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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/30336?offset=1120</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/file/view/39260/biotechnology-eligibility-test-bet-question-paper</guid>
	<pubDate>Wed, 17 Apr 2019 04:56:33 -0500</pubDate>
	<link>https://bioinformaticsonline.com/file/view/39260/biotechnology-eligibility-test-bet-question-paper</link>
	<title><![CDATA[Biotechnology Eligibility Test (BET) Question Paper !]]></title>
	<description><![CDATA[<p><span>DBT-BET JRF 2019 Exam was held successfully on 14th April 2019. Official Question Paper &amp; Answer key of&nbsp;DBT-BET 2019 Exam has been released. </span></p><p><span>The best part about the DBT-BET Exam is both the question paper &amp; answer key is released immediately after the exam so you get the upper hand here to check how well you did in the DBT-BET exam, how much you are going to score and if you can pass the Exam with good score. This helps you in planning your career ahead. </span></p><p><span>DBT Exam is held once in a year by Department of Biotechnology &ndash; Govt of India.&nbsp;Candidates willing to get DBT-Junior Research Fellowship &ndash; DBT JRF fellowship should take up the&nbsp; DBT-BET Exam, DBT &ndash; Biotechnology Eligibility Test (BET) 2020 to be held on somewhere in the month of April. Official Notification for DBT-BET 2020 will be released in Feb 2020.</span></p><p><span>Answer at&nbsp;<a href="https://bioinformaticsonline.com/file/view/39262/biotechnology-eligibility-test-bet-answer-set">https://bioinformaticsonline.com/file/view/39262/biotechnology-eligibility-test-bet-answer-set</a></span></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
	<enclosure url="https://bioinformaticsonline.com/file/download/39260" length="524574" type="application/pdf" />
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/10773/bioinformatics-jrfsrf-position-at-national-research-centre-on-plant-biotechnology</guid>
  <pubDate>Sun, 11 May 2014 22:29:12 -0500</pubDate>
  <link></link>
  <title><![CDATA[Bioinformatics JRF/SRF position at NATIONAL RESEARCH CENTRE ON PLANT BIOTECHNOLOGY]]></title>
  <description><![CDATA[
<p>NATIONAL RESEARCH CENTRE ON PLANT BIOTECHNOLOGY<br />LBS, CENTRE, PUSA CAMPUS, IARI NEW DELHI<br />NEW DELHI – 110 012</p>

<p>WALK- IN –INTERVIEWS</p>

<p>Eligible candidates may appear in Walk-in-Interview on May 23, 2014 at 10 AM for the posts of Research Associates &amp; Senior Research Fellows (SRF) in the following DST/DBT/ICAR funded projects.</p>

<p>1 NPTC Project on Bioinformatics and Comparative Genomics</p>

<p>Research Associate (One)</p>

<p>Rs. 24000/- + 30% HRA for masters degree holder with more than 4 years experience</p>

<p>Essential: Ph D in Plant Molecular Biology &amp; Biotechnology/Genetics 0r Candidates who have already submitted their Ph D thesis in above subjects</p>

<p>Desirable: Research experience in Genomics, Molecular biology, Microarrays analysis, Gene cloning, transgenic Techniques , and computational analysis.</p>

<p>Senior Research Fellow ( UGCCSIR/ DBT/ ICAR Net qualified only): (One)</p>

<p>Rs. 16000/- + 30% HRA and Rs. 18000+30 HRA from 3rd year onwards</p>

<p>Essential:</p>

<p>1. ICAR/ UGCCSIR/DBT Net qualified only</p>

<p>2. M. Sc. (with thesis) in Biotechnology, Life Sciences, Biosciences/ Bioinformatics, Genetics/ Plant Pathology with experience in molecular biology.</p>

<p>Or M.Sc with more than 3 years research experiences</p>

<p>3. B.Sc. Agriculture or Biology</p>

<p>Desirable:<br />1. M. Sc. with thesis<br />2. Experience in molecular biology, plant tissue culture<br />3. Bioinformatics knowledge is important</p>

<p>2 DST JC Bose National Fellowship</p>

<p>Research Associate (Bioinformatics) : One</p>

<p>Rs.22000/- + 30% HRA for 1 &amp; 2nd Yr., Rs. 23000+ 30% HRA for 3rd year and Rs. 24000+30% HRA for 4th &amp;5th yr</p>

<p>Essential: M Ph D in Plant Molecular Biology &amp; Biotechnology/Genetics</p>

<p>Desirable: Research experience in Genomics, Molecular biology, Microarrays analysis, Gene cloning, transgenic Techniques , and computational analysis.</p>

<p>Age limit: Max.35 years (Age relaxation of 5 years for SC/ST &amp; women and 3 years for OBC)</p>

<p>The posts are purely temporary in nature and are co-terminus with the project. Initially the offer will be made for one year only and may be further extendable based on performance of the candidate. The interview will be held on May 23 , 2014 at 10:00 AM at NRCPB, LBS Building, Pusa Campus, IARI, New Delhi- 110012. The candidates must bring four copies of biodata (in the prescribed proforma), original certificates, attested photocopies of each of the certificates and an attested copy of recent passport size photograph. No. TA/DA would be given for the appearance in interview. Only the candidates having essential qualification would be entertained for the interviews. Short-listing of candidates based on academic merit and experience will be done in case of large number of applicants.</p>

<p>Advertisement: http://www.nrcpb.org/sites/default/files/Advertisement%20for%20RA%20and%20SRF%20Position.pdf</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41442/gsp4pdb-a-web-tool-to-visualize-search-and-explore-protein-ligand-structural-patterns</guid>
	<pubDate>Sun, 15 Mar 2020 03:41:12 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41442/gsp4pdb-a-web-tool-to-visualize-search-and-explore-protein-ligand-structural-patterns</link>
	<title><![CDATA[GSP4PDB: a web tool to visualize, search and explore protein-ligand structural patterns]]></title>
	<description><![CDATA[<p><span><span>GSP4PDB is a user-friendly and efficient application to search and discover new patterns of protein-ligand interaction.</span></span></p>
<p><span>GSP4PDB</span><span>&nbsp;is part of the services provided by the&nbsp;</span><a href="https://structuralbio.utalca.cl/" target="_blank">Bioinformatic Group</a><span>&nbsp;of the&nbsp;</span><a href="http://www.utalca.cl/" target="_blank">University of Talca</a></p>
<p><a href="http://gdblab.com/gsp4pdb/gsp4pdb2/">http://gdblab.com/gsp4pdb/gsp4pdb2/</a></p>
<p>https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-3352-x</p><p>Address of the bookmark: <a href="http://gdblab.com/gsp4pdb/gsp4pdb2/" rel="nofollow">http://gdblab.com/gsp4pdb/gsp4pdb2/</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/12943/a-history-of-bioinformatics-in-the-year-2039</guid>
	<pubDate>Wed, 23 Jul 2014 06:37:51 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/12943/a-history-of-bioinformatics-in-the-year-2039</link>
	<title><![CDATA[A History of Bioinformatics (in the Year 2039)]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/uwsjwMO-TEA" frameborder="0" allowfullscreen></iframe><p>C. Titus Brown http://video.open-bio.org/video/1/a-history-of-bioinformatics-in-the-year-2039</p>]]></description>
	
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34504/minion-gc-an-r-script-to-do-some-qc-on-minion-data</guid>
	<pubDate>Sun, 03 Dec 2017 15:19:18 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34504/minion-gc-an-r-script-to-do-some-qc-on-minion-data</link>
	<title><![CDATA[MinION_GC: An R script to do some QC on MinION data]]></title>
	<description><![CDATA[<p><span>Other tools focus on getting data out of the fastq or fast5 files, which is slow and computationally intensive. The benefit of this approach is that it works on a single, small, .txt summary file. So it's a lot quicker than most other things out there: it takes about a minute to analyse a 4GB flowcell on my laptop.</span></p>
<p>https://github.com/roblanf/minion_qc</p><p>Address of the bookmark: <a href="https://github.com/roblanf/minion_qc" rel="nofollow">https://github.com/roblanf/minion_qc</a></p>]]></description>
	<dc:creator>Radha Agarkar</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/11144/scientists-map-17294-proteins-produced-in-human-body</guid>
	<pubDate>Thu, 29 May 2014 01:57:55 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/11144/scientists-map-17294-proteins-produced-in-human-body</link>
	<title><![CDATA[Scientists map 17,294 proteins produced in human body]]></title>
	<description><![CDATA[<p>Indian scientists missed the genomic profiling bus, but they've more than made up for it by creating the first human proteome map which is an extension of the genomic study. Till now, here is no direct equivalent for the human proteome. But recently two groups present mass spectrometry-based analysis of human tissues, body fluids and cells mapping the large majority of the human proteome.</p><p>The Indian scientists working in Bangalore, along with their American counterparts, have mapped more than 17,000 proteins in 30 organs of the human body. Just like the human genome was sequenced around the turn of the millennium, this is an equivalent mapping of the human proteome.<br /><br />The researcher estimated there are around 20,500 proteins in the human body. These scientists have profiled around 17,294, which account for around 84% of the total proteins. Apart from this, the team also traced around 2,500 of 3,000 proteins that had been categorised as "missing proteins".</p><p>The work, done by group of Indian scientists, and Johns Hopkins University, published in the renowned journal Nature ( http://www.nature.com/nature/journal/v509/n7502/full/nature13302.html ). Of the 72 people who worked on the project, 46 are Indians.</p><p>Reference:</p><p>http://www.nature.com/nature/journal/v509/n7502/full/nature13302.html</p><p>http://www.proteinatlas.org/ -The antibody-based Human Protein Atlas programme</p><p>http://www.humanproteomemap.org/ -Proteogenomic analysis by identifying translated proteins from annotated pseudogenes, non-coding RNAs and untranslated regions.</p><p>https://www.proteomicsdb.org/ -Assembled protein evidence for 18,097 genes in ProteomicsDB</p><p>&nbsp;</p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/42188/tools-and-method-for-haplotype-phasing</guid>
	<pubDate>Fri, 04 Sep 2020 20:41:40 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/42188/tools-and-method-for-haplotype-phasing</link>
	<title><![CDATA[Tools and Method for Haplotype phasing !]]></title>
	<description><![CDATA[<div>Huge amounts of genotype data are being produced with recent technological advances, both from increasingly&nbsp; comprehensive and inexpensive genome-wide SNP microarrays and from ever more accessible whole-genome and whole-exome sequencing methods. The vast amount of knowledge contained in these results, however, is best&nbsp; exploited through phased haplotypes, which classify the alleles co-located on the same chromosome. Since sequence and SNP array data normally take the form of unphased genotypes, one does not specifically observe which of the two parental chromosomes, or haplotypes, falls on a specific allele. Fortunately, new advances in both computational and laboratory methods promise improved determination of haplotype phase. Following are useful tools :</div><div>&nbsp;</div><p><strong>Arlequin:</strong>&nbsp;<a href="http://cmpg.unibe.ch/software/arlequin3/" target="_blank">http://cmpg.unibe.ch/software/arlequin3/</a></p><p><strong>BEAGLE:</strong>&nbsp;<a href="http://faculty.washington.edu/browning/beagle/beagle.html" target="_blank">http://faculty.washington.edu/browning/beagle/beagle.html</a></p><p><strong>fastPHASE:</strong>&nbsp;<a href="http://stephenslab.uchicago.edu/software.html" target="_blank">http://stephenslab.uchicago.edu/software.html</a></p><p><strong>GENEHUNTER:</strong>&nbsp;<a href="http://linkage.rockefeller.edu/soft/gh/" target="_blank">http://linkage.rockefeller.edu/soft/gh/</a></p><p><strong>The Genome Analysis Toolkit:</strong></p><p><a href="http://www.broadinstitute.org/gsa/wiki/index.php/The_Genome_Analysis_Toolkit" target="_blank">http://www.broadinstitute.org/gsa/wiki/index.php/The_Genome_Analysis_Toolkit</a></p><p><strong>IMPUTE2:</strong>&nbsp;<a href="https://mathgen.stats.ox.ac.uk/impute/impute_v2.html" target="_blank">https://mathgen.stats.ox.ac.uk/impute/impute_v2.html</a></p><p><strong>MACH:</strong>&nbsp;<a href="http://www.sph.umich.edu/csg/abecasis/MACH/" target="_blank">http://www.sph.umich.edu/csg/abecasis/MACH/</a></p><p><strong>MERLIN:</strong>&nbsp;<a href="http://www.sph.umich.edu/csg/abecasis/Merlin/" target="_blank">http://www.sph.umich.edu/csg/abecasis/Merlin/</a></p><p><strong>PHASE:</strong>&nbsp;<a href="http://stephenslab.uchicago.edu/software.html" target="_blank">http://stephenslab.uchicago.edu/software.html</a></p><p><strong>PL-EM:</strong>&nbsp;<a href="http://www.people.fas.harvard.edu/~junliu/plem/" target="_blank">http://www.people.fas.harvard.edu/~junliu/plem/</a></p><p><strong>&ldquo;Read-backed phasing&rdquo; algorithm</strong>:&nbsp;<a href="http://www.broadinstitute.org/gsa/wiki/index.php/Read-backed_phasing_algorithm" target="_blank">http://www.broadinstitute.org/gsa/wiki/index.php/Read-backed_phasing_algorithm</a></p><p><strong>SHAPE-IT:</strong>&nbsp;<a href="http://www.griv.org/shapeit/" target="_blank">http://www.griv.org/shapeit/</a></p>]]></description>
	<dc:creator>Manisha Mishra</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/11249/how-to-sequence-the-human-genome-mark-j-kiel</guid>
	<pubDate>Fri, 30 May 2014 13:24:11 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/11249/how-to-sequence-the-human-genome-mark-j-kiel</link>
	<title><![CDATA[How to sequence the human genome - Mark J. Kiel]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/MvuYATh7Y74" frameborder="0" allowfullscreen></iframe>View full lesson: http://ed.ted.com/lessons/how-to-sequence-the-human-genome-mark-j-kiel

Your genome, every human's genome, consists of a unique DNA sequence of A's, T's, C's and G's that tell your cells how to operate. Thanks to technological advances, scientists are now able to know the sequence of letters that makes up an individual genome relatively quickly and inexpensively. Mark J. Kiel takes an in-depth look at the science behind the sequence.

Lesson by Mark J. Kiel, animation by Marc Christoforidis.]]></description>
	
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/36373/tools-to-predict-the-impact-of-missense-variants</guid>
	<pubDate>Mon, 23 Apr 2018 12:57:33 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/36373/tools-to-predict-the-impact-of-missense-variants</link>
	<title><![CDATA[Tools to Predict the Impact of Missense Variants !]]></title>
	<description><![CDATA[<p><span>Prioritizing missense variants for further experimental investigation is a key challenge in current sequencing studies for exploring complex and Mendelian diseases. A large number of&nbsp;</span><em>in silico</em><span>&nbsp;tools have been employed for the task of pathogenicity prediction, including PolyPhen‐2, SIFT, FatHMM, MutationTaster‐2, MutationAssessor, Combined Annotation Dependent Depletion, LRT, phyloP, and GERP++, as well as optimized methods of combining tool scores, such as Condel and Logit. Due to the wealth of these methods, an important practical question to answer is which of these tools generalize best, that is, correctly predict the pathogenic character of new variants. </span></p><p><span>Study of 10 tools on five datasets that such a comparative evaluation of these tools is hindered by two types of circularity: they arise due to (1) the same variants or (2) different variants from the same protein occurring both in the datasets used for training and for evaluation of these tools, which may lead to overly optimistic results. Comparative evaluations of predictors that do not address these types of circularity may erroneously conclude that circularity confounded tools are most accurate among all tools, and may even outperform optimized combinations of tools.</span></p><p><span>Following tools are useful for mis sense muation detection ...&nbsp;</span></p><p>PolyPhen‐2 (PP2)<br />&ldquo;Predicts possible impact of an amino acid substitution on the structure and function of a human protein using straightforward physical and comparative considerations&rdquo;</p><p>MutationTaster‐2 (MT2)<br />&ldquo;Evaluation of the disease‐causing potential of DNA sequence alterations&rdquo;</p><p>MutationAssessor (MASS)<br />&ldquo;Predicts the functional impact of amino acid substitutions in proteins, such as mutations discovered in cancer or missense polymorphisms&rdquo;</p><p>LRT<br />&ldquo;Identify a subset of deleterious mutations that disrupt highly conserved amino acids within protein‐coding sequences, which are likely to be unconditionally deleterious&rdquo;</p><p>SIFT<br />&ldquo;Predicts whether an amino acid substitution affects protein function&rdquo;</p><p>GERP++<br />&ldquo;Identifies constrained elements in multiple alignments by quantifying substitution deficits. These deficits represent substitutions that would have occurred if the element were neutral DNA, but did not occur because the element has been under functional constraint. We refer to these deficits as &ldquo;rejected substitutions.&rdquo; Rejected substitutions are a natural measure of constraint that reflects the strength of past purifying selection on the element&rdquo;</p><p>phyloP<br />&ldquo;Compute conservation or acceleration P values based on an alignment and a model of neutral evolution&rdquo;</p><p>FatHMM unweighted (FatHMM‐U)<br />Predicts &ldquo;functional consequences of both coding variants, that is, nonsynonymous single‐nucleotide variants, and noncoding variants&rdquo;</p><p>FatHMM weighted (FatHMM‐W)<br />Predicts &ldquo;functional consequences of both coding variants, that is, nonsynonymous single‐nucleotide variants, and noncoding variants&rdquo; and its weighting scheme attributes higher tolerance scores to SNVs in proteins, related proteins, or domains that already include a high fraction of pathogenic variantsh</p><p>Combined Annotation Dependent Depletion (CADD)<br />&ldquo;CADD is a tool for scoring the deleteriousness of single‐nucleotide variants as well as insertion/deletions variants in the human genome&rdquo;</p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/11354/genomics-and-personalized-medicine</guid>
	<pubDate>Sun, 01 Jun 2014 23:38:42 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/11354/genomics-and-personalized-medicine</link>
	<title><![CDATA[Genomics and Personalized Medicine]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/pgHAXCMMcro" frameborder="0" allowfullscreen></iframe>(October 20, 2009) Michael Snyder, Professor of Genetics and Chair of the Department of Genetics at Stanford, discusses advances in gene sequencing, the impact of genomics on medicine, the potential for personalized medicine. and efforts at Stanford to further study these issues.

Stanford Mini Med School is a series arranged and directed by Stanford's School of Medicine, and presented by the Stanford Continuing Studies program. Featuring more than thirty distinguished, faculty, scientists and physicians from Stanford's medical school, the series offers students a dynamic introduction to the world of human biology, health and disease, and the groundbreaking changes taking place in medical research and health care.

Stanford University
http://www.stanford.edu

Stanford University School of Medicine
http://med.stanford.edu

Stanford Continuing Studies
http://continuingstudies.stanford.edu

Stanford University Channel on YouTube:
http://www.youtube.com/stanford]]></description>
	
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