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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/37965?offset=110</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/5898/an-entire-genome-written-in-lab</guid>
	<pubDate>Fri, 25 Oct 2013 09:43:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/5898/an-entire-genome-written-in-lab</link>
	<title><![CDATA[An entire genome written in lab]]></title>
	<description><![CDATA[<p>This is the first time ever the genetic code has been fundamentally changed. The breakthrough is a huge step forward in synthetic biology and opens up the possibility of turning re-coded bacteria into biofactories, capable of producing potent new forms of protein that could fight disease or generate sustainable materials.</p><p>More @ <a href="http://news.yale.edu/2013/10/17/researchers-rewrite-entire-genome-and-add-healthy-twist">http://news.yale.edu/2013/10/17/researchers-rewrite-entire-genome-and-add-healthy-twist</a></p><p>News Reference:&nbsp;Yale news</p><p><img src="http://images.sciencedaily.com/2011/07/110714142130-large.jpg" alt="image" width="800" height="530" style="border: 0px; border: 0px;"></p><p>Image Source: Sciencedaily.</p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/7088/gabi</guid>
  <pubDate>Fri, 06 Dec 2013 16:43:01 -0600</pubDate>
  <link></link>
  <title><![CDATA[GABi]]></title>
  <description><![CDATA[
<p>GABi Research<br />The major researching fields defined as the GABi scope are described next:<br />    Sequence Analysis<br />    Protein Structure Prediction<br />    Comparative Genomics<br />    Functional Analysis of Residues on Protein Families<br />    Gene/Protein Networks<br />    Genome structure &amp; base composition<br />    Highthroughput data analysis from NGS</p>

<p>Lab Page http://gabi.cidbio.org/index/</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/videolist/watch/14338/biology-computers-collide-in-high-demand-field-of-bioinformatics</guid>
	<pubDate>Mon, 25 Aug 2014 00:56:10 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/14338/biology-computers-collide-in-high-demand-field-of-bioinformatics</link>
	<title><![CDATA[Biology, Computers Collide in High-Demand Field of Bioinformatics]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/fk0z7KOTyMo" frameborder="0" allowfullscreen></iframe>Dr. Shivas Amin calls bioinformatics a "collision of biology and computers." Students learn how to use computers and skills in math and biology to analyze genome and proteome projects to prepare for high-demand jobs in the life sciences. Learn more about Amin and hear from student Medina Baitemirova and alumnus Lukas Simon about the fast-growing field of bioinformatics.]]></description>
	
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/23633/biorg</guid>
  <pubDate>Tue, 04 Aug 2015 20:52:52 -0500</pubDate>
  <link></link>
  <title><![CDATA[BioRG]]></title>
  <description><![CDATA[
<p>This research group works on problems from the fields of Bioinformatics, Biotechnology, Data Mining, and Information Retrieval. The group's research projects includes Comparative Genomics of Bacterial genomes, Metagenomics, Genomic databases, Pattern Discovery in sequences and structures, micro-array data analysis, prediction of regulatory elements, primer design, probe design, phylogenetic analysis, medical image processing, image analysis, data integration, data mining, information retrieval, knowledge discovery in electronic medical records, and more. </p>

<p>More at http://biorg.cis.fiu.edu/</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33789/i-pv-interactive-protein-sequence-visualization</guid>
	<pubDate>Mon, 03 Jul 2017 07:52:51 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33789/i-pv-interactive-protein-sequence-visualization</link>
	<title><![CDATA[I-PV: Interactive Protein Sequence Visualization]]></title>
	<description><![CDATA[<p><span>I-PV is a interactive data visualization software designed for inspection of protein sequences and mutation information. It is mainly used for Genetics and Bioinformatics. So what exactly makes it standout?</span></p>
<p><span>http://i-pv.org/ipv_rec</span></p><p>Address of the bookmark: <a href="http://i-pv.org/" rel="nofollow">http://i-pv.org/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/44604/new-release-of-refseq</guid>
	<pubDate>Tue, 16 Jul 2024 10:09:21 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/44604/new-release-of-refseq</link>
	<title><![CDATA[New Release of RefSeq !]]></title>
	<description><![CDATA[<p>Check out RefSeq release 225, now available&nbsp;<a href="https://www.ncbi.nlm.nih.gov/refseq/?utm_source=ncbi_insights&amp;utm_medium=referral&amp;utm_campaign=refseq-release-225-20240715">online</a>&nbsp;and from the&nbsp;<a href="https://ftp.ncbi.nlm.nih.gov/refseq/release/">FTP</a>&nbsp;site. You can access RefSeq data through&nbsp;<a href="https://www.ncbi.nlm.nih.gov/datasets/?utm_source=ncbi_insights&amp;utm_medium=referral&amp;utm_campaign=refseq-release-225-20240715">NCBI Datasets</a>.</p><h5>What&rsquo;s included in this release?</h5><p>As of July 8, 2024, this full release incorporates genomic, transcript, and protein data containing:</p><ul>
<li><span>448,507,905 records</span></li>
<li><span>334,845,613 proteins</span></li>
<li><span>63,542,774 RNAs</span></li>
<li><span>Sequences from 152,668 organisms</span></li>
</ul><p>The release is provided in several directories as a complete dataset and also as divided by logical groupings.</p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/11611/ten-recommendations-for-creating-usable-bioinformatics-command-line-software</guid>
	<pubDate>Sun, 08 Jun 2014 10:06:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/11611/ten-recommendations-for-creating-usable-bioinformatics-command-line-software</link>
	<title><![CDATA[Ten recommendations for creating usable bioinformatics command line software]]></title>
	<description><![CDATA[<p><span>Bioinformatics software varies greatly in quality. In terms of usability, the command line interface is the first experience a user will have of a tool. Unfortunately, this is often also the last time a tool will be used. Here I present ten recommendations for command line software author&rsquo;s tools to follow, which I believe would greatly improve the uptake and usability of their products, waste less user&rsquo;s time, and improve the quality of scientific analyses.</span></p><p>Address of the bookmark: <a href="http://www.gigasciencejournal.com/content/2/1/15?utm_content=buffer25ee0&amp;utm_medium=social&amp;utm_source=twitter.com&amp;utm_campaign=buffer" rel="nofollow">http://www.gigasciencejournal.com/content/2/1/15?utm_content=buffer25ee0&amp;utm_medium=social&amp;utm_source=twitter.com&amp;utm_campaign=buffer</a></p>]]></description>
	<dc:creator>RAJESH DETROJA</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/19636/google-genomics</guid>
	<pubDate>Thu, 18 Dec 2014 11:05:42 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/19636/google-genomics</link>
	<title><![CDATA[Google Genomics]]></title>
	<description><![CDATA[<ul>
<li>
<p><strong>Explore genetic variation interactively.</strong> Compare entire cohorts in seconds with SQL-like queries. Compute transition/transversion ratios, genome-wide association, allelic frequency and more.</p>
</li>
<li>
<p><strong>Process big genomic data easily.</strong> Run batch analyses like principal component analysis and Hardy-Weinberg equilibrium on as many samples as you like, in minutes or hours, with just a little code.</p>
</li>
<li>
<p><strong>Use Google's infrastructure and big data expertise.</strong> Store one genome or a million using Google Genomics and take advantage of the same infrastructure that powers Search, Maps, YouTube, Gmail and Drive.</p>
</li>
<li>
<p><strong>Support emerging global standards.</strong> Google Genomics is implementing the API defined by the Global Alliance for Genomics and Health for visualization, analysis and more. Compliant software can access Google Genomics, local servers, or any other implementation.</p>
</li>
</ul><p>Address of the bookmark: <a href="https://cloud.google.com/genomics/" rel="nofollow">https://cloud.google.com/genomics/</a></p>]]></description>
	<dc:creator>Tenzin Paul</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38378/gwaspro-a-high-performance-genome-wide-association-analysis-server</guid>
	<pubDate>Fri, 07 Dec 2018 08:04:57 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38378/gwaspro-a-high-performance-genome-wide-association-analysis-server</link>
	<title><![CDATA[GWASpro: A High-Performance Genome-Wide Association Analysis Server]]></title>
	<description><![CDATA[<p>GWASpro supports building complex design matrices, by which complex experimental designs that may include replications, treatments, locations and times, can be accounted for in the linear mixed model (LMM). GWASpro is optimized to handle GWAS data that may consist of up to 10 million markers and 10,000 samples from replicable lines or hybrids. GWASpro provides an interface that significantly reduces the learning curve for new GWAS investigators.</p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="https://bioinfo.noble.org/GWASPRO/" rel="nofollow">https://bioinfo.noble.org/GWASPRO/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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