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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/20585?offset=1340</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44516/16srna-database-download</guid>
	<pubDate>Wed, 24 Apr 2024 04:33:15 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44516/16srna-database-download</link>
	<title><![CDATA[16sRNA Database Download]]></title>
	<description><![CDATA[<p>Downloading 16S rRNA databases can be crucial for various bioinformatics analyses, especially in microbiome research. However, it's important to note that databases can vary based on your specific needs, such as the taxonomic coverage you require or the type of analysis you're performing. Here's a general guideline on how you can obtain 16S rRNA databases:</p><ol>
<li>
<p><span>NCBI (National Center for Biotechnology Information)</span>:</p>
<ul>
<li>NCBI provides various databases related to genetic information, including 16S rRNA sequences.</li>
<li>You can access the 16S ribosomal RNA sequences from NCBI's Nucleotide database (<a href="https://www.ncbi.nlm.nih.gov/nucleotide/" target="_new">https://www.ncbi.nlm.nih.gov/nucleotide/</a>).</li>
<li>Perform a search using keywords like "16S rRNA" or specific bacterial names to find relevant sequences.</li>
<li>You can download sequences individually or in batches using the provided tools.</li>
</ul>
</li>
<li>
<p><span>GreenGenes</span>:</p>
<ul>
<li>GreenGenes is a widely used 16S rRNA gene sequence database.</li>
<li>You can access it at <a target="_new">http://greengenes.secondgenome.com/</a>.</li>
<li>GreenGenes provides precompiled databases for various purposes, including classification, alignment, and phylogenetic analysis.</li>
</ul>
</li>
<li>
<p><span>SILVA</span>:</p>
<ul>
<li>SILVA (<a href="https://www.arb-silva.de/" target="_new">https://www.arb-silva.de/</a>) is another comprehensive database for ribosomal RNA (rRNA) sequences.</li>
<li>It covers not only 16S rRNA but also other ribosomal RNA sequences.</li>
<li>SILVA provides precompiled databases for various purposes, including taxonomic classification and alignment.</li>
</ul>
</li>
<li>
<p><span>Ribosomal Database Project (RDP)</span>:</p>
<ul>
<li>RDP (<a target="_new">http://rdp.cme.msu.edu/</a>) is a curated database that offers 16S rRNA sequences.</li>
<li>It provides tools for sequence analysis and classification.</li>
<li>You can download sequences and taxonomy information from their website.</li>
</ul>
</li>
<li>
<p><span>QIIME (Quantitative Insights Into Microbial Ecology)</span>:</p>
<ul>
<li>QIIME (<a href="https://qiime2.org/" target="_new">https://qiime2.org/</a>) is a widely used bioinformatics platform for microbiome analysis.</li>
<li>It provides tools for analyzing microbial communities, including processing 16S rRNA sequences.</li>
<li>QIIME often includes its own preprocessed 16S rRNA databases that can be used for analysis within the platform.</li>
</ul>
</li>
</ol><p>Before downloading any database, make sure to read the terms of use and citation requirements, as some databases may have specific usage policies. Additionally, consider the compatibility of the database with your analysis pipeline and software tools.</p><p>&nbsp;</p><p>NCBI 16s RNA database location&nbsp;ftp://ftp.ncbi.nih.gov/blast/db/16SMicrobial.tar.gz</p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/852/queensland-centre-for-medical-genomics-grimmond-lab</guid>
  <pubDate>Sun, 14 Jul 2013 11:58:34 -0500</pubDate>
  <link></link>
  <title><![CDATA[Queensland Centre for Medical Genomics, Grimmond Lab]]></title>
  <description><![CDATA[
<p>Queensland Centre for Medical Genomics</p>

<p>Research Area:<br />pancreatic cancer; ovarian cancer; prostate cancer; bowel cancer; brain cancer; endometrial cancer; breast cancer; personalised medicine; high-throughput genomics</p>

<p>Link @ http://www.imb.uq.edu.au/sean-grimmond</p>
]]></description>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/4725/complex-systems-from-physics-to-biology-october-15-16-2013-at-jnu-convention-center</guid>
  <pubDate>Mon, 23 Sep 2013 10:17:17 -0500</pubDate>
  <link></link>
  <title><![CDATA[Complex Systems: From Physics to Biology October 15-16 2013 at JNU Convention Center]]></title>
  <description><![CDATA[
<p>The symposium intents to focus on complex systems arising in a variety of settings in physics and biology. In particular, applications of the concepts of physics to biological sciences will be the major theme of this meeting.</p>

<p>Selected Topics:</p>

<p>    Cluster Dynamics<br />    Non-equilibrium Statistical Mechanics<br />    Forced Systems<br />    Hamiltonian Dynamics<br />    Synchronization &amp; Control<br />    Genomics &amp; Systems Biology<br />    Computational Neuroscience<br />    Econophysics</p>

<p>More @ http://www.jnu.ac.in/Conference/SCS2013/</p>
]]></description>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/863/rolland-lagan-lab</guid>
  <pubDate>Sun, 14 Jul 2013 12:57:57 -0500</pubDate>
  <link></link>
  <title><![CDATA[Rolland-Lagan lab]]></title>
  <description><![CDATA[
<p>The Rolland-Lagan lab at the University of Ottawa is specializing in computational and developmental biology. We use a combination of experimental work, microscopy, image analysis and computer simulations to explore developmental mechanisms in two and three dimensions. </p>

<p>Research Area</p>

<p>Developmental biology, Computational biology, Simulation modeling, Image data analysis</p>

<p>Link @ http://mysite.science.uottawa.ca/arolland/index.html</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/file/view/989/bioinformatics-approach-to-boar-taint</guid>
	<pubDate>Wed, 17 Jul 2013 15:50:37 -0500</pubDate>
	<link>https://bioinformaticsonline.com/file/view/989/bioinformatics-approach-to-boar-taint</link>
	<title><![CDATA[Bioinformatics approach to Boar Taint]]></title>
	<description><![CDATA[<p><span>Meat products obtained from intact male pigs often produce offensive smell or odour which is recognized as a complex genetic trait called boar taint.Androstenone and Skatole&nbsp;in the fat primarily cause boar taint. Metabolism of androstenone and sex steroids share a common pathway which makes removal of boar taint a very challenging task. Castration is a traditional solution to remove boar taint but it also results in bad quality of meat due to low level of steroids which is objectionable to many consumers. Detected functional variant(s) underlying boar taint compounds can be used as genetic markers in selection of male pigs with reduced boar taint levels. Resequencing of a total of 47 samples belong to Norwegian Landrace (NL) and Duroc (D) pigs with varied boar taint levels were done in Illumina HiSeq2000 to &gt;10X average coverage. Short reads generated from these samples mapped to&nbsp;<em>Sus Scrofa</em>&nbsp;version 10.2 reference assembly using Bowtie2. Alignment file then used for calling SNPs and InDels inside previousy identified QTL regions on SSC5,13, and 7 with the aid of FreeBayes , a variant caller tool. A final list of SNPs was prepared after filtering SNPs on the basis of SNP quality, coverage of SNP allele, functional and structural annotation, and repeats, etc. Selected SNPs will be genotyped in sample population for validation and then used for constructing SNPs haplotypes in close linkage disequilibrium with QTLs and fine mapping of QTLs through association mapping of genotyped SNPs.</span><span>&nbsp;</span></p><p><span>&nbsp;</span></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
	<enclosure url="https://bioinformaticsonline.com/file/download/989" length="19688" type="image/jpeg" />
</item>
<item>
	<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>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/4762/how-dna-is-packaged-advanced</guid>
	<pubDate>Mon, 23 Sep 2013 18:08:34 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/4762/how-dna-is-packaged-advanced</link>
	<title><![CDATA[How DNA is Packaged (Advanced)]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/gbSIBhFwQ4s" frameborder="0" allowfullscreen></iframe>Each chromosome consists of one continuous thread-like molecule of DNA coiled tightly around proteins, and contains a portion of the 6,400,000,000 basepairs (DNA building blocks) that make up your DNA. 
Originally created for DNA Interactive ( http://www.dnai.org ).
TRANSCRIPT: In this animation we'll see the remarkable way our DNA is tightly packed up to fit into the nucleus of every cell. The process starts with assembly of a nucleosome, which is formed when eight separate histone protein subunits attach to the DNA molecule. The combined tight loop of DNA and protein is the nucleosome. Six nucleosomes are coiled together and these then stack on top of each other. The end result is a fiber of packed nucleosomes known as chromatin. This structure, is then looped and further packaged using other proteins (which are not shown here) to give the final "chromosomal" shapes. It is this remarkable multiple folding which allows six feet of DNA to fit into the nucleus of each cell in our body. And a typical cell nucleus is so small that ten thousand could fit on the tip of a needle. It is important to realize that chromosomes are not always present, they form only when cells are dividing. At other times, as we can see here at the end of cell division, our DNA becomes less highly organized.)]]></description>
	
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42965/nucl2vec-local-alignment-of-dna-sequences-using-distributed-vector-representation</guid>
	<pubDate>Tue, 16 Mar 2021 05:45:44 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42965/nucl2vec-local-alignment-of-dna-sequences-using-distributed-vector-representation</link>
	<title><![CDATA[Nucl2Vec: Local alignment of DNA sequences using Distributed Vector Representation]]></title>
	<description><![CDATA[<p><span>We demonstrate a novel approach for</span><span>local alignment of DNA reads with respect to reference genome.</span><span>For this process we have used Skip-gram model for creating</span><span>encoding(Nucl2Vec) and k-nearest neighbor for the alignment.</span><span>With our new approach we have reduced computation cost for</span><span>local alignment , while achieving accuracy comparable to existing</span><span>defacto standard BWA-MEM tool.</span> </p>
<p><em>https://prakharg24.github.io/papers/401851.full.pdf</em></p><p>Address of the bookmark: <a href="https://prakharg24.github.io/papers/401851.full.pdf" rel="nofollow">https://prakharg24.github.io/papers/401851.full.pdf</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34386/slidesort-bpr</guid>
	<pubDate>Mon, 20 Nov 2017 09:19:52 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34386/slidesort-bpr</link>
	<title><![CDATA[SLIDESORT-BPR]]></title>
	<description><![CDATA[<p>Chromosomal rearrangement events are caused by abnormal breaking and rejoining of DNA molecules. They are responsible for many of the cancer related diseases. Detecting the DNA breaking and repairing mechanism, therefore, may offer vital clues about the pathologic causes and diagnostic/therapeutic target of these diseases. But this effort also poses considerable challenges, because the structural variations and the genomes are different from one person to another. Intermediate comparison via reference genome could lead to the loss information. Unlike the current methods which make use the reference genome, we developed a method to detect the breakpoint reads directly from observing the differences between two (or more) NGS short reads samples. Slidesort-BPR is a command line tool implemented in C++.</p><p>Address of the bookmark: <a href="https://github.com/ewijaya/slidesort-bpr" rel="nofollow">https://github.com/ewijaya/slidesort-bpr</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34914/ra-assembler-a-de-novo-dna-assembler-for-third-generation-sequencing-data</guid>
	<pubDate>Wed, 27 Dec 2017 20:36:54 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34914/ra-assembler-a-de-novo-dna-assembler-for-third-generation-sequencing-data</link>
	<title><![CDATA[Ra assembler - a de novo DNA assembler for third generation sequencing data]]></title>
	<description><![CDATA[<p>Integration of the Ra assembler - a de novo DNA assembler for third generation sequencing data developed on Faculty of Electrical Engineering and Computing (FER), Ruder Boskovic Institute (RBI) and Genome Institute of Singapore (GIS).</p>
<p>Ra is in development since 2014 in the form of several separate components that used to be run individually.<br>This project aims to ease the usage of Ra by integrating it into a complete de novo assembly tool.</p>
<p>Unlike other state-of-the-art assemblers,&nbsp;<span>Ra does not have an error correction step.</span>&nbsp;Instead, it relies on detecting overlaps using a very sensitive and specific overlapper ("graphmap -w owler",&nbsp;<a href="https://github.com/isovic/graphmap">https://github.com/isovic/graphmap</a>) and constructing and reducing an overlap graph (Ra layout,&nbsp;<a href="https://github.com/mariokostelac/ra">https://github.com/mariokostelac/ra</a>).</p><p>Address of the bookmark: <a href="https://github.com/mariokostelac/ra-integrate/" rel="nofollow">https://github.com/mariokostelac/ra-integrate/</a></p>]]></description>
	<dc:creator>biogeek</dc:creator>
</item>

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