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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/4590?offset=80</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/1973/webinar-wednesday-21-august-2013-at-noon-edt</guid>
	<pubDate>Sun, 11 Aug 2013 19:31:56 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/1973/webinar-wednesday-21-august-2013-at-noon-edt</link>
	<title><![CDATA[Webinar: Wednesday 21 August 2013 at Noon EDT]]></title>
	<description><![CDATA[<p>This webinar will describe the use of combinatorial pooling to reconstruct gene sequences within BACs. Recent work in barley has shown that this level of sequence knowledge is sufficient to support critical end-point objectives such as map-based cloning and marker-assisted breeding.</p><p>http://www.extension.org/pages/67926/upcoming-webinar:-selective-sequencing-through-combinatorial-pooling#.UggsVuHyPqU</p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/10392/research-associate-ra-at-institute-of-advanced-study-in-science-and-technology</guid>
  <pubDate>Mon, 05 May 2014 08:44:24 -0500</pubDate>
  <link></link>
  <title><![CDATA[Research Associate (RA) at INSTITUTE OF ADVANCED STUDY IN SCIENCE AND TECHNOLOGY]]></title>
  <description><![CDATA[
<p>INSTITUTE OF ADVANCED STUDY IN SCIENCE AND TECHNOLOGY<br />(An Autonomous Institute under Department of Science and Technology, Govt. of India)<br />Paschim Boragaon, Garchuk, Guwahati-781035</p>

<p>Appointment Adv.No.2</p>

<p>Applications in plain paper are invited from Indian citizens for one/two position each of Research Associate, Traineeship and Studentship for BIF facility, Division of Life Sciences, IASST.</p>

<p>Applications with complete Bio-data containing contact address, e-mail and phone number, two recent passport size photographs and attested copies of mark sheets, certificates etc., should be sent to the Registrar, IASST, Paschim Boragaon, Garchuk, Guwahati – 781035, Assam, so as to reach on or before 5/05/2014.</p>

<p>A. Research Associate:</p>

<p>Number of vacancies: 1 (One)</p>

<p>Qualifications:</p>

<p>PhD in Bioinformatics or allied disciplines with knowledge of Bioinformatics. The candidates who have submitted PhD thesis may also apply.</p>

<p>In case, candidates having PhD are not found, candidates having MSc in Bioinformatics or allied disciplines with sound knowledge of Bioinformatics will be preferred.</p>

<p>Remuneration: Candidate having PhD will get a consolidated remuneration of Rs. 22,000/- +HRA per month. MSc having NET/GATE/SLET qualified candidate will get a remuneration of Rs. 16,000/= and HRA and candidate with only MSc will get a remuneration of Rs.14,000/- and HRA.</p>

<p>Tenure:</p>

<p>The post is initially for one year and may be extended depending on the performance till the tenure of the project.</p>

<p>B. Traineeship:</p>

<p>Number of vacancies: 2 (Two)</p>

<p>Qualifications:</p>

<p>Candidate with a postgraduate degree in Bioinformatics/Biotechnology/Life sciences from a recognised University</p>

<p>Remuneration: Rs. 5000/month for 6 months</p>

<p>C. Studentship:</p>

<p>Number of vacancies: 2 (Two)</p>

<p>Qualifications:</p>

<p>Candidate pursuing M.Sc in bioinformatics in a recognised University.</p>

<p>Remuneration: Rs. 5000/month for 6 months</p>

<p>Advertisement:</p>

<p>http://iasst.gov.in/pdf/recruitment/advt%20no_2_24042014.pdf</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/11195/ncbi-gene-screencast</guid>
	<pubDate>Fri, 30 May 2014 06:21:18 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/11195/ncbi-gene-screencast</link>
	<title><![CDATA[NCBI Gene Screencast]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/WyFIf7YdM8A" frameborder="0" allowfullscreen></iframe>A short walkthrough of the NCBI Gene page]]></description>
	
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	<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>
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	<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>
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	<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>
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	<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>
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	<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>
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	<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>
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	<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>
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