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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/31302?offset=1070</link>
	<atom:link href="https://bioinformaticsonline.com/related/31302?offset=1070" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43923/monkeypox-virus-isolate-mpxv-usa-2022-ma001-complete-genome</guid>
	<pubDate>Tue, 26 Jul 2022 06:21:07 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43923/monkeypox-virus-isolate-mpxv-usa-2022-ma001-complete-genome</link>
	<title><![CDATA[Monkeypox virus isolate MPXV_USA_2022_MA001, complete genome]]></title>
	<description><![CDATA[<pre>LOCUS       ON563414              197205 bp    DNA     linear   VRL 30-MAY-2022
DEFINITION  Monkeypox virus isolate MPXV_USA_2022_MA001, complete genome.
ACCESSION   ON563414
VERSION     ON563414.3
KEYWORDS    .
SOURCE      Monkeypox virus (monkeypox)
  ORGANISM  <a href="https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?id=10244">Monkeypox virus</a>
            Viruses; Varidnaviria; Bamfordvirae; Nucleocytoviricota;
            Pokkesviricetes; Chitovirales; Poxviridae; Chordopoxvirinae;
            Orthopoxvirus.</pre><p>Address of the bookmark: <a href="https://www.ncbi.nlm.nih.gov/nuccore/ON563414" rel="nofollow">https://www.ncbi.nlm.nih.gov/nuccore/ON563414</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/7387/bioinformatics-software-for-biologists-in-the-genomics-era</guid>
	<pubDate>Sun, 22 Dec 2013 17:31:05 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/7387/bioinformatics-software-for-biologists-in-the-genomics-era</link>
	<title><![CDATA[Bioinformatics software for biologists in the genomics era]]></title>
	<description><![CDATA[<p>The genome sequencing revolution is approaching a landmark figure of 1000 completely sequenced genomes. Coupled with fast-declining, per-base sequencing costs, this influx of DNA sequence data has encouraged laboratory scientists to engage large datasets in comparative sequence analyses for making evolutionary, functional and translational inferences. However, the majority of the scientists at the forefront of experimental research are not bioinformaticians, so a gap exists between the user-friendly software needed and the scripting/programming infrastructure often employed for the analysis of large numbers of genes, long genomic segments and groups of sequences. We see an urgent need for the expansion of the fundamental paradigms under which biologist-friendly software tools are designed and developed to fulfill the needs of biologists to analyze large datasets by using sophisticated computational methods. We argue that the design principles need to be sensitive to the reality that comparatively small teams of biologists have historically developed some of the most popular biological software packages in molecular evolutionary analysis. Furthermore, biological intuitiveness and investigator empowerment need to take precedence over the current supposition that biologists should re-tool and become programmers when analyzing genome scale datasets.</p><p>Address of the bookmark: <a href="http://bioinformatics.oxfordjournals.org/content/23/14/1713.full" rel="nofollow">http://bioinformatics.oxfordjournals.org/content/23/14/1713.full</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44483/baclife-an-automated-genome-mining-tool-for-identification-of-lifestyle-associated-genes</guid>
	<pubDate>Fri, 15 Mar 2024 04:59:14 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44483/baclife-an-automated-genome-mining-tool-for-identification-of-lifestyle-associated-genes</link>
	<title><![CDATA[bacLIFE: an automated genome mining tool for identification of lifestyle associated genes]]></title>
	<description><![CDATA[<p style="margin-top: 0px; margin-bottom: 16px; color: #1f2328; font-size: 16px; font-style: normal; font-weight: 400; text-align: start; background-color: #ffffff;" dir="auto">bacLIFE is a streamlined computational workflow that annotates bacterial genomes and performs large-scale comparative genomics to predict bacterial lifestyles and to pinpoint candidate genes, denominated<span>&nbsp;</span><strong style="font-weight: var(--base-text-weight-semibold, 600);">lifestyle-associated genes (LAGs)</strong>, and biosynthetic gene clusters associated with each lifestyle detected. This whole process is divided into different modules:</p>
<ul style="margin-top: 0px; margin-bottom: 16px; color: #1f2328; font-size: 16px; font-style: normal; font-weight: 400; text-align: start; background-color: #ffffff;" dir="auto">
<li><strong style="font-weight: var(--base-text-weight-semibold, 600);">Clustering module</strong><span>&nbsp;</span>Predicts, clusters and annotates the genes of every input genome</li>
<li style="margin-top: 0.25em;"><strong style="font-weight: var(--base-text-weight-semibold, 600);">Lifestyle prediction</strong><span>&nbsp;</span>Employs a machine learning model to forecast bacterial lifestyle or other specified metadata</li>
<li style="margin-top: 0.25em;"><strong style="font-weight: var(--base-text-weight-semibold, 600);">Analitical module (Shiny app)</strong><span>&nbsp;</span>Results from the previous modules are embedded in a user-friendly interface for comprehensive and interactive comparative genomics.</li>
</ul>
<p style="margin-top: 0px; margin-bottom: 16px; color: #1f2328; font-size: 16px; font-style: normal; font-weight: 400; text-align: start; background-color: #ffffff;" dir="auto">You can find the complete wiki here [<a href="https://github.com/Carrion-lab/bacLIFE/wiki/bacLIFE-wiki">https://github.com/Carrion-lab/bacLIFE/wiki/bacLIFE-wiki</a>]</p><p>Address of the bookmark: <a href="https://github.com/Carrion-lab/bacLIFE" rel="nofollow">https://github.com/Carrion-lab/bacLIFE</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/7753/jrf-pondicherry-university</guid>
  <pubDate>Fri, 03 Jan 2014 16:48:56 -0600</pubDate>
  <link></link>
  <title><![CDATA[JRF @ PONDICHERRY UNIVERSITY]]></title>
  <description><![CDATA[
<p>PONDICHERRY UNIVERSITY</p>

<p>CENTRE FOR BIOINFORMATICS</p>

<p>PUDUCHERRY</p>

<p>Applications are invited for one Project Assistant to work in the UGC sponsored Research Award "Molecular Docking and Dynamics studies to understand the interacting mechanism of oncogenic 101 protein with its cellular proteins".</p>

<p>The duration for the fellowship is 12months only with consolidated pay ofRs. 5,000 per month.</p>

<p>Application on plain paper with following details: Name, Address, Data of Birth, Father's Name, Nationality, Educational Qualification (SSLC onwards-enclose attested copies of certificate) and Researcb Experience may be addressed to Dr. R. Krishna, Principle Investigator (PI), UGC Research Award, Centre for Bioinformatics, Pondicherry University, Pondicherry - 605 014.</p>

<p>Application should reach in January 261h , 2013.</p>

<p>Essential Qualification: M.Sc. in Bioinformatics/Biophysics with good academic record.</p>

<p>Qualification for Project Fellow:</p>

<p>M.Sc in Bioinformatics/Biophysics.</p>

<p>The person to be considered for appointment as Project Fellow must have second class master degree with a minimum of 55% marks in the subject concerned or a related subject.</p>

<p>The candidate to be appointed as Project Fellows should be below thc age of40 years at the time of appointment.</p>

<p>Desirable Qualification for this Project: Research Experience in Small/Macromolecule Crystallography and Structural Bioinformatics.</p>

<p>For more details, refer the web site: www.pondiuni.edu.in/sites/default/files/BIC-311213.pdf</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/45376/can-ai-decide-which-genomic-model-to-trust</guid>
	<pubDate>Sat, 24 Oct 2026 03:10:06 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/45376/can-ai-decide-which-genomic-model-to-trust</link>
	<title><![CDATA[Can AI Decide Which Genomic Model to Trust?]]></title>
	<description><![CDATA[<p>Imagine you're sitting with a genomic dataset of some sort (thousands of SNPs), a set of plant phenotypes and the one very simple question: can we predict what these plants will become? The first thing you'd probably do is reach for machine learning. That's where the real problem lies. Which model do we use? GBLUP? Bayesian ridge? LASSO? Random Forest? A neural net? How do we know that the model that looks so good on a single cross-validation split will still be good tomorrow? In genomic prediction we're often just left to pick these things manually. The problem is that a single cross-validation split can make a model look incredibly good when in reality it's not. This is the problem that Nagendra Pratap Singh and Venugopal Mendu have set out to solve with GenoBridge (https://github.com/nps-genomics/GenoBridge), a sample size adaptive machine learning pipeline designed to reduce the amount of human guesswork involved. The paper (Plant Methods, September 2026) describes a pipeline that instead of asking the user to pick a model and parameters in advance, adapts the analysis based on the size of the sample. It tests multiple models and evaluates them via cross-validation. The premise is simple but powerful: don't just ask if a model can predict, ask if the model can predict when the data it's being tested on are genuinely unseen. GenoBridge therefore places a strong emphasis on cross-validated R2 as well as correlation, using the reproducibility of the predictive signal as a gate before moving into GWAS. In other words prediction is the first checkpoint: if the genomic signal can't generalise then we shouldn't be trusting the downstream association results. Once a trait has passed that checkpoint it proceeds into kinship based mixed model GWAS to provide a bridge between prediction and biological interpretation.</p><p>The authors tested the pipeline across 5 datasets from 4 species, spanning an almost 80-fold difference in sample size (from 136 to over 10,700 individuals) and found that the predictive accuracy was within 0.02-0.05 of the standard GBLUP and Bayesian ridge regression approaches, again without the user having to manually select a model. More importantly the pipeline was able to recover known biological signals (FLC, FT and DOG1 in Arabidopsis, Waxy and GW5 in rice and Glu-D1 in wheat) and produced well calibrated association statistics. The most interesting observation though is that prediction accuracy and biological association are not necessarily the same thing. Two traits with identical predictive performance could have association significance that varied by over 60 orders of magnitude. So knowing how well we can predict a trait doesn't necessarily tell us anything about the distribution of the genetic architecture of that trait across the genome. That's where GenoBridge is actually of value. It's not just another machine learning pipeline. It's an example of an adaptive analytical workflow that doesn't just throw algorithms at the data but asks whether the data actually justify the next analytical step. As the amount of genomic data we generate continues to grow and the number of computational methods available to us increases, the bottleneck may not be in finding the right algorithm at all. The bottleneck may be in deciding which algorithm we can actually trust.</p><p>GenoBridge offers a potential solution: let the data tell you which model is appropriate, validate the prediction rigorously and only then move from prediction to biological discovery. That may be where computational genomics is heading. Rather than a future in which there is one single 'best' model, we may be heading towards a future of intelligent systems that know when a model is appropriate, when a result is reliable and when the biology is actually telling us something that we should be listening to.</p><p>More at&nbsp;https://link.springer.com/article/10.1186/s13007-026-01596-5</p>]]></description>
	<dc:creator>BioStar</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/7998/faculty-positions-at-iiit-allahabad</guid>
  <pubDate>Thu, 23 Jan 2014 06:19:34 -0600</pubDate>
  <link></link>
  <title><![CDATA[FACULTY POSITIONS AT IIIT-ALLAHABAD]]></title>
  <description><![CDATA[
<p>OPENINGS OF FACULTY POSITIONS AT IIIT-ALLAHABAD</p>

<p>(Under Tenure-Track Model)</p>

<p>Open Advt. No IIITA/DIC/16012014</p>

<p>IIIT-Allahabad has several Openings for the Faculty positions at the Assistant Professor level.</p>

<p>It is a regular tenure-track faculty positions for 3-5 years in teaching and research. A regular faculty is expected to engage heavily in research and teaching. The eligibility criteria for regular faculty positions are similar as in IITs. For an Assistant Professor position, a candidate must have a PhD (in IT/Computer Science &amp;/or Engineering/Electrical, Electronics &amp;/or Communication Engineering/etc; for interdisciplinary areas the PhD may be in an appropriate field), plus three years experience. However, for PhDs from a well known University/Institute (e.g. IITs/IISc/TIFR/ISI in India or well known research universities across the world), and a good research/academic record, the 3 years experience requirement may be waived.</p>

<p>The pay scale for faculty is same as in IITs. Other benefits include initiation research grant, travel support, book grant, professional society membership, etc., and personal benefits such as medical/LTC, on campus subsidized family housing with excellent modern infrastructural facilities.</p>

<p>Areas of Interest</p>

<p>IIIT-Allahabad aims to build strong research groups in important and emerging areas in CS/IT/ECE as well as in emerging interdisciplinary areas, and applications are invited in all these areas. Some of the areas of special interest, besides strengthening the existing research areas, are : Software Engineering, Theoretical Computer Science, Cyber Physical Systems, Robotics, Network science, Digital Media, Computational neuroscience, Machine learning, Healthcare informatics, Computational Biology, Communications networks (both at hardware and protocol levels), Circuits (including VLSI, analog, low power, etc), Energy systems and technologies, Biomedical electronics and systems, Computer Architecture, signal/image processing, Embedded and control systems.</p>

<p>Application Process</p>

<p>Interested candidates can apply by sending their detailed CV with list of publications clearly mentioning Journal names and citation index with three references through email entitled “Faculty positions at IIIT Allahabad” to faculty.applications@iiita.ac.in. Do not send your applications in any other email addresses. Applications will be considered regularly, hence there is no deadline for applying.</p>

<p>Important Clarifications on Eligibility</p>

<p>A PhD in CS/IT (or other disciplines, as announced) is the minimum expected requirement for an Assistant Professor.</p>

<p>Advertisement: http://iiita.ac.in/pub/Faculty-Position-IIITA1.pdf</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/4183/320000-viruses-in-mammals-yet-to-sequenced-in-future</guid>
	<pubDate>Tue, 03 Sep 2013 08:35:30 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/4183/320000-viruses-in-mammals-yet-to-sequenced-in-future</link>
	<title><![CDATA[320000 viruses in mammals yet to sequenced in future!!!]]></title>
	<description><![CDATA[<p>With current biological technique improvements, finally it is now possible to look at millions of unknown viruses at genomic level and understand the mechanism. According to available data, close to 70 per cent of emerging viral diseases such as HIV/AIDS, West Nile, Ebola, SARS, and influenza, are zoonoses - infections of animals that cross into humans.</p><p>To address the challenges of describing and estimating virodiversity, a team of investigators from Center for Infection and Immunity (CII) and EcoHealth Alliance began in jungles of Bangladesh - home to the flying fox.</p><p>Reference:</p><p><a href="http://economictimes.indiatimes.com/news/news-by-industry/et-cetera/mammals-harbour-at-least-320000-new-viruses/articleshow/22253268.cms">http://economictimes.indiatimes.com/news/news-by-industry/et-cetera/mammals-harbour-at-least-320000-new-viruses/articleshow/22253268.cms</a></p><p><a href="http://www.bbc.co.uk/news/science-environment-23932400">http://www.bbc.co.uk/news/science-environment-23932400</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/4574/tools-to-detect-synteny-blocks-regions-among-multiple-genomes</guid>
	<pubDate>Mon, 16 Sep 2013 17:12:02 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/4574/tools-to-detect-synteny-blocks-regions-among-multiple-genomes</link>
	<title><![CDATA[Tools to detect synteny blocks regions among multiple genomes]]></title>
	<description><![CDATA[<p>The synteny block (which etymologically means &ldquo;on the same ribbon&rdquo;) is a collection of contiguous genes located on the same chromosome. These block regions have mostly been preserved by genome rearrangements, and so synteny blocks from two related species (e.g., humans and mice) will be roughly similar but flipped around on the respective genomes. Ovcharenko et. al. define it as &lsquo;any conserved sequence blocks, regardless of whether it encompasses multiple genes, an area containing single genes, or areas devoid of known genes to be considers as synteny block as long as there is conservation at the sequence level. Today, however, biologists usually refer to synteny as the conservation of blocks of order within two sets of chromosomes that are being compared with each other. This concept can also be referred to as shared synteny. The NHBLI/NCBI Glossary define synteny as &ldquo;Two genes which occur on the same chromosome are syntenic; however, syntenic genes may or may not be "linked."</p><p>Now a day, geneticists have developed a language of their own. They are pouring lots of money and energy to read the entire genomic text and understand the gods own code ATGC. It is somewhat fascinating, not only for geneticist but also for non-biologist to know that there are several conserved blocks in genome which remain conserved over hundreds of millions of years. There have been several researches on conserved blocks and non-conserved regions to understand the mechanism and importance of all these regions (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2675965/). The finding indicates conservation and rearrangements of certain evolutionary important genes play an important role in evolution/adaptive changes (http://www.nature.com/nature/journal/v491/n7424/abs/nature11622.html https://academic.oup.com/gbe/article/8/8/2442/2198198/Novel-Insights-into-Chromosome-Evolution-in-Birds , http://science.sciencemag.org/content/346/6215/1311).</p><p>But the puzzle remains open, how to correctly define the synteny (presence of two or more genes on the same chromosome) and conserved synteny (presence of two or more genes on chromosome of each of the two species) on several genomes.</p><p><img src="http://bioinformaticsonline.com/mod/photo/syntenyImg.jpg" alt="image" width="720" height="179" style="border: 0px; border: 0px;"></p><p>Figure: Image generated with Evolution Highway (EH) tool http://eh-demo.ncsa.illinois.edu/&nbsp;</p><p>Keeping the new approach to define conserved synteny in mind there have been various algorithms developed to identify the conserved homologous synteny blocks (HSB) amongst species. Some of them which were commonly used for synteny detections are:</p><p>SyntenyTracker ( http://www-app.igb.uiuc.edu/labs/lewin/donthu/Synteny_assign/html/),</p><p>SyntenyTracker was shown to be an efficient and accurate automated tool for defining HSBs using datasets that may contain minor errors resulting from limitations in map construction methodologies.</p><p>CoGe (http://genomevolution.org/CoGe/SynFind.pl )</p><p>Satsuma (http://evomics.org/learning/genomics/satsuma/)</p><p>Cinteny (http://cinteny.cchmc.org/) ,</p><p>Cinteny server can be used for finding regions syntenic across multiple genomes and measuring the extent of genome rearrangement using reversal distance as a measure.</p><p>OrthoCluster (http://krono.act.uji.es/noticias/orthocluster-a-new-tool-for-mining-syntenic-blocks)</p><p>A new tool for mining syntenic blocks in comparative genomics</p><p>SynMap (http://genomevolution.org/wiki/index.php/SynMap),</p><p>SyMAP (http://www.symapdb.org/)</p><p>SyMAP (Synteny Mapping and Analysis Program) v4.0 is an automated system for identifying and displaying genome synteny alignments. The genomes may be represented by sequenced chromosomes (pseudomolecules), by draft sequence contigs, or by FPC physical maps (with BAC-end or marker sequence).</p><p>http://genomevolution.org/CoGe/SynMap.pl</p><p>RegionMiner (http://www.genomatix.de/online_help/help_regionminer/orthologous.html)</p><p>SyntenyMiner is being developed as an application to visualize and interrogate comparisons among multiple complete genome sequences. http://syntenyminer.sourceforge.net/</p><p>AutoGRAPH ( http://autograph.genouest.org/),</p><p>AutoGRAPH is an integrated web server for multi-species comparative genomic analysis. It is designed for constructing and visualizing synteny maps between two or three species, determination and display of macrosynteny and microsynteny relationships among species, and for highlighting evolutionary breakpoints.</p><p>SynChro(http://www.lgm.upmc.fr/CHROnicle/SynChro.html)</p><p>SynChro is a tool designed to define conserved synteny blocks. It reconstructs synteny blocks between pairwise comparison of multiple genomes. The reconstructed synteny blocks may overlap each other, be included in one another or duplicated due to micro-rearrangements.</p><p>SyntenyView ( http://www.cbs.dtu.dk/dtucourse/cookbooks/nikob/exercises/gf1_output_5.html),</p><p>Ensembl 'SyntenyView' shows conservation of large-scale gene order between species pairs. A brief summary of the calculation method appears at the bottom of this help page.&nbsp; The left of a 'SyntenyView' page displays a diagram of chromosomes with blocks of conserved synteny. The right of a page shows homology matches between individual genes within syntenic blocks.</p><p>SynBrowse ( http://www.synbrowse.org/),</p><p>SynBrowse (Synteny Browser) is a generic sequence comparison tool for visualizing genome alignments both within and between species. It is intended to help scientists study and analyze synteny, homologous genes and other conserved elements between sequences. This software is useful in studying genome duplication and evolution. It can also aid in identifying uncharacterized genes, putative regulatory elements and novel structural features of study species by comparing to a well annotated reference sequence, thus enabling genome curators to refine and edit annotations of species that have incomplete genome annotations.</p><p>Sibelia (http://arxiv.org/abs/1307.7941).</p><p>A comparative genomic tool: It assists biologists in analysing the genomic variations that correlate with pathogens, or the genomic changes that help microorganisms adapt in different environments. Sibelia will also be helpful for the evolutionary and genome rearrangement studies for multiple strains of microorganisms.</p><p>GSV (http://cas-bioinfo.cas.unt.edu/gsv/homepage.php)</p><p>Genome Synteny Viewer allows users to upload files which contain synteny regions between two or more genomes and interactively visualize the synteny between them. GSV also allows users to upload annotation files to visualize annotated regions in addition to synteny regions.</p><p>MicroSyn (http://www.lgm.upmc.fr/CHROnicle/SynChro.html)</p><p>MicroSyn software as a means of detecting microsynteny in adjacent genomic regions surrounding genes in gene families. MicroSyn searches for conserved, flanking colinear homologous gene pairs between two genomic fragments to determine the relationship between two members in a gene family.</p><p>SynOrth (http://synorth.genereg.net/)</p><p>Synorth [s n &ocirc;rth], named in combination of "synteny" and "ortholog", is designed for the study of evolutionary changes of genomic regulatory blocks (GRBs) in vertebrate genomes, and especially the changes following the whole-genome duplication in teleost fish, by tracing the ortholog genes gain and loss in ancient synteny blocks.</p><p>SyDiG (http://www.ncbi.nlm.nih.gov/pubmed/21441096)</p><p>Uncovering Synteny in Distant Genomes.</p><p>MapSynteny&nbsp; (http://www.automatizacionysistemas.com/download.html)</p><p>MapSynteny is a macro in MS Excel&reg; able to create images to show the relationship between genetic maps and large sequences (scaffolds, chromosomes, BACs, etc.). Based on tab &ndash; delimited BLAST results and some formulas, a suitable image of syntenic relationships or physical mapping can be obtained. http://www.automatizacionysistemas.com/Poster_MapSynteny.pdf</p><p>One of the best synteny tutorial for beginer @&nbsp;http://www.nature.com/scitable/topicpage/synteny-inferring-ancestral-genomes-44022</p><p>Reference:</p><p><a href="http://www.nature.com/scitable/topicpage/synteny-inferring-ancestral-genomes-44022">http://www.nature.com/scitable/topicpage/synteny-inferring-ancestral-genomes-44022</a></p><p><a href="http://www.nature.com/nature/journal/v491/n7424/full/nature11622.html">http://www.nature.com/nature/journal/v491/n7424/full/nature11622.html</a></p><p><a href="http://en.wikipedia.org/wiki/Synteny">http://en.wikipedia.org/wiki/Synteny</a></p><p><a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2675965/">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2675965/</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/8198/scientist-positions-at-rajiv-gandhi-centre-for-biotechnology</guid>
  <pubDate>Thu, 06 Feb 2014 23:18:49 -0600</pubDate>
  <link></link>
  <title><![CDATA[Scientist Positions at Rajiv Gandhi Centre for Biotechnology]]></title>
  <description><![CDATA[
<p>Rajiv Gandhi Centre for Biotechnology</p>

<p>An Autonomous National Institute under Government of India,<br />Ministry of Science &amp; Technology<br />Department of Biotechnology</p>

<p>No: RGCB/ Advt./2014/1   <br />January 24, 2014</p>

<p>Scientist Positions</p>

<p>Group Leader in Computational Biology/Bioinformatics<br />A highly motivated and innovative individual who will pursue basic research, solve biological problems with emphasis on computational and quantitative experimental methods and build active bridges to translational research. The scientist will also provide computational biology support to analyze complex data sets generated by RGCB scientists and collaborators.</p>

<p>Location: Thiruvananthapuram (Trivandrum)</p>

<p>The above positions will be at the E-II, F or equivalent levels. For senior applicants with an outstanding track record, an option of a contract career path for research excellence at Scientist G or H equivalent level can also be discussed. All positions will initially be for 5 years. Essential and desired qualifications as well as other relevant details for all the above positions are posted on the RGCB website (http://www.rgcb.res.in). The last date for receiving applications is March 14, 2014.   </p>

<p>Sd/-<br />Director</p>

<p>Rajiv Gandhi Centre for Biotechnology<br />Thycaud, P.O., Poojappura,<br />Thiruvananthapuram, Kerala, India-695 014<br />Ph.: 91-471-2529400 (30 Lines), 2347975, 2348104, 2348753, 2345899<br />Fax: 91-471-2348096, 2346333</p>

<p>More at http://rgcb.res.in/jobs.html</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/file/view/8650/bioinformatician-duties-and-jobs</guid>
	<pubDate>Wed, 05 Mar 2014 14:32:26 -0600</pubDate>
	<link>https://bioinformaticsonline.com/file/view/8650/bioinformatician-duties-and-jobs</link>
	<title><![CDATA[Bioinformatician duties and jobs !!!]]></title>
	<description><![CDATA[<p><span><em>Needle</em> in a haystack</span> ... ohh yes this is what bioinformatician do. We handle and analyse, Terabytes and Petabytes of genomic data on daily basis.</p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
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