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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/42204?offset=10</link>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/10380/ra-at-alagappa-university</guid>
  <pubDate>Sun, 04 May 2014 23:33:15 -0500</pubDate>
  <link></link>
  <title><![CDATA[RA at ALAGAPPA UNIVERSITY]]></title>
  <description><![CDATA[
<p>DEPARTMENT OF BIOTECHNOLOGY<br />(UGC SAP and DST-FIST &amp; PURSE Sponsored Department)<br />ALAGAPPA UNIVERSITY<br />(A State University Accredited by NAAC with „A‟ Grade)<br />Karaikudi - 630 004, India</p>

<p>WALK IN INTERVIEW</p>

<p>A walk-in Interview for the following position tenable at the Bioinformatics Infrastructure Facility (BIF), Department of Biotechnology, Alagappa University will be held at the Department of Biotechnology, Alagappa University, Karaikudi 630 003 on 15.05.2014 (Thursday) at 01:00 PM. This national facility is funded by the Department of Biotechnology, Ministry of Science and Technology, Government of India, New Delhi. The main objectives of the Centre involve teaching and research activities in bioinformatics/biotechnology.</p>

<p>RA (One Post):</p>

<p>Salary : Rs. 11000 p.m. plus admissible HRA</p>

<p>Qualification: M.Sc., in Bioinformatics/Biotechnology/Biophysics/Biochemistry/ Life Sciences</p>

<p>Interested candidates are encouraged to send their Curriculum Vitae by email to “sk_pandian@rediffmail.com” in advance. On the day of interview, the candidates must produce original certificates in proof of their educational qualification and experience and a recommendation letter from the Head of the Department/Institution where last studied/worked. Candidates who have already passed the required Degree alone are eligible to appear for interview. No TA&amp;DA will be given for attending the interview.</p>

<p>Advertisement: http://www.alagappabiotech.org/Walk%20in%20interview.pdf</p>
]]></description>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/14800/a-comprehensive-atlas-of-human-gene-activity-released</guid>
	<pubDate>Tue, 02 Sep 2014 14:20:24 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/14800/a-comprehensive-atlas-of-human-gene-activity-released</link>
	<title><![CDATA[A comprehensive atlas of human gene activity released !!!]]></title>
	<description><![CDATA[<div><div id="postDescription_4018558404"><p>A large international consortium of researchers has produced the first comprehensive, detailed map of the way&nbsp;<a href="http://www.hsph.harvard.edu/news/topic/genetics/" target="_blank">genes</a>&nbsp;work across the major cells and tissues of the human body. The findings describe the complex networks that govern gene activity, and the new information could play a crucial role in identifying the genes involved with disease.</p><p><img src="http://www.kurzweilai.net/images/Coexpression-clustering.jpg" alt="image" width="640" height="460" style="border: 0px; border: 0px;"></p><p>We are able to pinpoint the regions of the genome that can be active in a disease and in normal activity, whether it&rsquo;s in a brain cell, the skin, in blood stem cells or in hair follicles. This is a major advance that will greatly increase our ability to understand the causes of disease across the body.</p><p>The research is outlined in a series of papers published March 27, 2014, two in the journal&nbsp;<em>Nature</em>&nbsp;and 16 in other scholarly journals. The work is the result of years of concerted effort among 250 experts from more than 20 countries as part of&nbsp;<a href="http://fantom.gsc.riken.jp/" target="_blank">FANTOM 5 (Functional Annotation of the Mammalian Genome)</a>. The FANTOM project, led by the Japanese institution RIKEN, is aimed at building a complete library of human genes.</p><p>Researchers studied human and mouse cells using a new technology called Cap Analysis of Gene Expression (CAGE), developed at RIKEN, to discover how 95% of all human genes are switched on and off. These &ldquo;switches&rdquo; &mdash; called &ldquo;promoters&rdquo; and &ldquo;enhancers&rdquo; &mdash; are the regions of DNA that manage gene activity. The researchers mapped the activity of 180,000 promoters and 44,000 enhancers across a wide range of human cell types and tissues and, in most cases, found they were linked with specific cell types.</p><p>Referene : www.kurzweilai.net/first-comprehensive-atlas-of-human-gene-activity-released</p></div></div>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/26234/manolis-kellis-lab</guid>
  <pubDate>Sun, 31 Jan 2016 20:51:06 -0600</pubDate>
  <link></link>
  <title><![CDATA[Manolis Kellis Lab]]></title>
  <description><![CDATA[
<p>A major focus of our lab is understanding the effects of genetic variation on molecular phenotypes and human disease. We develop methods for integrating diverse functional genomic datasets of transcription, chromatin modifications, regulator binding, and their changes across multiple conditions to interpret genetic associations, identify causal variants, and predict the effects of genetic perturbations.</p>

<p>More at http://compbio.mit.edu</p>
]]></description>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28906/gene-finding-and-predictions</guid>
	<pubDate>Fri, 26 Aug 2016 07:26:27 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28906/gene-finding-and-predictions</link>
	<title><![CDATA[Gene Finding and Predictions]]></title>
	<description><![CDATA[<p><span>In this exercise, a previously annotated gene will be used to measure the accuracy of different gene finding approaches. GRAIL, GENSCAN,&nbsp;</span><tt>geneid</tt><span>, FGENESH, GenomeScan, GrailEXP and GENEWISE will be used to annotate the sequence. Both search by signal, content and homology (protein and cDNA sequences) methods will be employed in order to improve the ab initio results. Weak conservation of Start codons will lead to wrong prediction of initial exons in most cases.</span></p>
<p>http://genome.crg.es/courses/Bioinformatics2003_genefinding/</p><p>Address of the bookmark: <a href="http://genome.crg.es/courses/Bioinformatics2003_genefinding/" rel="nofollow">http://genome.crg.es/courses/Bioinformatics2003_genefinding/</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<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/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/news/view/4590/tigers-genome-sequenced</guid>
	<pubDate>Tue, 17 Sep 2013 16:48:24 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/4590/tigers-genome-sequenced</link>
	<title><![CDATA[Tigers genome sequenced]]></title>
	<description><![CDATA[<p>Fifteen scientists led by Dr Jong Bhak of Genome Research Foundation, South Korea, decoded as many as 3 billion nucleotides (organic molecules that form the basic building blocks of nucleic acids, such as DNA). They identified 20,000 genes related to various functions of the tiger.&nbsp;</p><p>The biggest and perhaps most fearsome of the world's big cats, the tiger, shares 95.6 percent of its DNA with humans' cute and furry companions, domestic cats.</p><p>The new research showed that big cats have genetic mutations that enabled them to be carnivores. The team also identified mutations that allow snow leopards to thrive at high altitudes.</p><p>Reference:</p><p><a href="http://www.nbcnews.com/science/your-cat-ferocious-tigers-share-lot-95-6-percent-their-4B11182690">http://www.nbcnews.com/science/your-cat-ferocious-tigers-share-lot-95-6-percent-their-4B11182690</a></p><p><a href="http://timesofindia.indiatimes.com/home/environment/flora-fauna/Gene-mapping-of-tiger-completed/articleshow/22671681.cms">http://timesofindia.indiatimes.com/home/environment/flora-fauna/Gene-mapping-of-tiger-completed/articleshow/22671681.cms</a></p><p>Paper:</p><p><a href="http://www.nature.com/ncomms/2013/130917/ncomms3433/full/ncomms3433.html">http://www.nature.com/ncomms/2013/130917/ncomms3433/full/ncomms3433.html</a></p>]]></description>
	<dc:creator>Rahul Agarwal</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>
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	<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>
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