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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/13911?offset=30</link>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/7214/lapti-lab</guid>
  <pubDate>Thu, 12 Dec 2013 18:19:12 -0600</pubDate>
  <link></link>
  <title><![CDATA[LAPTI Lab]]></title>
  <description><![CDATA[
<p>The main theme of our research is the understanding of how genetic information is decoded from DNA into RNA and proteins. Someone may find this topic a little strange and argue that we already know how this is happening.</p>

<p>Translational recoding. </p>

<p>RNA editing. </p>

<p>Evolution of the genetic code and translation.</p>

<p>More at http://lapti.ucc.ie/research.html</p>

<p>Lab page http://lapti.ucc.ie/index.html</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/11603/ncbi-webinar</guid>
	<pubDate>Sun, 08 Jun 2014 02:47:01 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/11603/ncbi-webinar</link>
	<title><![CDATA[NCBI Webinar]]></title>
	<description><![CDATA[<p>In less than two weeks, NCBI will offer a webinar entitled "Introducing 3 NCBI Resources to Navigate Testing for Disease Linked Variants: MedGen, GTR and ClinVar". This webinar will delve into the lifecycle of genetic testing and teach attendees how to navigate the NIH Genetic Testing Registry, ClinVar, and MedGen resources. These resources can be used to prepare for clinical cases, access detailed information about orderable genetic tests, interpret test results, and more.</p><p>More at https://attendee.gotowebinar.com/register/8452228815737989634</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/18820/jrfsrf-at-university-of-calcutta</guid>
  <pubDate>Fri, 31 Oct 2014 08:53:10 -0500</pubDate>
  <link></link>
  <title><![CDATA[JRF/SRF at University of Calcutta]]></title>
  <description><![CDATA[
<p>Applications are invited to appear at a walk-in-interview for one post of Junior Research Fellow in the DBT(DBT Twinning NER) sponsored project entitled “Protein folding kinetics is a selection force on shaping codon usage bias in the high expression genes” in the room of the HOD, Department of Biotechnology and the Coordinator, DR. B. C. Guha Centre for Genetic Engineering and Biotechnology, University College of Science, 35 Ballygunge Circular Road, Kolkata 700019 on the 12th November, 2014 at 3:00 p.m.</p>

<p>Essential qualifications: First class M. Sc. in any branch of life sciences and qualified CSIR-UGC NET/GATE Examination.</p>

<p>Desirable qualifications: Practical experience in biochemical and biophysical studies of proteins</p>

<p>Emoluments: as per DBT norms</p>

<p>The project is tenable for two years, initially for one year.</p>

<p>Age: Below 28 years (relaxable in the case of SC/ST/OBC/women candidates)</p>

<p>Candidates are requested to bring two sets of complete applications on plain paper furnishing bio-data and copies of attested certificates along with originals (for verification) on the date of interview.</p>

<p>No TA/DA is admissible for candidates appearing at the interview.</p>

<p>Dr. Rajat Banerjee<br />Assistant Professor<br />Department of Biotechnology and<br />Dr. B. C. Guha Centre for Genetic Engineering and Biotechnology<br />University College of Science<br />35, Ballygunge Circular Road<br />Kolkata 700019</p>

<p>Advertisement: www.caluniv.ac.in/news/jrf_biotech_2.pdf</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31278/metapred2cs</guid>
	<pubDate>Fri, 03 Mar 2017 05:15:07 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31278/metapred2cs</link>
	<title><![CDATA[MetaPred2CS]]></title>
	<description><![CDATA[<p style="text-align: justify;"><strong>MetaPred2CS Web server&nbsp;</strong>is a meta-predictor based on&nbsp;<a href="http://www.ncbi.nlm.nih.gov/pubmed/17160063">Support Vector Machine (SVM)</a>&nbsp;that combines 6 individual sequence based protein-protein interaction prediction methods to predict&nbsp;<strong>prokaryotic two-component system&nbsp;</strong>protein-protein interactions (PPIs). The methods implemented in MetaPred2CS are 2 co-evolutionary methods:&nbsp;<a href="http://www.ncbi.nlm.nih.gov/pubmed/11933068">in-silico two hybrid (i2h)</a>&nbsp;and&nbsp;<a href="http://www.ncbi.nlm.nih.gov/pubmed/11707606">mirror tree (MT)</a>&nbsp;methods and 4 genomics context based methods:&nbsp;<a href="http://www.ncbi.nlm.nih.gov/pubmed/15947018">phylogenetic profiling (PP)</a>,&nbsp;<a href="http://www.ncbi.nlm.nih.gov/pubmed/10573422">gene fusion (GF)</a>,&nbsp;<a href="http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.0030043">gene neighbourhood (GN)</a>&nbsp;and and&nbsp;<a href="http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.0030043">gene operon methods (GO)</a>.</p>
<p>&nbsp;http://metapred2cs.ibers.aber.ac.uk/</p><p>Address of the bookmark: <a href="https://github.com/martinjvickers/MetaPred2CS" rel="nofollow">https://github.com/martinjvickers/MetaPred2CS</a></p>]]></description>
	<dc:creator>Manisha Mishra</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34744/foldit-solve-puzzles-for-science</guid>
	<pubDate>Thu, 21 Dec 2017 15:17:47 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34744/foldit-solve-puzzles-for-science</link>
	<title><![CDATA[Foldit: Solve Puzzles for Science]]></title>
	<description><![CDATA[<p><span>Foldit</span><span>&nbsp;is an online puzzle video game about protein&nbsp;</span><span>folding. It</span><span>&nbsp;is part of an experimental research project developed by the University of Washington, Center for Game Science, in collaboration with the UW Department of Biochemistry. The objective of&nbsp;</span><span>Foldit</span><span>&nbsp;is to&nbsp;</span><span>fold</span><span>&nbsp;the structures of selected proteins as perfectly as possible</span></p>
<p>https://fold.it/portal/</p><p>Address of the bookmark: <a href="https://fold.it/" rel="nofollow">https://fold.it/</a></p>]]></description>
	<dc:creator>Robert M Willioms</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/36392/protein-protein-interaction-sites-predictions</guid>
	<pubDate>Wed, 25 Apr 2018 04:53:20 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/36392/protein-protein-interaction-sites-predictions</link>
	<title><![CDATA[Protein-Protein Interaction Sites Predictions !]]></title>
	<description><![CDATA[<p><span>The study of Protein&ndash;Protein Interactions (PPIs) has a crucial role in biology, medicine and the pharmaceutical industry. PPIs can be investigated from two aspects: The interaction partners of a specific protein and the amino acid residues participating in a given PPI. Information about a protein&rsquo;s interaction partners allows scientists to construct protein interaction networks, such as signaling pathways, which in turn facilitate the understanding of many biological and clinical observations.&nbsp;</span></p><p><span>Following are the list of tools commonly used to PPIs predictions:</span></p><p>Protein-Protein Interaction Sites</p><p><a href="http://pipe.scs.fsu.edu/ppisp.html" target="_blank">PPISP</a></p><p>A consensus neural network method for predicting protein-protein interaction sites</p><p><a href="http://biunit.naist.jp/homcos/" target="_blank">HOMCOS</a></p><p>A server to predict interacting protein pairs and interacting sites by homology modeling of complex structures</p><p><a href="http://prism.ccbb.ku.edu.tr/hotpoint/" target="_blank">HotPOINT</a></p><p>Prediction of protein interfaces using an empirical model</p><p><a href="http://cubic.bioc.columbia.edu/services/isis/" target="_blank">ISIS</a></p><p>Prediction of interaction hotspots from sequence</p><p><a href="http://kfc.mitchell-lab.org/" target="_blank">KFC server</a></p><p>Automated decision-tree approach to predicting protein-protein interaction hot spots</p><p><a href="http://pipe.scs.fsu.edu/meta-ppisp.html" target="_blank">meta-PPISP</a></p><p>A meta server for predicting protein-protein interaction sites. meta-PPISP is built on three individual web servers:&nbsp;<a href="https://bip.weizmann.ac.il/toolbox/structure/binding.htm#cons">cons-PPISP</a>,&nbsp;<a href="https://bip.weizmann.ac.il/toolbox/structure/binding.htm#pin">PINUP</a>, and&nbsp;<a href="https://bip.weizmann.ac.il/toolbox/structure/binding.htm#pro">Promate</a></p><p><a href="http://www.molsoft.com/oda.html" target="_blank">ODA</a></p><p>Identification of optimal surface patches with the lowest docking desolvation energy values</p><p><a href="http://sparks.informatics.iupui.edu/PINUP/" target="_blank">PINUP</a></p><p>Protein binding site prediction with an empirical scoring function</p><p>Other Sites (DNA, RNA, Metals)</p><p><a href="http://ligin.weizmann.ac.il/~lpgerzon/mbs4/mbs.cgi" target="_blank">CHED</a>&nbsp;</p><p>Web server for predicting soft metal binding sites in proteins</p><p><a href="http://cssb.biology.gatech.edu/skolnick/webservice/DBD-Hunter/" target="_blank">DBD-Hunter</a></p><p>A knowledge-based method for the prediction of DNA-protein interactions</p><p><a href="http://pipe.scs.fsu.edu/displar.html" target="_blank">DISPLAR</a></p><p>Given the structure of a protein known to bind DNA, the method predicts residues that contact DNA using neural network method</p><p><a href="http://idbps.tau.ac.il/" target="_blank">iDBPs</a></p><p>Predicts DNA binding proteins for proteins with known 3D structure.</p><p><a href="http://pfp.technion.ac.il/" target="_blank">PFplus</a></p><div style="text-align: left;">A tool for extracting and displaying positive electrostatic patches on protein surfaces which can be indicative of nucleic acid binding interfaces.</div>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44882/fantasia</guid>
	<pubDate>Wed, 20 Aug 2025 02:48:15 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44882/fantasia</link>
	<title><![CDATA[FANTASIA]]></title>
	<description><![CDATA[<p dir="auto">FANTASIA is an advanced pipeline for the automatic functional annotation of protein sequences using state-of-the-art protein language models. It integrates deep learning embeddings and in-memory similarity searches, retrieving reference vectors from a PostgreSQL database with pgvector, to associate Gene Ontology (GO) terms with proteins.</p>
<p>https://www.nature.com/articles/s42003-025-08651-2</p><p>Address of the bookmark: <a href="https://github.com/CBBIO/FANTASIA" rel="nofollow">https://github.com/CBBIO/FANTASIA</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41582/flexidot-highly-customizable-ambiguity-aware-dotplots-for-visual-sequence-analyses</guid>
	<pubDate>Fri, 24 Apr 2020 08:39:28 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41582/flexidot-highly-customizable-ambiguity-aware-dotplots-for-visual-sequence-analyses</link>
	<title><![CDATA[flexidot: Highly customizable, ambiguity-aware dotplots for visual sequence analyses]]></title>
	<description><![CDATA[<p><span>FlexiDot is a cross-platform dotplot suite generating high quality self, pairwise and all-against-all visualizations. To improve dotplot suitability for comparison of consensus and error-prone sequences, FlexiDot harbors routines for strict and relaxed handling of mismatches and ambiguous residues. The custom shading modules facilitate dotplot interpretation and motif identification by adding information on sequence annotations and sequence similarities to the images. Combined with collage-like outputs, FlexiDot supports simultaneous visual screening of a large sequence sets, allowing dotplot use for routine screening.</span></p>
<p><img src="https://github.com/molbio-dresden/flexidot/blob/master/images/Beetle_matrix_shading.png?raw=true" alt="image" style="border: 0px; border: 0px;"></p><p>Address of the bookmark: <a href="https://github.com/molbio-dresden/flexidot" rel="nofollow">https://github.com/molbio-dresden/flexidot</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/3917/the-story-of-you-encode-and-the-human-genome</guid>
	<pubDate>Sat, 24 Aug 2013 18:49:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/3917/the-story-of-you-encode-and-the-human-genome</link>
	<title><![CDATA[The Story of You: ENCODE and the human genome]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/TwXXgEz9o4w" frameborder="0" allowfullscreen></iframe><p>Ever since a monk called Mendel started breeding pea plants we've been learning about our genomes. In 1953, Watson, Crick and Franklin described the structure of the molecule that makes up our genomes: the DNA double helix. Then, in 2001, scientists wrote down the entire 3-billion letter code contained in the average human genome. Now they're trying to interpret that code; to work out how it's used to make different types of cells and different people. The ENCODE project, as it's called, is the latest chapter in the story of you. To read the ENCODE research papers and more, visit http://www.nature.com/ENCODE</p>]]></description>
	
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/4634/immune-response-to-cancer-cells-awesome</guid>
	<pubDate>Fri, 20 Sep 2013 06:20:47 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/4634/immune-response-to-cancer-cells-awesome</link>
	<title><![CDATA[Immune response to cancer cells! AWESOME]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/C6YuBh-wAPQ" frameborder="0" allowfullscreen></iframe><p>Awesome viddeo explaining the way in which the antibody, HuLuc 63, appears to induce anti-tumor effects by binding to a protein that is only expressed on the surface of myeloma cells. This initiates antibody-dependent cellular cytotoxicity activity that kills myeloma cells and leaves healthy cells intact.</p>]]></description>
	
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