGEN team</a>for studying the evolution of gene order in vertebrates.</p>
<p><a href="http://www.ibens.ens.fr/?rubrique43&amp;lang=fr">http://www.ibens.ens.fr/?rubrique43&amp;lang=fr</a></p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="https://github.com/DyogenIBENS/LibsDyogen" rel="nofollow">https://github.com/DyogenIBENS/LibsDyogen</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42570/breeding-insight</guid>
	<pubDate>Wed, 06 Jan 2021 19:49:21 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42570/breeding-insight</link>
	<title><![CDATA[Breeding Insight]]></title>
	<description><![CDATA[<p><span><span>Breeding Insight&nbsp;at Cornell University will leverage recent improvements in genomics and open source informatics components, and in&nbsp;partnership with small breeding programs, will enable these programs to harness&nbsp;&nbsp;powerful digital tools to accelerate their genetic gains</span></span></p>
<p><span>Breeding Insight is funded by&nbsp;the&nbsp;</span><span><a href="https://www.ars.usda.gov/about-ars/" target="_blank">U.S. Department of Agriculture (USDA) Agricultural Research Service (ARS)</a></span><span>&nbsp;through Cornell University. The USDA ARS delivers scientific solutions to national and global agricultural challenges. As a global leader&nbsp;in agricultural discovery through scientific excellence, ARS is committed to delivering cutting-edge, scientific tools and innovative solutions for American farmers, producers, industry, and communities to support the nourishment and well-being of all people; sustaining our nation&rsquo;s agroecosystems and natural resources; and ensuring the economic competitiveness and excellence of our agriculture.</span></p><p>Address of the bookmark: <a href="https://www.breedinginsight.org/" rel="nofollow">https://www.breedinginsight.org/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/45278/the-day-we-began-reading-the-dna-of-the-world</guid>
	<pubDate>Sat, 05 Sep 2026 01:43:21 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/45278/the-day-we-began-reading-the-dna-of-the-world</link>
	<title><![CDATA[The Day We Began Reading the DNA of the World]]></title>
	<description><![CDATA[<div>Picture waking up and knowing that in one lab, a scientist is reading the DNA of a whale. In another, a team is sequencing a rare plant. Meanwhile, researchers in India are decoding the genomes of different human populations.</div><div>&nbsp;</div><div>Many organisms. Many countries. All share one remarkable goal: to understand the genetic story of life.</div><div>DNA is nature's instruction manual, written with just four letters: A, T, C, and G. For years, scientists could only read small sections of this huge book. Now, new sequencing technology lets us read entire genomes like never before.</div><div>&nbsp;</div><div>One of the most ambitious projects is the Earth BioGenome Project (EBP). Its goal is to create reference genomes for about 1.5 million known eukaryotic species. This project links genome research across continents, species, and scientific fields.</div><div>&nbsp;</div><div>But this story goes beyond just animals and plants.</div><div>&nbsp;</div><div><strong>A New Chapter in Human Genomics</strong></div><div>&nbsp;</div><div>Large human genome projects are changing how we understand our own genetic diversity.</div><div>India's GenomeIndia project has sequenced thousands of people from different populations. This work is uncovering genetic variation that global databases have often missed.</div><div>&nbsp;</div><div>Similar population-scale effSimilar large-scale projects are happening worldwide. The All of Us Research Program in the United States, Europe's 1+ Million Genomes initiative, South Korea's national genomic data program, and projects in Saudi Arabia, Australia, and Singapore are all building huge genomic resources.collect DNA.</div><div>&nbsp;</div><div>The real goal is to link genomes with health, disease, and other biological information. This creates a base for more accurate research and, in time, more personalized medicine.</div><div>&nbsp;</div><div><strong>A Race Against Time</strong></div><div>&nbsp;</div><div>There is also an important twist.</div><div>&nbsp;</div><div>We are sequencing Earth's biodiversity even as many species face growing environmental threats.</div><div>A genome alone cannot save a species, but it does keep important information about its biology, evolution, and genetic diversity. This knowledge helps scientists understand risks, guide conservation, and find traits that could help agriculture or medicine.</div><div>&nbsp;</div><div>Projects like the Darwin Tree of Life, which studies species in Britain and Ireland, and the African BioGenome Project, which is growing genomics work across Africa, are key parts of this worldwide effort.</div><div>&nbsp;</div><div>Projects to Watch</div><ol>
<li>Earth BioGenome Project (EBP) - A global effort to sequence and annotate roughly 1.5 million known eukaryotic species and create a comprehensive reference library of Earth's biodiversity.</li>
<li>Vertebrate Genomes Project (VGP) - Focuses on producing high-quality reference genomes for vertebrate species, providing a major foundation for the wider Tree of Life.</li>
<li>Darwin Tree of Life - A UK and Ireland initiative aiming to sequence approximately 70,000 species, creating a detailed genomic map of regional biodiversity.</li>
<li>African BioGenome Project (AfricaBP) - A pan-African initiative focused on sequencing Africa's biodiversity while strengthening genomics and bioinformatics capacity across the continent.</li>
<li>European Reference Genome Atlas (ERGA) - A European effort to generate high-quality reference genomes for Europe's biodiversity and connect national sequencing efforts.</li>
<li>Canada BioGenome Project - Building reference genomes for Canadian biodiversity, supporting conservation, research and the study of ecosystems.</li>
<li>California Conservation Genomics Project - Uses genomics to understand and protect California's biodiversity and help inform conservation decisions.</li>
<li>Bat1K - An ambitious global effort to sequence the genomes of all known bat species, helping scientists study their evolution, longevity, immunity and unique biology.</li>
<li>Bird 10,000 (B10K) - A global project aiming to create genome resources covering the world's bird diversity and reconstruct avian evolutionary history.</li>
<li>Global Invertebrate Genomics Alliance (GIGA) - Builds genomic resources for the enormous and genetically diverse world of invertebrates.</li>
<li>GenomeIndia - India's national human-genome initiative, designed to capture the country's extraordinary population diversity and create a reference resource for Indian genomics.</li>
<li>All of Us Research Program - A US national research program combining genomic information with health and lifestyle data at population scale.</li>
<li>1+ Million Genomes / Genome of Europe - A European effort to enable secure, cross-border use of genomic and health data and develop genome-scale resources involving more than one million people.</li>
<li>Korea National Integrated Bio Big Data Project - South Korea's large-scale national effort combining genomic and health information from a very large population cohort.</li>
<li>Saudi Genome Program - A national genomics initiative focused on genetic diseases, population genomics and precision medicine in Saudi Arabia.</li>
<li>Australian Genomics / Genomics Health Futures Mission - Australia's growing investment in genomic medicine, rare disease research and population-scale health genomics.</li>
</ol><div><strong>The Library of Life</strong></div><div>&nbsp;</div><div>The fuThe future of genomics is not just about sequencing one genome at a time. Now, it is about creating a worldwide network of genomes&mdash;human and non-human, individual and species&mdash;linked by powerful databases and advanced computational tools.ine a library without shelves.</div><div>&nbsp;</div><div>In this library, the books are genomes. The pages are made of DNA. Scientists and machines are the readers. And this library is still being written. Each genome sequenced today adds a new page to the story of life and helps us better understand our place in it.</div>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44661/lovis4u-locus-visualisation-tool-for-comparative-genomics</guid>
	<pubDate>Tue, 17 Sep 2024 02:30:57 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44661/lovis4u-locus-visualisation-tool-for-comparative-genomics</link>
	<title><![CDATA[LoVis4u: Locus Visualisation tool for comparative genomics]]></title>
	<description><![CDATA[<p dir="auto"><a href="https://github.com/art-egorov/lovis4u/blob/main/docs/img/lovis4u_logo.png" target="_blank"><img src="https://github.com/art-egorov/lovis4u/raw/main/docs/img/lovis4u_logo.png" alt="image" width="300" style="border: 0px; border: 0px;"></a></p>
<div dir="auto">
<h2 dir="auto">Description</h2>
<a href="https://github.com/art-egorov/lovis4u#description"></a></div>
<p dir="auto"><span>LoVis4u</span>&nbsp;is a bioinformatics tool for&nbsp;<span>Lo</span>ci&nbsp;<span>Vis</span>ualisation.</p>
<p dir="auto"><span>LoVis4u, a command-line tool and Python API designed for highly customizable and fast visualisation of multiple genomic loci. LoVis4u generates vector images in PDF format based on annotation data from GenBank or GFF files. It is capable of visualising entire genomes of bacteriophages as well as plasmids and user-defined regions of longer prokaryotic genomes. Additionally, LoVis4u offers optional data processing steps to identify and highlight accessory and core genes in input sequences.</span></p>
<p dir="auto">https://art-egorov.github.io/lovis4u/</p>
<p dir="auto">&nbsp;</p><p>Address of the bookmark: <a href="https://github.com/art-egorov/lovis4u" rel="nofollow">https://github.com/art-egorov/lovis4u</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
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