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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/41493?offset=190</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/2423/cancers-origins-revealed</guid>
	<pubDate>Thu, 15 Aug 2013 13:06:56 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/2423/cancers-origins-revealed</link>
	<title><![CDATA[Cancer's origins revealed]]></title>
	<description><![CDATA[<p>Researchers have provided the first comprehensive compendium of mutational processes that drive tumour development. Together, these mutational processes explain most mutations found in 30 of the most common cancer types. This new understanding of cancer development could help to treat and prevent a wide-range of cancers.<br /><br />More at &gt;&gt; http://www.sanger.ac.uk/about/press/2013/130814.html</p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
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	<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>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34543/acana-an-accurate-and-consistent-alignment-tool-for-dna-sequences</guid>
	<pubDate>Wed, 06 Dec 2017 09:45:29 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34543/acana-an-accurate-and-consistent-alignment-tool-for-dna-sequences</link>
	<title><![CDATA[ACANA: An accurate and consistent alignment tool for DNA sequences]]></title>
	<description><![CDATA[<p><span>ACANA is an accurate and consistent alignment tool for DNA sequences. ACANA is specifically designed for aligning sequences that share only some moderately conserved regions and/or have a high frequency of long insertions or deletions. It attempts to combine the best of local and global alignments algorithms in searching for evolutionarily related regions of sequences in order to achieve the best alignment. ACANA is also robust to the small changes of alignment parameters, particularly the gap extension score. As an accurate alignment tool, ACANA is particularly useful in comparative sequence analysis for identifying conserved functional regulatory elements.</span></p><p>Address of the bookmark: <a href="https://www.niehs.nih.gov/research/resources/software/biostatistics/acana/index.cfm" rel="nofollow">https://www.niehs.nih.gov/research/resources/software/biostatistics/acana/index.cfm</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37987/ropebwt2-incremental-construction-of-fm-index-for-dna-sequences</guid>
	<pubDate>Thu, 25 Oct 2018 04:48:54 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37987/ropebwt2-incremental-construction-of-fm-index-for-dna-sequences</link>
	<title><![CDATA[RopeBWT2: Incremental construction of FM-index for DNA sequences]]></title>
	<description><![CDATA[<p><span>RopeBWT2 is an tool for constructing the FM-index for a collection of DNA sequences. It works by incrementally inserting one or multiple sequences into an existing pseudo-BWT position by position, starting from the end of the sequences. This algorithm can be largely considered a mixture of&nbsp;</span><a href="http://dx.doi.org/10.1007/978-3-642-21458-5_20">BCR</a><span>&nbsp;and&nbsp;</span><a href="http://dfmi.sourceforge.net/">dynamic FM-index</a><span>. Nonetheless, ropeBWT2 is unique in that it may&nbsp;</span><em>implicitly</em><span>sort the input into reverse lexicographical order (RLO) or reverse-complement lexicographical order (RCLO) while building the index.</span></p><p>Address of the bookmark: <a href="https://github.com/lh3/ropebwt2" rel="nofollow">https://github.com/lh3/ropebwt2</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/42958/claus-peter-stelzer-lab</guid>
  <pubDate>Mon, 15 Mar 2021 15:24:41 -0500</pubDate>
  <link></link>
  <title><![CDATA[Claus-Peter Stelzer Lab]]></title>
  <description><![CDATA[
<p>Interested in various topics at the intersection of ecology and evolution. In my research I use rotifers as model organisms for experimental studies at the individual and population level. Rotifers are ideally suited for this, because populations of thousands can be kept in small containers in the lab, while single individuals can still be handled conveniently. </p>

<p>More at https://www.uibk.ac.at/limno/personnel/stelzer/index.html.en#research</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/5380/04-informatics-approach-to-cancer-interview-with-dr-joel-saltz</guid>
	<pubDate>Mon, 07 Oct 2013 14:35:43 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/5380/04-informatics-approach-to-cancer-interview-with-dr-joel-saltz</link>
	<title><![CDATA[04- Informatics Approach to Cancer - Interview with Dr. Joel Saltz]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/8Kf5EP4LY7k" frameborder="0" allowfullscreen></iframe>For additional information visit http://www.cancerquest.org/joel-saltz-interview.

Dr. Joel Saltz is a Professor in the Departments of Pathology, Biostatistics and Bioinformatics, and Mathematics and Computer Science at
Emory University. Dr. Saltz's research on bioinformatics spans several disciplines.  One project involves applying computer analysis to medical imaging to yield better results for patients.  As an example, a computer program may able to help doctors detect small cancers in a CT scan or mammogram. 

In this interview segment, Dr. Saltz  discusses the informatics approach to cancer.

To learn more about cancer and watch additional interviews, please visit the CancerQuest website at http://www.cancerquest.org.]]></description>
	
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/7568/oldest-hominin-dna-sequenced</guid>
	<pubDate>Fri, 27 Dec 2013 19:58:31 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/7568/oldest-hominin-dna-sequenced</link>
	<title><![CDATA[Oldest Hominin DNA Sequenced]]></title>
	<description><![CDATA[<p>Matthias Meyer and his team from the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany, have developed new techniques for retrieving and sequencing highly degraded ancient DNA. They then joined forces with Juan-Luis Arsuaga and applied the new techniques to a cave bear from the Sima de los Huesos site. After this success, the researchers sampled two grams of bone powder from a hominin thigh bone from the cave. They extracted its DNA and sequenced the genome of the mitochondria or mtDNA, a small part of the genome that is passed down along the maternal line and occurs in many copies per cell. The researchers then compared this ancient mitochondrial DNA with Neandertals, Denisovans, present-day humans, and apes.<br /><br />From the missing mutations in the old DNA sequences the researchers calculated that the Sima hominin lived about 400,000 years ago. They also found that it shared a common ancestor with the Denisovans, an extinct archaic group from Asia related to the Neandertals, about 700,000 years ago. "The fact that the mtDNA of the Sima de los Huesos hominin shares a common ancestor with Denisovan rather than Neandertal mtDNAs is unexpected since its skeletal remains carry Neandertal-derived features," says Matthias Meyer. Considering their age and Neandertal-like features, the Sima hominins were likely related to the population ancestral to both Neandertals and Denisovans. Another possibility is that gene flow from yet another group of hominins brought the Denisova-like mtDNA into the Sima hominins or their ancestors.<br /><br /></p><p>Reference</p><p>http://www.sciencedaily.com/releases/2013/12/131204132018.htm</p>]]></description>
	<dc:creator>Surajeet</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26179/alignment-of-closely-related-whole-genomesscaffolds</guid>
	<pubDate>Fri, 29 Jan 2016 10:37:27 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26179/alignment-of-closely-related-whole-genomesscaffolds</link>
	<title><![CDATA[Alignment of closely related whole genomes/scaffolds]]></title>
	<description><![CDATA[<p>With the relative ease and low cost of current generation sequencing technologies has led to a dramatic increase in the number of sequenced genomes for species across the tree of life. This increasing volume of data requires tools that can quickly compare multiple whole-genome sequences, millions of base pairs in length, to aid in the study of populations, pan-genomes, and genome evolution.This bookmaks have been created to report new tools for whole genome alignments.</p>
<p>Please report new whole genome alignment tools under comment sections.</p><p>Address of the bookmark: <a href="http://www.cs.utoronto.ca/~brudno/721.full.pdf" rel="nofollow">http://www.cs.utoronto.ca/~brudno/721.full.pdf</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29683/method-in-comparative-genomics</guid>
	<pubDate>Wed, 09 Nov 2016 16:29:24 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29683/method-in-comparative-genomics</link>
	<title><![CDATA[Method in Comparative genomics !!]]></title>
	<description><![CDATA[<p>We present methods for the automatic determination of genome correspondence. The algorithms enabled the automatic identification of orthologs for more than 90% of genes and intergenic regions across the four species despite the large number of duplicated genes in the yeast genome. The remaining ambiguities in the gene correspondence revealed recent gene family expansions in regions of rapid genomic change.</p>
<p>We present methods for the identification of protein-coding genes based on their patterns of nucleotide conservation across related species. We observed the pressure to conserve the reading frame of functional proteins and developed a test for gene identification with high sensitivity and specificity. We used this test to revisit the genome of S. cerevisiae, reducing the overall gene count by 500 genes (10% of previously annotated genes) and refining the gene structure of hundreds of genes. We present novel methods for the systematic de novo identification of regulatory motifs. The methods do not rely on previous knowledge of gene function and in that way differ from the current literature on computational motif discovery. Based on the genome-wide conservation patterns of known motifs, we developed three conservation criteria that we used to discover novel motifs. We used an enumeration approach to select strongly conserved motif cores, which we extended and collapsed into a small number of candidate regulatory motifs. These include most previously known regulatory motifs as well as several noteworthy novel motifs. The majority of discovered motifs are enriched in functionally related genes, allowing us to infer a candidate function for novel motifs.</p>
<p>Our results demonstrate the power of comparative genomics to further our understanding of any species. Our methods are validated by the extensive experimental knowledge in yeast, and will be invaluable in the study of complex genomes like that of human.</p><p>Address of the bookmark: <a href="http://web.mit.edu/manoli/www/publications/Kellis_JCB_04.pdf" rel="nofollow">http://web.mit.edu/manoli/www/publications/Kellis_JCB_04.pdf</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/38418/charles-swanton-lab</guid>
  <pubDate>Tue, 11 Dec 2018 08:09:22 -0600</pubDate>
  <link></link>
  <title><![CDATA[CHARLES SWANTON LAB]]></title>
  <description><![CDATA[
<p>They are using the latest DNA sequencing technology to read the genetic makeup of cancer cells within tumours in ever greater detail, teasing out patterns of evolution (evolutionary rule books), cancer heterogeneity and working out what changes have happened as a tumour evolves. We’re also investigating the processes that cause mutations and accelerate tumour evolution and working out how they might be stopped. And we are running evolutionary clinical trials with immune and targeted therapies to bring the benefits of our work to patients as quickly as possible.</p>

<p>https://www.crick.ac.uk/research/labs/charles-swanton</p>
]]></description>
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