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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/35802?</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36583/eugi-a-novel-resource-for-studying-genomic-islands-to-facilitate-horizontal-gene-transfer-detection-in-eukaryotes</guid>
	<pubDate>Sat, 12 May 2018 07:26:59 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36583/eugi-a-novel-resource-for-studying-genomic-islands-to-facilitate-horizontal-gene-transfer-detection-in-eukaryotes</link>
	<title><![CDATA[EuGI: a novel resource for studying genomic islands to facilitate horizontal gene transfer detection in eukaryotes]]></title>
	<description><![CDATA[<p><span>SWGIS v2.0 along with the EuGI database, which houses GIs identified in 66 different eukaryotic species, and the EuGI web-resource, provide the first comprehensive resource for studying HGT in eukaryotes.</span></p>
<p>https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-018-4724-8</p><p>Address of the bookmark: <a href="https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-018-4724-8" rel="nofollow">https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-018-4724-8</a></p>]]></description>
	<dc:creator>Surabhi Chaudhary</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35106/alien-hunter-prediction-of-putative-horizontal-gene-transfer-hgt-events</guid>
	<pubDate>Sun, 07 Jan 2018 19:11:18 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35106/alien-hunter-prediction-of-putative-horizontal-gene-transfer-hgt-events</link>
	<title><![CDATA[Alien_Hunter : prediction of putative Horizontal Gene Transfer (HGT) events]]></title>
	<description><![CDATA[<p>Alien_hunter is an application for the prediction of putative Horizontal Gene Transfer (HGT) events with the implementation of Interpolated Variable Order Motifs (IVOMs).</p>
<p>An IVOM approach exploits compositional biases using variable order motif distributions and captures more reliably the local composition of a sequence compared to fixed-order methods. Optionally the predictions can be parsed into a 2-state 2nd order Hidden Markov Model (HMM), in a change-point detection framework, to optimize the localization of the boundaries of the predicted regions. The predictions (embl format) can be automatically loaded into the freely available Artemis genome viewer.</p><p>Address of the bookmark: <a href="http://www.sanger.ac.uk/science/tools/alien-hunter" rel="nofollow">http://www.sanger.ac.uk/science/tools/alien-hunter</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41969/shadowcaster-a-hybrid-approach-for-the-detection-of-horizontal-gene-transfer-events-in-prokaryotes</guid>
	<pubDate>Tue, 14 Jul 2020 06:42:10 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41969/shadowcaster-a-hybrid-approach-for-the-detection-of-horizontal-gene-transfer-events-in-prokaryotes</link>
	<title><![CDATA[ShadowCaster: a hybrid approach for the detection of horizontal gene transfer events in prokaryotes]]></title>
	<description><![CDATA[<p><span>ShadowCaster implements an evolutionary model to calculate Bayesian likelihoods for each &lsquo;alien genes&rsquo; with an unusual sequence composition according to the host genome background to detect HGT events in prokaryotes.</span></p>
<p><a href="https://www.mdpi.com/2073-4425/11/7/756/htm">https://www.mdpi.com/2073-4425/11/7/756/htm</a></p>
<p><a href="https://shadowcaster.readthedocs.io/en/latest/">https://shadowcaster.readthedocs.io/en/latest/</a></p>
<p><a href="https://github.com/dani2s/ShadowCaster_testData">https://github.com/dani2s/ShadowCaster_testData</a></p><p>Address of the bookmark: <a href="https://github.com/dani2s/ShadowCaster" rel="nofollow">https://github.com/dani2s/ShadowCaster</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35252/hgt-finder-a-new-tool-for-horizontal-gene-transfer-finding-and-application-to-aspergillus-genomes</guid>
	<pubDate>Wed, 17 Jan 2018 05:03:19 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35252/hgt-finder-a-new-tool-for-horizontal-gene-transfer-finding-and-application-to-aspergillus-genomes</link>
	<title><![CDATA[HGT-Finder: A New Tool for Horizontal Gene Transfer Finding and Application to Aspergillus genomes]]></title>
	<description><![CDATA[<p><span>HGT-Finder: </span></p>
<p><span>(i) can be used for HGT detection in both prokaryotes and eukaryotes, </span></p>
<p><span>(ii) can report a statistical&nbsp;</span><em>P</em><span>&nbsp;value for each gene to indicate how likely it is to be horizontally transferred, and </span></p>
<p><span>(iii) is fully automated (requires minimal human intervention), as well as very easy to install and run.&nbsp;</span></p><p>Address of the bookmark: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4626719/" rel="nofollow">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4626719/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33651/darkhorse-a-method-for-genome-wide-prediction-of-horizontal-gene-transfer</guid>
	<pubDate>Thu, 22 Jun 2017 07:58:35 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33651/darkhorse-a-method-for-genome-wide-prediction-of-horizontal-gene-transfer</link>
	<title><![CDATA[DarkHorse: a method for genome-wide prediction of horizontal gene transfer]]></title>
	<description><![CDATA[<p><span>A new approach to rapid, genome-wide identification and ranking of horizontal transfer candidate proteins is presented. The method is quantitative, reproducible, and computationally undemanding. It can be combined with genomic signature and/or phylogenetic tree-building procedures to improve accuracy and efficiency. The method is also useful for retrospective assessments of horizontal transfer prediction reliability, recognizing orthologous sequences that may have been previously overlooked or unavailable. These features are demonstrated in bacterial, archaeal, and eukaryotic examples.</span></p><p>Address of the bookmark: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1852411/" rel="nofollow">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1852411/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27959/darkhorse</guid>
	<pubDate>Wed, 22 Jun 2016 05:37:38 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27959/darkhorse</link>
	<title><![CDATA[DarkHorse]]></title>
	<description><![CDATA[<p><em>DarkHorse</em>&nbsp;is a bioinformatic method for rapid, automated identification and ranking of phylogenetically atypical proteins on a genome-wide basis. It works by selecting potential ortholog matches from a reference database of amino acid sequences, then using these matches to calculate a lineage probability index (LPI) score for each genome protein.</p>
<p>LPI scores are inversely proportional to the phylogenetic distance between database match sequences and the query genome. These scores are useful not only for large-scale<em>de novo</em>&nbsp;predictions of horizontally transferred proteins, but can also serve as an independent quality control test for potential horizontal transfer candidates identified by alternative methods, especially those based on nucleic acid signatures. Candidates having high LPI scores are unlikely to have been horizontally transferred, since they are highly conserved among closely related organisms.</p>
<p>One unique and powerful feature of the DarkHorse HGT Candidate database is the opportunity to explore the phylogenetic background of potential HGT donors as well as recipients. The breadth of the database allows not only query sequences, but also their database match partners to be evaluated for sequence similarity or novelty compared to taxonomically related organisms.</p>
<p><em>DarkHorse</em>&nbsp;is configurable for varying degrees of phylogenetic granularity and protein sequence conservation. Users should consult the&nbsp;<a href="http://darkhorse.ucsd.edu/#references">references</a>&nbsp;cited below for a complete explanation of parameter selection and result interpretation. A brief&nbsp;<a href="http://darkhorse.ucsd.edu/tutorial.html">tutorial</a>&nbsp;page is also available on-line.</p><p>Address of the bookmark: <a href="http://darkhorse.ucsd.edu/download.html" rel="nofollow">http://darkhorse.ucsd.edu/download.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27961/nearhgt</guid>
	<pubDate>Wed, 22 Jun 2016 05:41:57 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27961/nearhgt</link>
	<title><![CDATA[NearHGT]]></title>
	<description><![CDATA[<p>Horizontal gene transfer (HGT), the transfer of genetic material between organisms, is crucial for genetic innovation and the evolution of genome architecture. Existing HGT detection algorithms rely on a strong phylogenetic signal distinguishing the transferred sequence from ancestral (vertically derived) genes in its recipient genome. Detecting HGT between closely related species or strains is challenging, as the phylogenetic signal is usually weak and the nucleotide composition is normally nearly identical. Nevertheless, there is a great importance in detecting HGT between congeneric species or strains, especially in clinical microbiology, where understanding the emergence of new virulent and drug-resistant strains is crucial, and often time-sensitive.</p>
<p>We developed a novel, self-contained technique named&nbsp;<em>Near HGT</em>, based on the&nbsp;<em>synteny index</em>, to measure the divergence of a gene from its native genomic environment and used it to identify candidate HGT events between closely related strains. The method confirms candidate transferred genes based on the&nbsp;<em>constant relative mutability</em>&nbsp;(CRM). Using CRM, the algorithm assigns a confidence score based on &ldquo;unusual&rdquo; sequence divergence. A gene exhibiting exceptional deviations according to both synteny and mutability criteria, is considered a validated HGT product. We first employed the technique to a set of three&nbsp;<em>E. coli</em>&nbsp;strains and detected several highly probable horizontally acquired genes. We then compared the method to existing HGT detection tools using a larger strain data set.</p>
<p>When combined with additional approaches our new algorithm provides richer picture and brings us closer to the goal of detecting all newly acquired genes in a particular strain.</p>
<p><strong>Availability:</strong><span>&nbsp;The method is publicly available at</span><a href="http://research.haifa.ac.il/~ssagi/software/nearHGT.zip">http://research.haifa.ac.il/~ssagi/software/nearHGT.zip</a></p><p>Address of the bookmark: <a href="http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004408" rel="nofollow">http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004408</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41362/genemates-an-r-package-for-detecting-horizontal-gene-co-transfer-between-bacteria-using-gene-gene-associations-controlled-for-population-structure</guid>
	<pubDate>Sat, 07 Mar 2020 05:52:20 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41362/genemates-an-r-package-for-detecting-horizontal-gene-co-transfer-between-bacteria-using-gene-gene-associations-controlled-for-population-structure</link>
	<title><![CDATA[GeneMates: an R package for Detecting Horizontal Gene Co-transfer between Bacteria Using Gene-gene Associations Controlled for Population Structure]]></title>
	<description><![CDATA[<p><span>GeneMates is an R package implementing a network approach to identify horizontal gene co-transfer (HGcoT) between bacteria using whole-genome sequencing (WGS) data. It is particularly useful for investigating intra-species HGcoT, where presence-absence status of acquired genes is usually confounded by bacterial population structure due to clonal reproduction.</span></p>
<p><a href="https://www.biorxiv.org/content/10.1101/2020.02.29.970970v1">https://www.biorxiv.org/content/10.1101/2020.02.29.970970v1</a></p><p>Address of the bookmark: <a href="https://github.com/wanyuac/GeneMates" rel="nofollow">https://github.com/wanyuac/GeneMates</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41571/wego-simple-but-useful-tool-for-visualizing-comparing-and-plotting-go-gene-ontology-annotation-results</guid>
	<pubDate>Sun, 12 Apr 2020 10:02:22 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41571/wego-simple-but-useful-tool-for-visualizing-comparing-and-plotting-go-gene-ontology-annotation-results</link>
	<title><![CDATA[WEGO : simple but useful tool for visualizing, comparing and plotting GO (Gene Ontology) annotation results]]></title>
	<description><![CDATA[<p><span>WEGO (Web Gene Ontology Annotation Plot) is a simple but useful tool for visualizing, comparing and plotting GO (Gene Ontology) annotation results. As the GO vocabulary became more and more popular, WEGO was widely adopted and used in many researches. Therefore we have updated WEGO 2.0 in 2018. Here are some changes we&rsquo;ve made:</span><br><span>1. The limit of input file numbers was cancelled. Now the users could upload as many files as they want with one operation.</span><br><span>2. We have added the reference data of 9 species for users selection.</span><br><span>3. Besides the traditional WEGO histogram, WEGO 2.0 outputs an additional type of bar graph showing GO terms with significant gene number differences.</span></p><p>Address of the bookmark: <a href="http://wego.genomics.org.cn/" rel="nofollow">http://wego.genomics.org.cn/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38055/ancestral-genomes-a-resource-for-reconstructed-ancestral-genes-and-genomes-across-the-tree-of-life</guid>
	<pubDate>Fri, 02 Nov 2018 08:16:27 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38055/ancestral-genomes-a-resource-for-reconstructed-ancestral-genes-and-genomes-across-the-tree-of-life</link>
	<title><![CDATA[Ancestral Genomes: a resource for reconstructed ancestral genes and genomes across the tree of life]]></title>
	<description><![CDATA[<p><span>&nbsp;Ancestral Genomes (</span><a href="http://ancestralgenomes.org/" target="">http://ancestralgenomes.org</a><span>) is a resource for comprehensive reconstructions of these &lsquo;fossil genomes&rsquo;. Comprehensive sets of protein-coding genes have been reconstructed for 78 genomes of now-extinct species that were the common ancestors of extant species from across the tree of life.&nbsp;</span></p><p>Address of the bookmark: <a href="http://ancestralgenomes.org/" rel="nofollow">http://ancestralgenomes.org/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
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