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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/31300?offset=90</link>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/26569/genome-stability-laboratory</guid>
  <pubDate>Mon, 07 Mar 2016 04:16:32 -0600</pubDate>
  <link></link>
  <title><![CDATA[Genome Stability Laboratory]]></title>
  <description><![CDATA[
<p>The bakers yeast, Saccharomyces cerevisiae is an ideal model organism to understand mechanisms of meiotic chromosome segregation. In S. cerevisiae and in mammals, the majority of meiotic crossovers are formed through a highly conserved MSH4p-MSH5p, MLH1p-MLH3p dependent pathway. We are interested in charactering the role of these complexes in crossover formation and distribution among all homolog pairs. Errors in this process are linked to congenital birth defects in humans such as Down's syndrome.Our laboratory is also interested in understanding the effect of genetic background on mutation rate variation using S. cerevisiae as a model. These studies are relevant for understanding cancer progression, genome evolution and architecture. We use high- throughput genomic methods as well as classical genetics to achieve these aims. </p>

<p>More at http://faculty.iisertvm.ac.in/~nishantkt/index.html</p>
]]></description>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/26499/katju-lab</guid>
  <pubDate>Fri, 26 Feb 2016 03:25:32 -0600</pubDate>
  <link></link>
  <title><![CDATA[Katju Lab]]></title>
  <description><![CDATA[
<p>TheLab seek to understand the genetic factors contributing to genomic variation and phenotypic diversity.  To this end, we employ molecular and bioinformatic tools to study evolutionary processes at the level of populations, both experimental and natural, and genomes.  Our research interests encompass a wide range of topics, including the evolution of organellar and nuclear genomes, gene duplication and the origin of novel function, and the fitness and phenotypic consequences of mutation in evolution. For details regards ongoing projects, please see the Research page.</p>

<p>http://katjulab.com/research.html</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26426/genome-browser-gbrowse</guid>
	<pubDate>Fri, 19 Feb 2016 09:22:43 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26426/genome-browser-gbrowse</link>
	<title><![CDATA[Genome Browser : GBrowse]]></title>
	<description><![CDATA[<p>Generic Genome Browser Version 2: A Tutorial for Administrators</p>
<p>This is an extensive tutorial to take you through the main features and gotchas of configuring GBrowse as a server. This tutorial assumes that you have successfully set up Perl, GD, BioPerl and the other GBrowse dependencies. If you haven't, please see the <a href="http://gmod.org/wiki/GBrowse_2.0_HOWTO">GBrowse HOWTO</a> During most of the tutorial, we will be using the "in-memory" GBrowse database (no relational database required!) Later we will show how to set up a genome size database using the berkeleydb and MySQL adaptors.</p>
<p>More at http://elp.ucdavis.edu/tutorial/tutorial.html</p><p>Address of the bookmark: <a href="http://elp.ucdavis.edu/tutorial/tutorial.html" rel="nofollow">http://elp.ucdavis.edu/tutorial/tutorial.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27438/hagfish-assess-an-assembly-through-creative-use-of-coverage-plots</guid>
	<pubDate>Fri, 20 May 2016 19:08:17 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27438/hagfish-assess-an-assembly-through-creative-use-of-coverage-plots</link>
	<title><![CDATA[Hagfish - assess an assembly through creative use of coverage plots]]></title>
	<description><![CDATA[<p>Hagfish is a tool that is to be used in data analysis of Next Generation Sequencing (NGS) experiments. Hagfish builds on the concept of coverage plots and aims to assist (amongst others) in quality control of&nbsp;<em style="font-size: 12.8px;">de novo</em>&nbsp;genome assembly or identification of structural variation in a genome re-sequencing experiment.</p>
<p>Hagfish requires a reference sequence and a&nbsp;<span>paired end</span>&nbsp;re-sequencing data set. Hagfish has more power the larger the insert size of the paired end library is.</p>
<p>Quick links:&nbsp;<a href="https://github.com/mfiers/hagfish/wiki/Install">Installation</a>,<a href="https://github.com/mfiers/hagfish/wiki/Operation">Operation</a>,&nbsp;<a href="https://github.com/mfiers/hagfish/wiki/ReadMappers">Read mappers</a>,&nbsp;<a href="https://github.com/mfiers/hagfish/wiki/Scripts">Hagfish scripts</a>,&nbsp;<a href="https://github.com/mfiers/hagfish/wiki/Plots">Hagfish plots</a></p><p>Address of the bookmark: <a href="https://github.com/mfiers/hagfish" rel="nofollow">https://github.com/mfiers/hagfish</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28269/4dgenome</guid>
	<pubDate>Mon, 04 Jul 2016 00:44:55 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28269/4dgenome</link>
	<title><![CDATA[4DGenome]]></title>
	<description><![CDATA[<p><span>Records in 4DGenome are compiled through comprehensive literature curation of experimentally-derived and computationally-predicted interactions. The current release contains 4,433,071 experimentally-derived and 3,605,176 computationally-predicted interactions in 5 organisms. Experimental data cover both high throughput datasets and individiual focused studies.&nbsp;</span><br><br><span>All interaction data are freely available in a standardized file format. Records can be queried by genomic regions, gene names, organism, and detection technology.&nbsp;</span></p><p>Address of the bookmark: <a href="http://4dgenome.research.chop.edu/" rel="nofollow">http://4dgenome.research.chop.edu/</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/13226/you-and-your-friend-have-similar-dna</guid>
	<pubDate>Sun, 27 Jul 2014 20:44:05 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/13226/you-and-your-friend-have-similar-dna</link>
	<title><![CDATA[You and your friend have similar DNA !!!]]></title>
	<description><![CDATA[<p>New research out of Massachusetts claims that people often choose friends that are similar to them in genetics and they are more accurate than you might suppose. A study published on PNAS&nbsp;http://www.pnas.org/content/111/Supplement_3/10796.full found that people are apt to pick friends who are genetically similar to themselves - so much so that friends tend to be as alike at the genetic level as a person's fourth cousin.</p><div style="text-align: center;"><img src="http://i.kinja-img.com/gawker-media/image/upload/s--CwLwHa43--/18fbmlokxcmqcjpg.jpg" alt="image" width="300" height="271" style="border: 0px; border: 0px;"></div><p>Scientists with a long-running Framingham Heart Study looked at 1,932 people (examination of about 1.5 million markers of genetic variations), comparing unrelated friends to unrelated strangers. They found that friends shared about 1% of their genes &mdash; a percentage much higher than those shared with strangers.This new findings made it clear that people have more DNA in common with those who are selected as friends than with strangers in the same population.&nbsp;</p><p>The genes that lined up the most were olfactory genes, which deal with smell. The ones that lined up the least were immune system genes. The researchers weren't sure why that happened :/. Olfactory genes might be a straightforward explanation: People who like the same smells tend to be drawn to similar environments, where they meet others with the same tendencies.</p><p>Reference:</p><p>http://www.pnas.org/content/111/Supplement_3/10796.full</p><p>Image : http://i.kinja-img.com</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28290/bioinformatics-tools-and-software</guid>
	<pubDate>Tue, 05 Jul 2016 10:02:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28290/bioinformatics-tools-and-software</link>
	<title><![CDATA[Bioinformatics tools and software]]></title>
	<description><![CDATA[<p><a href="http://drive5.com/usearch">USEARCH &gt;</a><br><span>Extreme high-throughput sequence analysis. Orders of magnitude faster than BLAST.</span>&nbsp;<a href="http://drive5.com/muscle">MUSCLE &gt;</a><br><span>Multiple sequence alignment. Faster and more accurate than CLUSTALW.</span></p>
<p>&nbsp;<a href="http://drive5.com/uparse">UPARSE &gt;</a><br><span>OTU clustering for 16S and other marker genes. Highly accurate OTU sequences and improved diversity measures.</span>&nbsp;<a href="http://drive5.com/uchime">UCHIME &gt;</a><br><span>Chimeric sequence detection.</span>&nbsp;<a href="http://drive5.com/piler">PILER &gt;</a><br><span>De novo genome repeat finder.</span>&nbsp;<a href="http://drive5.com/pilercr">PILER-CR &gt;</a><br><span>Detection of CRISPR repeats in bacterial genomes.</span>&nbsp;<a href="http://drive5.com/qscore">QSCORE &gt;</a><br><span>Compare two multiple alignments for benchmarking.</span>&nbsp;<a href="http://drive5.com/pals">PALS &gt;</a><br><span>Whole-genome alignment.</span>&nbsp;<a href="http://drive5.com/muscle/prefab.htm">PREFAB &gt;</a><br><span>Protein Reference Alignment Database.</span>&nbsp;<a href="http://drive5.com/bench">MSA benchmark collection &gt;</a><br><span>Selected multiple alignment benchmarks in a standardized FASTA format.</span></p><p>Address of the bookmark: <a href="http://drive5.com/software.html" rel="nofollow">http://drive5.com/software.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28554/megan6</guid>
	<pubDate>Mon, 25 Jul 2016 05:45:22 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28554/megan6</link>
	<title><![CDATA[MEGAN6]]></title>
	<description><![CDATA[<p>Microbiome analysis using a single application</p>
<p>MEGAN6 is a comprehensive toolbox for interactively analyzing microbiome data. All the interactive tools you need in one application.</p>
<ul>
<li>Taxonomic analysis using the NCBI taxonomy or a customized taxonomy such as SILVA</li>
<li>Functional analysis using InterPro2GO, SEED, eggNOG or KEGG</li>
<li>Bar charts, word clouds, Voronoi tree maps and many other charts</li>
<li>PCoA, clustering and networks</li>
<li>Supports metadata</li>
<li>MEGAN parses many different types of input</li>
</ul>
<p>Why use MEGAN6?</p>
<div>&nbsp;The software is:</div>
<div><ol>
<li>Easy to use. MEGAN6 is a single application and all features are available through menus, toolbars and graphics. No scripting skills required.</li>
<li>Powerful. MEGAN6 allows you to work with hundreds of samples containing&nbsp;hundreds of millions of sequencing reads. Blast-like analysis can be performed using DIAMOND.</li>
<li>Comprehensive. MEGAN6 offers a large range of analysis tools, and is under active development.</li>
</ol></div><p>Address of the bookmark: <a href="https://ab.inf.uni-tuebingen.de/software/megan6" rel="nofollow">https://ab.inf.uni-tuebingen.de/software/megan6</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29276/murasaki</guid>
	<pubDate>Fri, 30 Sep 2016 10:22:30 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29276/murasaki</link>
	<title><![CDATA[Murasaki]]></title>
	<description><![CDATA[<p>Murasaki is an anchor alignment program that is</p>
<ul style="margin-left: 16px;">
<li>exteremely fast (17 CPU hours for whole Human x Mouse genome (with 40 nodes: 35 wall minutes), or 8 mammals in 21 CPU hours (42 wall minutes))</li>
<li>scalable (Arbitrarily parallelizable across multiple nodes using MPI)</li>
<li>memory efficient. (Even a single node with 16GB of ram can handle over 1Gbp of sequence)</li>
<li>unlimited by pattern length or selection</li>
<li>repeat tolerant</li>
</ul>
<p><img src="http://murasaki.dna.bio.keio.ac.jp/9mammals-small.png" width="500" height="375" alt="image" style="border: 0px;"></p><p>Address of the bookmark: <a href="http://murasaki.dna.bio.keio.ac.jp/wiki/index.php?Murasaki" rel="nofollow">http://murasaki.dna.bio.keio.ac.jp/wiki/index.php?Murasaki</a></p>]]></description>
	<dc:creator>Anjana</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29305/miro-mirna-omics</guid>
	<pubDate>Tue, 04 Oct 2016 14:50:48 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29305/miro-mirna-omics</link>
	<title><![CDATA[MIRO : miRNA omics]]></title>
	<description><![CDATA[<p><span>The MIRO (the miRNA omics) pipeline is a flexible and powerful tool for the analysis of miRNA (or more generall short RNA) expression using short-read deep sequencing data. In its present implementation MIRO is especially adapted for the analysis of reads generated with the Illumina sequencing platform. MIRO allows to preprocess the Solexa-reads, map them flexibly to several reference genomes using one of four different mappers, create differential gene (miRNA) expression profiles and cluster reads using one of several algorithm. MIRO output is furthermore compatible with software such as genome browsers and miRDeep.</span></p><p>Address of the bookmark: <a href="http://seq.crg.es/download/software/Miro/" rel="nofollow">http://seq.crg.es/download/software/Miro/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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