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<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/41814?offset=50</link>
	<atom:link href="https://bioinformaticsonline.com/related/41814?offset=50" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37954/biogps-spotlight-on-the-gene-expression-atlas</guid>
	<pubDate>Thu, 18 Oct 2018 12:15:12 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37954/biogps-spotlight-on-the-gene-expression-atlas</link>
	<title><![CDATA[BioGPS: Spotlight on the Gene Expression Atlas]]></title>
	<description><![CDATA[<p>BioGPS opened 2016 with a publication in Nucleic Acids Research, right after the New Year holiday. Throughout the year, new designs for the site were being created, reviewed, adjusted, reviewed, adjusted, and more review/adjustments in anticipation of a site redesign for 2017. A Plugin registration Blitz was held in March and April; followed by a Plugin Review Blitz in May. The BioGPS spotlight series was also restarted, with spotlights on BGEE, Intermine, and other Intermine-related plugins.</p>
<p>There were ~910,000 requests made to BioGPS in 2016. Requests to BioGPS peaked in March and at the lowest in December.</p><p>Address of the bookmark: <a href="http://biogps.org/" rel="nofollow">http://biogps.org/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41820/shinygo-v061-gene-ontology-enrichment-analysis-more</guid>
	<pubDate>Wed, 03 Jun 2020 08:00:30 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41820/shinygo-v061-gene-ontology-enrichment-analysis-more</link>
	<title><![CDATA[ShinyGO v0.61: Gene Ontology Enrichment Analysis + more]]></title>
	<description><![CDATA[<p>2/3/2020: Now published by&nbsp;<a href="https://doi.org/10.1093/bioinformatics/btz931" target="_blank">Bioinformatics.</a></p>
<p>11/3/2019: V 0.61, Improve graphical visualization (thanks to reviewers). Interactive networks and much more.</p>
<p>5/20/2019: V.0.60, Annotation database updated to Ensembl 96. New bacterial and fungal genomes based on STRING-db! Just paste your gene list to get enriched GO terms and othe pathways for over 315 plant and animal species, based on annotation from Ensembl (Release 96), Ensembl plants (R. 43) and Ensembl Metazoa (R. 43). An additional 2031 genomes (including bacteria and fungi) are annotated based on STRING-db (v.10). In addition, it also produces KEGG pathway diagrams with your genes highlighted, hierarchical clustering trees and networks summarizing overlapping terms/pathways, protein-protein interaction networks, gene characterristics plots, and enriched promoter motifs.&nbsp;</p><p>Address of the bookmark: <a href="http://bioinformatics.sdstate.edu/go/" rel="nofollow">http://bioinformatics.sdstate.edu/go/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43804/agora-algorithm-for-gene-order-reconstruction-in-ancestors</guid>
	<pubDate>Mon, 28 Feb 2022 23:26:21 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43804/agora-algorithm-for-gene-order-reconstruction-in-ancestors</link>
	<title><![CDATA[AGORA: Algorithm for Gene Order Reconstruction in Ancestors]]></title>
	<description><![CDATA[<p dir="auto">AGORA stands for &ldquo;Algorithm for Gene Order Reconstruction in Ancestors&rdquo; and was developed by Matthieu Muffato in the DYOGEN Laboratory at the &Eacute;cole normale sup&eacute;rieure in Paris in 2008.</p>
<div>
<pre><code>    // | |     //   ) )  //   ) ) //   ) )  // | |
   //__| |    //        //   / / //___/ /  //__| |
  / ___  |   //  ____  //   / / / ___ (   / ___  |
 //    | |  //    / / //   / / //   | |  //    | |
//     | | ((____/ / ((___/ / //    | | //     | |
</code></pre>
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<p dir="auto">AGORA is used to generate ancestral genomes for the&nbsp;<a href="https://www.genomicus.biologie.ens.fr/genomicus">Genomicus</a>&nbsp;online server for gene order comparison, and has been in constant use in the group since.</p><p>Address of the bookmark: <a href="https://github.com/DyogenIBENS/Agora" rel="nofollow">https://github.com/DyogenIBENS/Agora</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/33306/ancestral-sequence-reconstruction-asr-or-ancestral-genesequence-reconstructionresurrection-tools-to-study-molecular-evolution</guid>
	<pubDate>Tue, 30 May 2017 04:20:05 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/33306/ancestral-sequence-reconstruction-asr-or-ancestral-genesequence-reconstructionresurrection-tools-to-study-molecular-evolution</link>
	<title><![CDATA[Ancestral sequence reconstruction (ASR) or ancestral gene/sequence reconstruction/resurrection tools to study molecular evolution]]></title>
	<description><![CDATA[<p><span><strong>Ancestral sequence reconstruction</strong><span>&nbsp;(</span><strong>ASR</strong><span>) &ndash; also known as&nbsp;</span><strong>ancestral gene</strong><span>/</span><strong>sequence reconstruction</strong><span>/</span><strong>resurrection</strong><span>&nbsp;&ndash; is a technique used in the study of&nbsp;</span>molecular evolution<span>. The method consists of the synthesis of an ancestral&nbsp;</span>gene<span>&nbsp;and expression of the corresponding ancestral&nbsp;</span>protein<span>.&nbsp;</span><sup id="cite_ref-thornton_1-0"><a href="https://en.wikipedia.org/wiki/Ancestral_sequence_reconstruction#cite_note-thornton-1"></a></sup><span>The idea of protein 'resurrection' was suggested in 1963 by Pauling and Zuckerkandl.</span><sup id="cite_ref-2"><a href="https://en.wikipedia.org/wiki/Ancestral_sequence_reconstruction#cite_note-2"></a></sup><span>&nbsp;Some early efforts were made in the eighties-nineties, led by the laboratory of&nbsp;</span>Steven A. Benner<span>, showing the potential of this technique &ndash; one that only started to be fulfilled in the post-genomic era.</span><sup id="cite_ref-3"><a href="https://en.wikipedia.org/wiki/Ancestral_sequence_reconstruction#cite_note-3"></a></sup><span>&nbsp;Thanks to the improvement of algorithms and of better sequencing and synthesis techniques, the method was developed further in the early 2000s to allow the resurrection of a greater variety of and much more ancient genes.</span><sup id="cite_ref-4"><a href="https://en.wikipedia.org/wiki/Ancestral_sequence_reconstruction#cite_note-4"></a></sup><span>&nbsp;Over the last decade, ancestral protein resurrection has developed as a strategy to reveal the mechanisms and dynamics of protein evolution.&nbsp;</span></span></p><p><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e4/ASR_phylogeny.png/510px-ASR_phylogeny.png" alt="image" width="610" height="435" style="border: 0px; border: 0px;"></p><p><span>Following are the list of&nbsp;</span><strong style="font-size: 12.8px;">Ancestral /sequence/ reconstruction</strong><span>&nbsp;(</span><strong style="font-size: 12.8px;">ASR</strong><span>) tools:&nbsp;</span></p><p><a href="http://www.bx.psu.edu/miller_lab/car/" target="_blank" title="To inferCars official website"><span>inferCars</span></a></p><p><span><span><span><span><span>Reconstructs contiguous regions of an ancestral genome. Given information about adjacencies between conserved segments in each modern species, our goal is to infer segment order in the ancestral genome. To get a clean and precise statement of the problem, we formalize it using graph theory. We develop an algorithm that identifies a most parsimonious scenario for the history of each individual adjacency, although the whole-genome prediction is not guaranteed to optimize traditional measures like the number of breakpoints. We introduce weights to the graph edges to model the reliability of each adjacency.</span></span></span></span></span></p><p><span><span><a href="http://paleogenomics.irmacs.sfu.ca/ANGES/" target="_blank" title="To ANGES official website">ANGES</a>:</span><a href="http://paleogenomics.irmacs.sfu.ca/ANGES/" target="_blank" title="To ANGES official website">reconstructing ANcestral GEnomeS maps</a></span></p><p><span><span><span><span><span><span>A suite of Python programs that allows reconstructing ancestral genome maps from the comparison of the organization of extant-related genomes. ANGES can reconstruct ancestral genome maps for multichromosomal linear genomes and unichromosomal circular genomes. It implements methods inspired from techniques developed to compute physical maps of extant genomes.</span></span></span></span></span></span></p><p><a href="http://virulence.molgen.mpg.de/cocos/" target="_blank" title="To Cocos official website"><span>Cocos</span></a></p><p><span><span><span><span><span><span><span>Constructs phylogenies of multi-domain proteins. With a given species tree and domain phylogenies, the procedure infers the composition of ancestral multi-domain proteins. Cocos implements and extend a suggested algorithmic approach by Behzadi and Vingron in an easy-to-use program. Such method could be applied to reconstruction of partial homologous units such as bacterial operons or protein complexes.</span></span></span></span></span></span></span></p><p><a href="https://github.com/msrosenberg/MySSP" target="_blank" title="To MySSP official website"><span>MySSP</span></a></p><p><span><span><span><span><span><span><span><span>Constructs an initial DNA sequence at the root of the tree and simulates evolution across the tree using a variety of common models of DNA evolution. MySSP is a program for the simulation of DNA sequence evolution across a phylogenetic tree. It is designed for large-scale studies, including simulation of multiple replicates and outputs sequences into NEXUS, MEGA, or FASTA formats. MySSP has a fairly simple graphical user interface (GUI) for basic use, but also has a specialized batch script interpreter to allow for more complicated or large-scale simulations.</span></span></span></span></span></span></span></span></p><p><span><span><a href="http://www.cs.cmu.edu/~ckingsf/software/parana/" target="_blank" title="To PARANA official website">PARANA</a>:&nbsp;</span><a href="http://www.cs.cmu.edu/~ckingsf/software/parana/" target="_blank" title="To PARANA official website">Parsimonious Ancestral Reconstruction And Network Analysis</a></span></p><p><span><span><span><span><span><span><span><span><span>Performs parsimony based inference of ancestral biological networks. Given multiple extant networks and phylogenetic information relating extant nodes, PARANA finds a parsimonious set of ancestral interaction events (edge gains and losses) which explain the extant networks. The framework adopted by PARANA is able to represent network evolution under models that support gene duplication and loss and independent interaction gain and loss. The method works on both directed and undirected networks and can incorporate asymmetric interaction gain and loss costs. In contrast to previous approaches, PARANA does not require knowing the relative ordering of unrelated duplication events and thus, works on phylogenetic trees even where branch lengths are not provided.</span></span></span></span></span></span></span></span></span></p><p><span><span><a href="http://www-labs.iro.umontreal.ca/~mabrouk/" target="_blank" title="To GapAdj official website">GapAdj</a>:&nbsp;</span><a href="http://www-labs.iro.umontreal.ca/~mabrouk/" target="_blank" title="To GapAdj official website">Gapped Adjacencies</a></span></p><p><span><span><span><span><span><span><span><span><span><span>A synteny-based method that is flexible enough to handle a model of evolution involving whole genome duplication events, in addition to rearrangements, gene insertions, and losses. Ancestral relationships between markers are defined in term of Gapped Adjacencies, i.e. pairs of markers separated by up to a given number of markers. It improves on a previous restricted to direct adjacencies, which revealed a high accuracy for adjacency prediction, but with the drawback of being overly conservative, i.e. of generating a large number of contiguous ancestral regions (CARs).</span></span></span></span></span></span></span></span></span></span></p><p><a href="http://ancestors.bioinfo.uqam.ca/"><span><span><span><span><span><span><span><span><span><span>ANCESTOR</span></span></span></span></span></span></span></span></span></span></a></p><p><span><span><span><span><span><span><span><span><span><span><span>A web server allowing one to easily and quickly perform the last three steps of the ancestral genome reconstruction procedure. Ancestors implements several alignment algorithms, an indel maximum likelihood solver and a context-dependent maximum likelihood substitution inference algorithm. The results presented by the server include the posterior probabilities for the last two steps of the ancestral genome reconstruction and the expected error rate of each ancestral base prediction.</span></span></span></span></span></span></span></span></span></span></span></p><p><a href="http://bioinfo.lifl.fr/procars/" target="_blank" title="To ProCARs official website"><span>ProCARs</span></a></p><p>Reconstructs ancestral gene orders as contiguous ancestral regions (CARs) with a progressive homology-based method. ProCARs runs from a phylogeny tree (without branch lengths needed) with a marked ancestor and a block file. This homology-based method is based on iteratively detecting and assembling ancestral adjacencies, while allowing some micro-rearrangements of synteny blocks at the extremities of the progressively assembled CARs. The method starts with a set of blocks as the initial set of CARs, and detects iteratively the potential ancestral adjacencies between extremities of CARs, while building up the CARs progressively by adding, at each step, new non-conflicting adjacencies that induce the less homoplasy phenomenon. The species tree is used, in some additional internal steps, to compute a score for the remaining conflicting adjacencies, and to detect other reliable adjacencies, in order to reach completely assembled ancestral genomes.</p><p><a href="http://fastml.tau.ac.il/" target="_blank" title="To FastML official website"><span>FastML</span></a></p><p>A user-friendly tool for the reconstruction of ancestral sequences. FastML implements various novel features that differentiate it from existing tools: (i) FastML uses an indel-coding method, in which each gap, possibly spanning multiples sites, is coded as binary data. FastML then reconstructs ancestral indel states assuming a continuous time Markov process. FastML provides the most likely ancestral sequences, integrating both indels and characters; (ii) FastML accounts for uncertainty in ancestral states: it provides not only the posterior probabilities for each character and indel at each sequence position, but also a sample of ancestral sequences from this posterior distribution, and a list of the k-most likely ancestral sequences; (iii) FastML implements a large array of evolutionary models, which makes it generic and applicable for nucleotide, protein and codon sequences; and (iv) a graphical representation of the results is provided, including, for example, a graphical logo of the inferred ancestral sequences.</p><p><a href="http://rth.dk/resources/maxAlike/" target="_blank" title="To maxAlike official website"><span>maxAlike</span></a></p><p>Reconstructs a genomic sequence for a specific taxon based on sequence homologs in other species. The input is a multiple sequence alignment and a phylogenetic tree that also contains the target species. For this target species, the algorithm computes nucleotide probabilities at each sequence position. Consensus sequences are then reconstructed based on a certain confidence level.</p><p><span><span><a href="http://www.geneorder.org/server.php" target="_blank" title="To MLGO official website">MLGO</a>:&nbsp;</span><a href="http://www.geneorder.org/server.php" target="_blank" title="To MLGO official website">Maximum Likelihood for Gene Order Analysis</a></span></p><p>A web tool for the reconstruction of phylogeny and/or ancestral genomes from gene-order data. MLGO was designed for analysis of large-scale genomic changes including not only rearrangements but also gene insertions, deletions and duplications. MLGO can be used to infer a phylogeny from genome rearrangement and gene order data, and can also obtain an estimation of ancestral genomes, given an input tree. MLGO takes the advantage of binary encoding on gene-order data, supports a fairly general model of genomic evolution (rearrangements plus duplications, insertions, and losses of genomic regions), and successfully accommodates itself into the framework of maximized likelihood.</p><p>Image Reference : Wiki</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36849/glean-an-unsupervised-learning-system-to-integrate-disparate-sources-of-gene-structure-evidence</guid>
	<pubDate>Sat, 02 Jun 2018 07:38:33 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36849/glean-an-unsupervised-learning-system-to-integrate-disparate-sources-of-gene-structure-evidence</link>
	<title><![CDATA[GLEAN: an unsupervised learning system to integrate disparate sources of gene structure evidence]]></title>
	<description><![CDATA[<p><span>GLEAN is an unsupervised learning system to integrate disparate sources of gene structure evidence (gene model predictions, EST/protein genomic sequence alignments, SAGE/peptide tags, etc) to produce a consensus gene prediction, without prior training.</span></p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/glean-gene/" rel="nofollow">https://sourceforge.net/projects/glean-gene/</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38541/geneoverlap-an-r-package-to-test-and-visualize-gene-overlaps</guid>
	<pubDate>Thu, 27 Dec 2018 19:45:52 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38541/geneoverlap-an-r-package-to-test-and-visualize-gene-overlaps</link>
	<title><![CDATA[GeneOverlap: An R package to test and visualize gene overlaps]]></title>
	<description><![CDATA[<p>Overlapping gene lists can reveal biological meanings and may lead to novel hypotheses. For example, histone modification is an important cellular mechanism that can pack and re-pack chromatin. By making the chromatin structure more dense or loose, the gene expression can be turned on or off. Tri-methylation on lysine 4 of histone H3 (H3K4me3) is associated with gene activation and its genome-wide enrichment can be mapped by using ChIP-seq experiments. Because of its activating role, if we overlap the genes that are bound by H3K4me3 with the genes that are highly expressed, we should expect a positive association. Similary, we can perform such kind of overlapping between the gene lists of different histone modifications with that of various expression groups and establish each histone modification&rsquo;s role in gene regulation.</p><p>Address of the bookmark: <a href="https://bioconductor.org/packages/release/bioc/vignettes/GeneOverlap/inst/doc/GeneOverlap.pdf" rel="nofollow">https://bioconductor.org/packages/release/bioc/vignettes/GeneOverlap/inst/doc/GeneOverlap.pdf</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42038/pyparanoid-a-pipeline-for-rapid-identification-of-homologous-gene-families-in-a-set-of-genomes</guid>
	<pubDate>Thu, 13 Aug 2020 10:06:19 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42038/pyparanoid-a-pipeline-for-rapid-identification-of-homologous-gene-families-in-a-set-of-genomes</link>
	<title><![CDATA[PyParanoid: a pipeline for rapid identification of homologous gene families in a set of genomes]]></title>
	<description><![CDATA[<p>PyParanoid is a pipeline for rapid identification of homologous gene families in a set of genomes - a central task of any comparative genomics analysis. The "gold standard" for identifying homologs is to use reciprocal best hits (RBHs) which depends on performing a all-vs-all sequence comparison, usually using BLAST, to determine homology. However, these methods are computationally expensive, requiring&nbsp;O(n2)&nbsp;resources to identify RBHs. This is problematic, as the modern deluge of sequencing data means that comparative genomics analyses could be performed on datasets of thousands of strains.</p><p>Address of the bookmark: <a href="https://github.com/ryanmelnyk/PyParanoid" rel="nofollow">https://github.com/ryanmelnyk/PyParanoid</a></p>]]></description>
	<dc:creator>BioStar</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/bookmarks/view/40969/leaflet-javascript-libraries-for-interactive-maps</guid>
	<pubDate>Mon, 10 Feb 2020 01:35:07 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40969/leaflet-javascript-libraries-for-interactive-maps</link>
	<title><![CDATA[Leaflet: JavaScript libraries for interactive maps]]></title>
	<description><![CDATA[<p><a href="http://leafletjs.com/">Leaflet</a><span>&nbsp;is one of the most popular open-source JavaScript libraries for interactive maps.</span></p>
<h3>Features</h3>
<ul>
<li>Interactive panning/zooming</li>
<li>Compose maps using arbitrary combinations of:
<ul>
<li>Map tiles</li>
<li>Markers</li>
<li>Polygons</li>
<li>Lines</li>
<li>Popups</li>
<li>GeoJSON</li>
</ul>
</li>
<li>Create maps right from the R console or RStudio</li>
<li>Embed maps in&nbsp;<a href="http://yihui.name/knitr/">knitr</a>/<a href="http://rmarkdown.rstudio.com/">R Markdown</a>&nbsp;documents and&nbsp;<a href="http://shiny.rstudio.com/">Shiny</a>&nbsp;apps</li>
<li>Easily render spatial objects from the&nbsp;<code>sp</code>&nbsp;or&nbsp;<code>sf</code>&nbsp;packages, or data frames with latitude/longitude columns</li>
<li>Use map bounds and mouse events to drive Shiny logic</li>
<li>Display maps in non spherical mercator projections</li>
<li>Augment map features using chosen plugins from&nbsp;<a href="http://leafletjs.com/plugins">leaflet plugins repository</a></li>
</ul>
<p><a href="https://rstudio.github.io/leaflet/">https://rstudio.github.io/leaflet/</a></p><p>Address of the bookmark: <a href="https://rstudio.github.io/leaflet/" rel="nofollow">https://rstudio.github.io/leaflet/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32851/anges-reconstructing-ancestral-genomes-maps</guid>
	<pubDate>Thu, 18 May 2017 05:27:08 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32851/anges-reconstructing-ancestral-genomes-maps</link>
	<title><![CDATA[ANGES: reconstructing ANcestral GEnomeS maps]]></title>
	<description><![CDATA[<p>This page contains the software ANGES 1.01, that aims at reconstucting ancestral genome maps from homologous markers in extant related genomes.</p>
<h3>Download</h3>
<ul>
<li><a href="http://paleogenomics.irmacs.sfu.ca/ANGES/anges_1.01.tar.gz">Program, version 1.01</a>&nbsp;(July 10, 2012, documentation updated in August 2014)</li>
<li><a href="http://paleogenomics.irmacs.sfu.ca/ANGES/anges_1.01_examples_with_results.tar.gz">Examples with results (featured ancestors: boreoeutherian, amniote, yeasts, Burkholderia, monocots)</a>; please refer to the documentation of the distribution above.</li>
</ul><p>Address of the bookmark: <a href="http://paleogenomics.irmacs.sfu.ca/ANGES/" rel="nofollow">http://paleogenomics.irmacs.sfu.ca/ANGES/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>

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