<?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/44770?offset=50</link>
	<atom:link href="https://bioinformaticsonline.com/related/44770?offset=50" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30459/prodigal-prokaryotic-dynamic-programming-genefinding-algorithm</guid>
	<pubDate>Thu, 29 Dec 2016 03:26:45 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30459/prodigal-prokaryotic-dynamic-programming-genefinding-algorithm</link>
	<title><![CDATA[Prodigal (Prokaryotic Dynamic Programming Genefinding Algorithm)]]></title>
	<description><![CDATA[<p><span>Prodigal (</span><strong>Pro</strong><span>karyotic&nbsp;</span><strong>Dy</strong><span>namic Programming&nbsp;</span><strong>G</strong><span>enefinding&nbsp;</span><strong>Al</strong><span>gorithm) is a microbial (bacterial and archaeal) gene finding program developed at Oak Ridge National Laboratory and the University of Tennessee. Key features of Prodigal include:</span></p>
<ul>
<li><strong>Speed</strong>: Prodigal is an extremely fast gene recognition tool (written in very vanilla C). It can analyze an entire microbial genome in 30 seconds or less.</li>
<li><strong>Accuracy</strong>: Prodigal is a highly accurate gene finder. It correctly locates the 3' end of every gene in the experimentally verified Ecogene data set (except those containing introns). It possesses a very sophisticated ribosomal binding site scoring system that enables it to locate the translation initiation site with great accuracy (96% of the 5' ends in the Ecogene data set are located correctly).</li>
<li><strong>Specificity</strong>: Prodigal's false positive rate compares favorably with other gene identification programs, and usually falls under 5%.</li>
<li><strong>GC-Content Indifferent</strong>: Prodigal performs well even in high GC genomes, with over a 90% perfect match (5'+3') to the&nbsp;<em>Pseudomonas aeruginosa</em>&nbsp;curated annotations.</li>
<li><strong>Metagenomic Version</strong>: Prodigal can run in metagenomic mode and analyze sequences even when the organism is unknown.</li>
<li><strong>Ease of Use</strong>: Prodigal can be run in one step on a single genomic sequence or on a draft genome containing many sequences. It does not need to be supplied with any knowledge of the organism, as it learns all the properties it needs to on its own.</li>
<li><strong>Open Source</strong>: Prodigal source code is freely available under the General Public License.</li>
</ul>
<p>&nbsp;</p>
<div style="text-align: center;"><strong>Download the latest version of Prodigal at&nbsp;<a href="http://github.com/hyattpd/prodigal/releases/">the Prodigal github page.</a></strong>&nbsp;<br>or&nbsp;<br><strong>Browse the&nbsp;<a href="http://github.com/hyattpd/prodigal/wiki">wiki documenation.</a></strong>&nbsp;</div><p>Address of the bookmark: <a href="http://prodigal.ornl.gov/" rel="nofollow">http://prodigal.ornl.gov/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34862/pasa-gene-structure-annotation-and-analysis</guid>
	<pubDate>Tue, 26 Dec 2017 21:14:03 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34862/pasa-gene-structure-annotation-and-analysis</link>
	<title><![CDATA[PASA: Gene Structure Annotation and Analysis]]></title>
	<description><![CDATA[<p><span>PASA, acronym for Program to Assemble Spliced Alignments, is a eukaryotic genome annotation tool that exploits spliced alignments of expressed transcript sequences to automatically model gene structures, and to maintain gene structure annotation consistent with the most recently available experimental sequence data. PASA also identifies and classifies all splicing variations supported by the transcript alignments.</span></p><p>Address of the bookmark: <a href="http://pasapipeline.github.io/" rel="nofollow">http://pasapipeline.github.io/</a></p>]]></description>
	<dc:creator>biogeek</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37460/revigo-reduced-visualize-gene-ontology</guid>
	<pubDate>Tue, 31 Jul 2018 05:28:42 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37460/revigo-reduced-visualize-gene-ontology</link>
	<title><![CDATA[REVIGO: Reduced Visualize gene ontology]]></title>
	<description><![CDATA[<div>REViGO can take long lists of Gene Ontology terms and summarize them by removing redundant GO terms. The remaining terms can be visualized in semantic similarity-based scatterplots, interactive graphs, or tag clouds.&nbsp;<a href="http://dx.doi.org/10.1371/journal.pone.0021800">More about REViGO...</a>&nbsp;|&nbsp;<a href="http://revigo.irb.hr/about_hr.jsp"><img src="http://revigo.irb.hr/gfx/croatian-wCrown.png" alt="In Croatian" title="" width="12" height="15" style="border: 0px;"></a></div>
<div>Please enter a list of Gene Ontology IDs below, each on its own line. The GO IDs may be followed by p-values or another quantity which describes the GO term in a way meaningful to you.&nbsp;<img src="http://revigo.irb.hr/gfx/qmark.png" alt="For instance, you may provide a p-value          (statistical significance), a fold change, enrichment, or some          directly measured quantity such as average signal intensity from          microarrays, ion count from mass spec, or read count from RNA-seq.          You may also provide more than one value per line, although only the          first value will be used in GO term selection/clustering." title="" width="16" height="15" style="border: 0px;"></div><p>Address of the bookmark: <a href="http://revigo.irb.hr/" rel="nofollow">http://revigo.irb.hr/</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>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42204/g-nest-the-gene-neighborhood-scoring-tool</guid>
	<pubDate>Fri, 25 Sep 2020 20:09:18 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42204/g-nest-the-gene-neighborhood-scoring-tool</link>
	<title><![CDATA[G-NEST: The Gene NEighborhood Scoring Tool]]></title>
	<description><![CDATA[<p><span>The Gene NEighborhood Scoring Tool (G-NEST) combines genomic location, gene expression, and evolutionary sequence conservation data to score putative gene neighborhoods across all window sizes. Primary author of final code = William F. Martin. Example data files are in the separate repository.</span></p><p>Address of the bookmark: <a href="https://github.com/dglemay/G-NEST" rel="nofollow">https://github.com/dglemay/G-NEST</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<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>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38462/egad-ultra-fast-functional-analysis-of-gene-networks</guid>
	<pubDate>Fri, 14 Dec 2018 04:10:35 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38462/egad-ultra-fast-functional-analysis-of-gene-networks</link>
	<title><![CDATA[EGAD: Ultra-fast functional analysis of gene networks]]></title>
	<description><![CDATA[<p><span>With the EGAD (Extending &lsquo;Guilt-by-Association&rsquo; by Degree) package, we present a series of highly efficient tools to calculate functional properties in networks based on the guilt-by-association principle. These allow rapid controlled comparisons and analyses. Two of the core features are: a function prediction algorithm which is fully vectorized (neighbor_voting), allowing network characterization across even thousands of functional groups to be accomplished in minutes in cross-validation and an analytic determination of the optimal prior to guess candidates genes across multiple functional sets (calculate_multifunc, auc_multifunc).</span></p><p>Address of the bookmark: <a href="https://github.com/sarbal/EGAD" rel="nofollow">https://github.com/sarbal/EGAD</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41814/gggenes-a-ggplot2-extension-for-drawing-gene-arrow-maps</guid>
	<pubDate>Tue, 02 Jun 2020 11:43:34 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41814/gggenes-a-ggplot2-extension-for-drawing-gene-arrow-maps</link>
	<title><![CDATA[gggenes: a ggplot2 extension for drawing gene arrow maps.]]></title>
	<description><![CDATA[<p>Install the stable version of gggenes from CRAN:</p>
<p><code><a href="https://www.rdocumentation.org/packages/utils/topics/install.packages">install.packages("gggenes")</a></code></p>
<p>If you want the development version, install it from GitHub:</p>
<p><code><a href="https://www.rdocumentation.org/packages/devtools/topics/reexports">devtools::install_github("wilkox/gggenes")</a></code></p>
<p>More at&nbsp;<a href="https://github.com/wilkox/gggenes">https://github.com/wilkox/gggenes</a></p><p>Address of the bookmark: <a href="http://wilkox.org/gggenes" rel="nofollow">http://wilkox.org/gggenes</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42619/metaeuk-sensitive-high-throughput-gene-discovery-and-annotation-for-large-scale-eukaryotic-metagenomics</guid>
	<pubDate>Wed, 13 Jan 2021 19:29:32 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42619/metaeuk-sensitive-high-throughput-gene-discovery-and-annotation-for-large-scale-eukaryotic-metagenomics</link>
	<title><![CDATA[MetaEuk - sensitive, high-throughput gene discovery and annotation for large-scale eukaryotic metagenomics]]></title>
	<description><![CDATA[<p><span>MetaEuk is a modular toolkit designed for large-scale gene discovery and annotation in eukaryotic metagenomic contigs. Metaeuk combines the fast and sensitive homology search capabilities of&nbsp;</span><a href="https://github.com/soedinglab/MMseqs2">MMseqs2</a><span>&nbsp;with a dynamic programming procedure to recover optimal exons sets. It reduces redundancies in multiple discoveries of the same gene and resolves conflicting gene predictions on the same strand. MetaEuk is GPL-licensed open source software that is implemented in C++ and available for Linux and macOS. The software is designed to run on multiple cores.</span></p><p>Address of the bookmark: <a href="https://github.com/soedinglab/metaeuk" rel="nofollow">https://github.com/soedinglab/metaeuk</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>