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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/34386?offset=60</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30375/mauve-a-system-for-constructing-multiple-genome-alignments-in-the-presence-of-large-scale-evolutionary-events-such-as-rearrangement-and-inversion</guid>
	<pubDate>Sat, 24 Dec 2016 09:20:53 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30375/mauve-a-system-for-constructing-multiple-genome-alignments-in-the-presence-of-large-scale-evolutionary-events-such-as-rearrangement-and-inversion</link>
	<title><![CDATA[Mauve: a system for constructing multiple genome alignments in the presence of large-scale evolutionary events such as rearrangement and inversion]]></title>
	<description><![CDATA[<p>Mauve is a system for constructing multiple genome alignments in the presence of large-scale evolutionary events such as rearrangement and inversion. Multiple genome alignments provide a basis for research into comparative genomics and the study of genome-wide evolutionary dynamics.</p>
<p>Mauve has been developed with the idea that a multiple genome aligner should require only modest computational resources. It employs algorithmic techniques that scale well in the lengths of sequences being aligned. For example, a pair of&nbsp;<em>Y. pestis</em>&nbsp;genomes can be aligned in under a minute, while a group of 9 divergent Enterobacterial genomes can be aligned in a few hours. However, the current algorithm&rsquo;s compute time (progressiveMauve) scales cubically in the number of genomes to align, making it unsuitable for datasets containing more than 50-100 bacterial genomes.</p><p>Address of the bookmark: <a href="http://darlinglab.org/mauve/mauve.html" rel="nofollow">http://darlinglab.org/mauve/mauve.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35899/reference-free-prediction-of-rearrangement-breakpoint-reads</guid>
	<pubDate>Thu, 08 Mar 2018 05:05:25 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35899/reference-free-prediction-of-rearrangement-breakpoint-reads</link>
	<title><![CDATA[Reference-free prediction of rearrangement breakpoint reads]]></title>
	<description><![CDATA[<p><span>lideSort-BPR (&nbsp;</span><span>b</span><span>&nbsp;reak&nbsp;</span><span>p</span><span>&nbsp;oint&nbsp;</span><span>r</span><span>&nbsp;eads) is based on a fast algorithm for all-against-all comparisons of short reads and theoretical analyses of the number of neighboring reads. When applied to a dataset with a sequencing depth of 100&times;, it finds &sim;88% of the breakpoints correctly with no false-positive reads. Moreover, evaluation on a real prostate cancer dataset shows that the proposed method predicts more fusion transcripts correctly than previous approaches, and yet produces fewer false-positive reads. To our knowledge, this is the first method to detect breakpoint reads without using a reference genome.</span></p>
<p><span>https://github.com/ewijaya/slidesort-bpr</span></p><p>Address of the bookmark: <a href="https://code.google.com/archive/p/slidesort-bpr/" rel="nofollow">https://code.google.com/archive/p/slidesort-bpr/</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
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	<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" />
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/2518/genome-browsers</guid>
	<pubDate>Fri, 16 Aug 2013 19:04:47 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/2518/genome-browsers</link>
	<title><![CDATA[Genome Browsers]]></title>
	<description><![CDATA[<p>Genome Browser is the platform/database used for searching and retreiving sequences and annotation of genomes belong to various eukaryotes, prokaryotes, etc.</p><p>Following are the weblink for different available browsers:</p><p><a href="http://www.ensembl.org/index.html">http://www.ensembl.org/index.html</a></p><p><a href="http://ensemblgenomes.org/">http://ensemblgenomes.org/</a></p><p><a href="http://genome.ucsc.edu/">http://genome.ucsc.edu/</a></p><p><a href="http://www.ncbi.nlm.nih.gov/genome">http://www.ncbi.nlm.nih.gov/genome</a></p><p><a href="http://www.ebi.ac.uk/genomes/">http://www.ebi.ac.uk/genomes/</a></p><p><a href="http://flybase.org/">http://flybase.org/</a></p><p><a href="http://cmr.jcvi.org/tigr-scripts/CMR/CmrHomePage.cgi">http://cmr.jcvi.org/tigr-scripts/CMR/CmrHomePage.cgi</a></p><p><a href="http://www.sanger.ac.uk/resources/databases/">http://www.sanger.ac.uk/resources/databases/</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/4297/how-genes-are-regulated-transcription-factors</guid>
	<pubDate>Thu, 05 Sep 2013 16:54:19 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/4297/how-genes-are-regulated-transcription-factors</link>
	<title><![CDATA[How Genes are Regulated: Transcription Factors]]></title>
	<description><![CDATA[<iframe src="http://player.vimeo.com/video/30034882?byline=0" width="" height="" frameborder="0" webkitAllowFullScreen allowFullScreen></iframe>Each cell in our body inherits the same master copy of DNA, but different cell types use it differently. Transcription Factors help influence which genes are used in which cell. Understanding how these dynamic proteins physically interact with DNA allows us to better understand and model their binding to DNA and their regulation of gene expression.  Scientific Direction by the Wasserman Lab at the University of British Columbia: http://www.cmmt.ubc.ca/research/investigators/wasserman/lab  Animation and editing by Blair Lyons of Stroma Studios: http://www.stromastudios.com]]></description>
	
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/4943/molecular-genetics-lecture</guid>
	<pubDate>Fri, 27 Sep 2013 04:24:45 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/4943/molecular-genetics-lecture</link>
	<title><![CDATA[Molecular Genetics Lecture]]></title>
	<description><![CDATA[<p><span>"Robert Sapolsky makes interdisciplinary connections between behavioral biology and molecular genetic influences. He relates protein synthesis and point mutations to microevolutionary change, and discusses conflicting theories of gradualism and punctuated equilibrium and the influence of epigenetics on development theories."&nbsp;</span></p>
<p><span>"<span><strong>Robert Sapolsky</strong> is an American neuroendocrinologist, professor of biology, neuroscience, and neurosurgery at Stanford University, researcher and author" ----Wikipedia</span></span></p><p>Address of the bookmark: <a href="http://www.youtube.com/watch?v=_dRXA1_e30o" rel="nofollow">http://www.youtube.com/watch?v=_dRXA1_e30o</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/8639/edit-dna</guid>
	<pubDate>Wed, 05 Mar 2014 02:27:54 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/8639/edit-dna</link>
	<title><![CDATA[Edit DNA !!!]]></title>
	<description><![CDATA[<p>A genome-engineering tool known as Crispr may allow scientists to alter the DNA of humans, animals and plants, a research breakthrough that promises to make a significant impact on science and fighting diseases, according to a <a href="http://www.nytimes.com/2014/03/04/health/a-powerful-new-way-to-edit-dna.html">March 3 story in the <em>New York Times</em></a>. Scientists hope Crispr might also be used for genomic surgery, as it were, to correct errant genes that cause disease.</p><p>A rescently publication paper ( http://jb.asm.org/content/169/12/5429.long )shows significance of an unusual repeated DNA sequences next to a gene in a common bacterium, and their scientific significance. The sequences, it turns out, are part of a sophisticated immune system that bacteria use to fight viruses. And that system, whose very existence was unknown until about seven years ago, may provide scientists with unprecedented power to rewrite the code of life. This means a genome can be edited, much as a writer might change words or fix spelling errors. It allows &ldquo;customizing the genome of any cell or any species at will,&rdquo;.</p><p>Reference:</p><p>http://www.prweb.com/releases/2014/03/prweb11636031.htm</p><p>http://www.nytimes.com/2014/03/04/health/a-powerful-new-way-to-edit-dna.html?hpw&amp;rref=health</p><p>http://jb.asm.org/content/169/12/5429.long</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/file/view/23838/scripted-dna</guid>
	<pubDate>Mon, 17 Aug 2015 17:44:04 -0500</pubDate>
	<link>https://bioinformaticsonline.com/file/view/23838/scripted-dna</link>
	<title><![CDATA[Scripted DNA !!!]]></title>
	<description><![CDATA[<p>As per bioinformatician DNA is partially scripted ;) You dont believe in it. Please have a look at image carefully:)</p>]]></description>
	<dc:creator>Jit</dc:creator>
	<enclosure url="https://bioinformaticsonline.com/file/download/23838" length="13498" type="image/gif" />
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30831/fsa-fast-statistical-alignment</guid>
	<pubDate>Mon, 06 Feb 2017 04:26:01 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30831/fsa-fast-statistical-alignment</link>
	<title><![CDATA[FSA: Fast Statistical Alignment]]></title>
	<description><![CDATA[<p><span>FSA is a probabilistic multiple sequence alignment algorithm which uses a "distance-based" approach to aligning homologous protein, RNA or DNA sequences. Much as distance-based phylogenetic reconstruction methods like Neighbor-Joining build a phylogeny using only pairwise divergence estimates, FSA builds a multiple alignment using only pairwise estimations of homology. This is made possible by the sequence annealing technique for constructing a multiple alignment from pairwise comparisons, developed by Ariel Schwartz in&nbsp;</span><a href="http://www.eecs.berkeley.edu/Pubs/TechRpts/2007/EECS-2007-39.html">"Posterior Decoding Methods for Optimization and Control of Multiple Alignments</a><span>."</span></p>
<p>FSA brings the high accuracies previously available only for small-scale analyses of proteins or RNAs to large-scale problems such as aligning thousands of sequences or megabase-long sequences. FSA introduces several novel methods for constructing better alignments:</p>
<ul>
<li>FSA uses machine-learning techniques to estimate gap and substitution parameters on the fly for each set of input sequences. This "query-specific learning" alignment method makes FSA very robust: it can produce superior alignments of sets of homologous sequences which are subject to very different evolutionary constraints.</li>
<li>FSA is capable of aligning hundreds or even thousands of sequences using a randomized inference algorithm to reduce the computational cost of multiple alignment. This randomized inference can be over ten times faster than a direct approach with little loss of accuracy.</li>
<li>FSA can quickly align very long sequences using the "anchor annealing" technique for resolving anchors and projecting them with transitive anchoring. It then stitches together the alignment between the anchors using the methods described above.</li>
<li>The included GUI, MAD (Multiple Alignment Display), can display the intermediate alignments produced by FSA, where each character is colored according to the probability that it is correctly aligned (see the picture and&nbsp;<a href="http://fsa.sourceforge.net/images/Suchard_SIV.fsa.mov">movie</a>&nbsp;at the top of the page).</li>
</ul>
<p><span>You can see more information on the&nbsp;</span><a href="http://fsa.sourceforge.net/FAQ.html">FAQ</a><span>.&nbsp;</span></p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="http://fsa.sourceforge.net/" rel="nofollow">http://fsa.sourceforge.net/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32465/tetra-nucleotide-analysis</guid>
	<pubDate>Thu, 04 May 2017 05:07:41 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32465/tetra-nucleotide-analysis</link>
	<title><![CDATA[Tetra-Nucleotide Analysis]]></title>
	<description><![CDATA[<p>A tetra-nucleotide is a fragment of DNA sequence with 4 bases (e.g. AGTC or TTGG). Pride&nbsp;<em>et al.</em>&nbsp;(2003) showed that the frequency of tetra-nucleotides in bacterial genomes contain useful, albeit weak, phylogenetic signals. Even though tetra-nucleotide analysis (TNA) utilizes the information of whole genome, it is evident that it cannot replace other alignment-based phylogenetic methods such as&nbsp;<a href="https://chunlab.wordpress.com/orthoani/">OrthoANI</a>&nbsp;or&nbsp;16S rRNA phylogeny. However, TNA can be useful for&nbsp;phylogenetic characterization when whole genome or 16S rRNA gene information is not available. For example, a partial genomic fragment obtained from a metagenome can be identified by TNA (Teeling&nbsp;<em>et al.</em>, 2004). TNA is also fast enough that it can be&nbsp;used&nbsp;as a search engine against a large genome database.</p><p>Address of the bookmark: <a href="https://chunlab.wordpress.com/tetra-nucleotide-analysis/" rel="nofollow">https://chunlab.wordpress.com/tetra-nucleotide-analysis/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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