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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/4943?offset=100</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/9007/what-is-personalized-medicine</guid>
	<pubDate>Thu, 13 Mar 2014 12:00:36 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/9007/what-is-personalized-medicine</link>
	<title><![CDATA[What Is Personalized Medicine?]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/SDlBb4LRQN4" frameborder="0" allowfullscreen></iframe>Gene Screen BC 2011 Participant. 
The goal of this short film is to introduce the audience to the emerging field of personalized cancer treatment. The story is loosely based on a rare case studied at the BC Cancer Agency's Genome Sciences Centre. The study, to the best of our knowledge, was the first in the world to examine a single individual's cancer genome in order to determine the best course of therapy where no established treatment regimen existed.]]></description>
	
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31209/dial</guid>
	<pubDate>Wed, 01 Mar 2017 08:42:28 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31209/dial</link>
	<title><![CDATA[DIAL]]></title>
	<description><![CDATA[<p>A computational pipeline for identifying single-base substitutions between two closely related genomes without the help of a reference genome. DIAL works even when the depth of coverage is insufficient for de novo assembly, and it can be extended to determine small insertions/deletions. Our main motivation is to use this tool to survey the genetic diversity of endangered species as the identified sequence differences can be used to design genotyping arrays to assist in the species' management.</p>
<p>http://www.bx.psu.edu/~ratan/</p><p>Address of the bookmark: <a href="http://www.bx.psu.edu/miller_lab/" rel="nofollow">http://www.bx.psu.edu/miller_lab/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33660/equant-energy-based-quality-assessment-of-protein</guid>
	<pubDate>Sat, 24 Jun 2017 19:24:24 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33660/equant-energy-based-quality-assessment-of-protein</link>
	<title><![CDATA[eQuant : energy-based quality assessment of protein]]></title>
	<description><![CDATA[<p><span>Protein structures are of varying quality. Especially,&nbsp;</span><em>in-silico</em><span>&nbsp;modeled structures are prone to contain serious errors, which limit the usefulness and reliability of these particular protein structures.</span><br><br><span>eQuant is a service for structure quality assessment of single proteins, which utilizes a coarse-grained energy model. The overall quality is calculated as well as the reliability of individual residues. You can submit single PDB files or archives containing a set of proteins.</span></p>
<p>https://biosciences.hs-mittweida.de/equant/</p><p>Address of the bookmark: <a href="https://biosciences.hs-mittweida.de/equant/" rel="nofollow">https://biosciences.hs-mittweida.de/equant/</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37965/kobas-a-web-server-for-geneprotein-functional-annotation-and-functional-gene-set-enrichment</guid>
	<pubDate>Fri, 19 Oct 2018 09:36:11 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37965/kobas-a-web-server-for-geneprotein-functional-annotation-and-functional-gene-set-enrichment</link>
	<title><![CDATA[KOBAS: a web server for gene/protein functional annotation and functional gene set enrichment]]></title>
	<description><![CDATA[<p><span>KOBAS 3.0 is a web server for gene/protein functional annotation (</span><a href="http://kobas.cbi.pku.edu.cn/annotate.php">Annotate</a><span>&nbsp;module) and functional gene set enrichment(Enrichment module). For Annotate module, it accepts gene list as input, including IDs or sequences, and generates annotations for each gene based on multiple databases about pathways, diseases, and Gene Ontology. For Enrichment module, it can accept either gene list or gene expression data as input, and generates enriched gene sets, corresponding name, p-value or a probability of enrichment and enrichment score based on results of multiple methods.</span></p><p>Address of the bookmark: <a href="http://kobas.cbi.pku.edu.cn/" rel="nofollow">http://kobas.cbi.pku.edu.cn/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44479/doubletrouble-identify-duplicated-genes-from-whole-genome-protein-sequences-and-classify</guid>
	<pubDate>Tue, 05 Mar 2024 00:23:49 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44479/doubletrouble-identify-duplicated-genes-from-whole-genome-protein-sequences-and-classify</link>
	<title><![CDATA[doubletrouble: identify duplicated genes from whole-genome protein sequences and classify]]></title>
	<description><![CDATA[<p><span>doubletrouble aims to identify duplicated genes from whole-genome protein sequences and classify them based on their modes of duplication. The duplication modes are i. segmental duplication (SD); ii. tandem duplication (TD); iii. proximal duplication (PD); iv. transposed duplication (TRD) and; v. dispersed duplication (DD). Transposon-derived duplicates (TRD) can be further subdivided into rTRD (retrotransposon-derived duplication) and dTRD (DNA transposon-derived duplication). If users want a simpler classification scheme, duplicates can also be classified into SD- and SSD-derived (small-scale duplication) gene pairs. Besides classifying gene pairs, users can also classify genes, so that each gene is assigned a unique mode of duplication. Users can also calculate substitution rates per substitution site (i.e., Ka and Ks) from duplicate pairs, find peaks in Ks distributions with Gaussian Mixture Models (GMMs), and classify gene pairs into age groups based on Ks peaks.</span></p><p>Address of the bookmark: <a href="https://bioconductor.org/packages/release/bioc/html/doubletrouble.html" rel="nofollow">https://bioconductor.org/packages/release/bioc/html/doubletrouble.html</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/5220/paolo-ruggerone-lab</guid>
  <pubDate>Tue, 01 Oct 2013 14:15:53 -0500</pubDate>
  <link></link>
  <title><![CDATA[Paolo Ruggerone Lab]]></title>
  <description><![CDATA[
<p>Efflux pumps (RND family)</p>

<p>Functioning of efflux systems in Gram-negative bacteria<br />Determinants of the compound-efflux system interactions<br />Action of inhibitors on efflux systems<br />Structural and dynamical features of the efflux systems</p>

<p>TatA<br />Assembly of the TatA system<br />Study of the dynamical features of the charge zipper</p>

<p>Methods<br />Setup of a kinetic Monte Carlo (KMC) scheme to study the flux of antibiotics through porins and efflux systems<br />Setup of protocol to integrate MD results in a ligand-based approach</p>

<p>Viral inhibitors<br />Interactions of selected compounds with RNA-dependent RNA polymerases (RdRps) of HCV and BVDV<br />Assessment of the role of mutations in RdRps<br />Antimicrobial peptides</p>

<p>Interactions of antimicrobial peptides with membranes: structure and dynamics<br />Interactions between antimicrobial peptides in the presence of different membranes<br />Protein-protein interactions<br />Effects of mutations</p>

<p>Lab Page<br />http://www.dsf.unica.it/~paolo/Site/Home.html</p>
]]></description>
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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/file/view/13911/amino-acid-flash-tutorial</guid>
	<pubDate>Mon, 11 Aug 2014 08:58:17 -0500</pubDate>
	<link>https://bioinformaticsonline.com/file/view/13911/amino-acid-flash-tutorial</link>
	<title><![CDATA[Amino Acid Flash Tutorial]]></title>
	<description><![CDATA[<p>Protein is a part of every cell in your body, and no other nutrient plays as many different roles in keeping you alive and healthy. Protein is the building block of our body, and amino acids are the main areas of interest. We have emphasized on all 20 amino acids in this documentary movie. This documentary has been developed that emphasize on chemical structure, chemical formula, IUPAC name and other detail information of all 20 amino acids with the voice, picture with interactive button. This will be helpful for the entire biology and bioinformatics student.</p><p>How to run?</p><p>You need to install flash player or open it with web browser ( I guess you have installed flash plugin) to play.</p><p>Comment below if you like it. Thanks</p>]]></description>
	<dc:creator>Jit</dc:creator>
	<enclosure url="https://bioinformaticsonline.com/file/download/13911" length="33435911" type="application/x-shockwave-flash" />
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	<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>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35787/protein-subcellular-localization-prediction</guid>
	<pubDate>Thu, 01 Mar 2018 06:20:47 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35787/protein-subcellular-localization-prediction</link>
	<title><![CDATA[Protein Subcellular Localization Prediction]]></title>
	<description><![CDATA[<p>Assigning subcellular localization to a protein is an important step towards elucidating its interaction partners, function, and potential role(s) in the cellular machinery. Computational tools offer an attractive complement to time-consuming and laborious experimental methods.</p>
<p>http://abi.inf.uni-tuebingen.de/Services/YLoc/webloc.cgi</p><p>Address of the bookmark: <a href="https://abi.inf.uni-tuebingen.de/Research/Systems%20Biology/protein-subcellular-localization" rel="nofollow">https://abi.inf.uni-tuebingen.de/Research/Systems%20Biology/protein-subcellular-localization</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
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