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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/35252?offset=200</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/14215/the-8000-years-old-tibetian-gene-mutation</guid>
	<pubDate>Wed, 20 Aug 2014 21:57:44 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/14215/the-8000-years-old-tibetian-gene-mutation</link>
	<title><![CDATA[The 8000 years old Tibetian gene mutation !!!]]></title>
	<description><![CDATA[<p>A new study has provided insight into how gene mutation around 8,000 years ago helped Tibetans' to survive in the thin air on the Tibetan Plateau, where an average elevation is of 14,800 feet.<br /><br />A study led by University of Utah scientists is the first to find a genetic cause for the adaptation, a single DNA base pair change that dates back 8,000 years and demonstrate how it contributes to the Tibetans' ability to live in low oxygen conditions.</p><p>About 8,000 years ago, the gene EGLN1 changed by a single DNA base pair. Today, a relatively short time later on the scale of human history, 88 percent of Tibetans have the genetic variation, and it was virtually absent from closely related lowland Asians. The findings indicate the genetic variation endows its carriers with an advantage.<br /><br />In those without the adaptation, low oxygen caused their blood to become thick with oxygen-carrying red blood cells, an attempt to feed starved tissues, which could cause long-term complications such as heart failure. The researchers found that the newly identified genetic variation protected Tibetans by decreasing the over-response to low oxygen.</p><p>Reference: http://www.nature.com/nature/journal/v512/n7513/abs/nature13408.html</p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/26499/katju-lab</guid>
  <pubDate>Fri, 26 Feb 2016 03:25:32 -0600</pubDate>
  <link></link>
  <title><![CDATA[Katju Lab]]></title>
  <description><![CDATA[
<p>TheLab seek to understand the genetic factors contributing to genomic variation and phenotypic diversity.  To this end, we employ molecular and bioinformatic tools to study evolutionary processes at the level of populations, both experimental and natural, and genomes.  Our research interests encompass a wide range of topics, including the evolution of organellar and nuclear genomes, gene duplication and the origin of novel function, and the fitness and phenotypic consequences of mutation in evolution. For details regards ongoing projects, please see the Research page.</p>

<p>http://katjulab.com/research.html</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28903/genevalidator-identify-problems-with-predicted-genes</guid>
	<pubDate>Fri, 26 Aug 2016 06:00:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28903/genevalidator-identify-problems-with-predicted-genes</link>
	<title><![CDATA[GeneValidator - Identify problems with predicted genes]]></title>
	<description><![CDATA[<p>GeneValidator helps in identifing problems with gene predictions and provide useful information extracted from analysing orthologs in BLAST databases. The results produced can be used by biocurators and researchers who need accurate gene predictions.</p>
<p>If you would like to use GeneValidator on a few sequences, see our online&nbsp;<a href="http://genevalidator.sbcs.qmul.ac.uk/">GeneValidator Web App</a>&nbsp;-<a href="http://genevalidator.sbcs.qmul.ac.uk/">http://genevalidator.sbcs.qmul.ac.uk</a>.</p>
<p>If you use GeneValidator in your work, please cite us as follows:</p>
<blockquote>
<p><a href="http://bioinformatics.oxfordjournals.org/content/early/2016/02/26/bioinformatics.btw015">Dragan M<span>&Dagger;</span>, Moghul MI<span>&Dagger;</span>, Priyam A, Bustos C &amp; Wurm Y. 2016. GeneValidator: identify problems with protein-coding gene predictions.&nbsp;<em>Bioinformatics</em>, doi: 10.1093/bioinformatics/btw015</a>.</p>
<p>&nbsp;</p>
</blockquote>
<h2>&nbsp;</h2><p>Address of the bookmark: <a href="https://github.com/wurmlab/genevalidator" rel="nofollow">https://github.com/wurmlab/genevalidator</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34324/orthognc-a-software-for-accurate-identification-of-orthologs-based-on-gene-neighborhood-conservation</guid>
	<pubDate>Tue, 14 Nov 2017 09:30:35 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34324/orthognc-a-software-for-accurate-identification-of-orthologs-based-on-gene-neighborhood-conservation</link>
	<title><![CDATA[OrthoGNC: A Software for Accurate Identification of Orthologs Based on Gene Neighborhood Conservation]]></title>
	<description><![CDATA[<div>
<p id="sp0005">Orthology relations can be used to transfer annotations from one gene (or protein) to another. Hence, detecting orthology relations has become an important task in the post-genomic era. Various genomic events, such as duplication and horizontal gene transfer, can cause erroneous assignment of orthology relations. In closely-related species, gene neighborhood information can be used to resolve many ambiguities in orthology inference. Here we present OrthoGNC, a software for accurately predicting pairwise orthology relations based on gene neighborhood conservation. Analyses on simulated and real data reveal the high accuracy of OrthoGNC. In addition to orthology detection, OrthoGNC can be employed to investigate the conservation of genomic context among potential orthologs detected by other methods. OrthoGNC is freely available online at http://bs.ipm.ir/softwares/orthognc and http://tinyurl.com/orthoGNC.</p>
<p>http://www.comp.nus.edu.sg/~wongls/projects/orthoGNC/</p>
</div><p>Address of the bookmark: <a href="http://www.sciencedirect.com/science/article/pii/S1672022917301663" rel="nofollow">http://www.sciencedirect.com/science/article/pii/S1672022917301663</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38661/gene-ontology-consortium</guid>
	<pubDate>Fri, 11 Jan 2019 05:51:02 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38661/gene-ontology-consortium</link>
	<title><![CDATA[Gene Ontology Consortium]]></title>
	<description><![CDATA[<p>The GO knowledgebase is composed of two primary components:</p>
<ul>
<li>the&nbsp;<strong><a href="http://geneontology.org/page/ontology-documentation">Gene Ontology (GO)</a></strong>, which provides the logical structure of the biological functions (&lsquo;terms&rsquo;) and their relationships to one another, manifested as a directed acyclic graph</li>
<li>the corpus of&nbsp;<strong><a href="http://geneontology.org/page/go-annotations">GO annotations</a></strong>, evidence-based statements relating a specific gene product (a protein, non-coding RNA, or macromolecular complex, which we often refer to as &lsquo;genes&rsquo; for simplicity) to a specific ontology term</li>
</ul>
<p>Together, the ontology and annotations aim to describe a comprehensive model of biological systems. Currently, the GO knowledgebase includes experimental findings from over&nbsp;<a href="https://www.ncbi.nlm.nih.gov/pubmed/?term=loprovGeneOntol[SB]">140 000 published papers</a>, represented as over 600 000 experimentally-supported GO annotations. These provide the core dataset for additional inference of over 6 million functional annotations for a diverse set of organisms spanning the tree of life.</p>
<p>In addition to this core knowledgebase, GOC resources also include software to edit and perform logical reasoning over the ontologies, web access to the ontology and annotations, and analytical tools that use the GO knowledgebase to support biomedical research.</p><p>Address of the bookmark: <a href="http://www.geneontology.org/" rel="nofollow">http://www.geneontology.org/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41948/predict-gene-ontology-with-sequences</guid>
	<pubDate>Wed, 08 Jul 2020 04:59:28 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41948/predict-gene-ontology-with-sequences</link>
	<title><![CDATA[Predict Gene Ontology with sequences !]]></title>
	<description><![CDATA[<p><strong>PANNZER</strong>&nbsp;(Protein ANNotation with Z-scoRE) is a fully automated service for functional annotation of prokaryotic and eukaryotic proteins of unknown function. The tool is designed to predict the functional description (DE) and GO classes.</p>
<p>PANNZER2 processes bacterial proteomes in minutes and eukaryotic proteomes in an hour. You can use&nbsp;<a href="http://ekhidna2.biocenter.helsinki.fi/AAI/">AAI-profiler</a>&nbsp;to summarize a proteome's species neighbors and reveal taxonomic identity or contamination.</p>
<p>http://ekhidna2.biocenter.helsinki.fi/sanspanz/</p>
<p>IterPro is for the beginners</p>
<p><a href="https://www.ebi.ac.uk/interpro/">h</a><a href="https://www.ebi.ac.uk/interpro/">ttps://www.ebi.ac.uk/interpro/</a></p>
<p>You can find other comparative info at&nbsp;<a href="https://academic.oup.com/view-large/118391389">https://academic.oup.com/view-large/118391389</a></p><p>Address of the bookmark: <a href="http://ekhidna2.biocenter.helsinki.fi/sanspanz/" rel="nofollow">http://ekhidna2.biocenter.helsinki.fi/sanspanz/</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43877/crowdgo-machine-learning-and-semantic-similarity-guided-consensus-gene-ontology-annotation</guid>
	<pubDate>Thu, 26 May 2022 00:59:49 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43877/crowdgo-machine-learning-and-semantic-similarity-guided-consensus-gene-ontology-annotation</link>
	<title><![CDATA[CrowdGO: Machine learning and semantic similarity guided consensus Gene Ontology annotation]]></title>
	<description><![CDATA[<p dir="auto">CrowdGO is a protein Gene Ontology predictor using a meta approach, analyzing the predictions of other tools in order to get an improved precision and recall.</p>
<p dir="auto">Please note that the CrowdGO snakemake workflow is currently only tested on Ubuntu. It should work on OSX, but please report any errors to <a href="mailto:maarten.reijnders@unil.ch">maarten.reijnders@unil.ch</a> or create an issue.</p>
<p>https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010075</p><p>Address of the bookmark: <a href="https://gitlab.com/mreijnders/crowdgo" rel="nofollow">https://gitlab.com/mreijnders/crowdgo</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38692/geneck-gene-network-construction-kit-is-a-comprehensive-online-tool-kit-that-integrate-various-statistical-methods-to-construct-gene-networks</guid>
	<pubDate>Tue, 15 Jan 2019 09:39:30 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38692/geneck-gene-network-construction-kit-is-a-comprehensive-online-tool-kit-that-integrate-various-statistical-methods-to-construct-gene-networks</link>
	<title><![CDATA[GeNeCK (Gene Network Construction Kit) is a comprehensive online tool kit that integrate various statistical methods to construct gene networks]]></title>
	<description><![CDATA[<p><strong>GeNeCK</strong><span>&nbsp;(Gene Network Construction Kit) is a comprehensive online tool kit that integrate various statistical methods to construct gene networks based on gene expression data and optional hub gene information.</span></p>
<p><span><span>It efficiently constructs gene networks from expression data. It allows the user to use ten different network construction methods (such as partial correlation-, likelihood-, Bayesian- and mutual information-based methods) and integrates the resulting networks from multiple methods. Hub gene information, if available, can be incorporated to enhance performance.</span></span></p>
<p><span><span><span>GeNeCK is an efficient and easy-to-use web application for gene regulatory network construction. It can be accessed at&nbsp;</span><span><a href="http://lce.biohpc.swmed.edu/geneck" target="_blank"><span>http://lce.biohpc.swmed.edu/geneck</span></a></span></span></span></p><p>Address of the bookmark: <a href="http://lce.biohpc.swmed.edu/geneck/" rel="nofollow">http://lce.biohpc.swmed.edu/geneck/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44327/homologizer-phylogenetic-phasing-of-gene-copies-into-polyploid-subgenomes</guid>
	<pubDate>Sat, 03 Jun 2023 19:19:10 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44327/homologizer-phylogenetic-phasing-of-gene-copies-into-polyploid-subgenomes</link>
	<title><![CDATA[homologizer: Phylogenetic phasing of gene copies into polyploid subgenomes]]></title>
	<description><![CDATA[<p dir="auto">This tutorial describes the usage of&nbsp;<code>homologizer</code>&nbsp;to phase gene copies into polyploid subgenomes. The tutorial is an abbreviated version of a soon-to-be published paper in Methods in Molecular Biology. Please see that paper for many more details and practical considerations for running&nbsp;<code>homologizer</code>&nbsp;analyses. If you use&nbsp;<code>homologizer</code>, please cite the paper in which we first describe the method:</p>
<ul dir="auto">
<li>Freyman, W.A., Johnson, M.G., and C.J. Rothfels. 2022. Homologizer: phylogenetic phasing of gene copies into polyploid subgenomes.&nbsp;<em>bioRxiv</em>&nbsp;<a href="https://www.biorxiv.org/content/10.1101/2020.10.22.351486v4">2020.10.22.351486v4</a></li>
</ul>
<p dir="auto"><code>homologizer</code>&nbsp;is implemented in&nbsp;<code>RevBayes</code>. Please see&nbsp;<a href="http://revbayes.com/">http://revbayes.com</a>&nbsp;to download and install&nbsp;<code>RevBayes</code>. For users without previous&nbsp;<code>RevBayes</code>&nbsp;experience, we recommend the tutorials at&nbsp;<a href="http://revbayes.com/">http://revbayes.com</a>.</p><p>Address of the bookmark: <a href="https://github.com/wf8/homologizer" rel="nofollow">https://github.com/wf8/homologizer</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/13522/yannick-wurm-lab</guid>
  <pubDate>Thu, 07 Aug 2014 18:02:37 -0500</pubDate>
  <link></link>
  <title><![CDATA[Yannick Wurm Lab]]></title>
  <description><![CDATA[
<p>Evolutionary genomics of social insects. Extensive theoretical work has explained how and why complex societies evolve. However, only little is known about the genes and molecular mechanisms responsible for social phenotypes. We have been identifying genes and mechanisms involved in the evolution of insect societies using modern genomics tools (Illumina, RNAseq, RADseq...). For example we recently:</p>

<p>1. sequenced and analyzed the genome of the invasive red fire ant Solenopsis invicta (PNAS 2011)</p>

<p>2. discovered that a fundamental social trait in this species (how many queens are accepted in the colony) is determined by variants of a social chromosome (Nature 2013).</p>

<p>3. described the gene expression changes that occur in a virgin queen when she is given the opportunity of replacing her mother (Mol Ecol 2010).</p>

<p>Homepage: http://yannick.poulet.org/</p>
]]></description>
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