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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/41493?offset=710</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41452/apollo-a-sequencing-technology-independent-scalable-and-accurate-assembly-polishing-algorithm</guid>
	<pubDate>Mon, 16 Mar 2020 10:09:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41452/apollo-a-sequencing-technology-independent-scalable-and-accurate-assembly-polishing-algorithm</link>
	<title><![CDATA[Apollo: A Sequencing-Technology-Independent, Scalable, and Accurate Assembly Polishing Algorithm]]></title>
	<description><![CDATA[<p><span>Apollo is an assembly polishing algorithm that attempts to correct the errors in an assembly. It can take multiple set of reads in a single run and polish the assemblies of genomes of any size. Described by Firtina et al. (preliminary version at&nbsp;</span><a href="https://arxiv.org/pdf/1902.04341.pdf">https://arxiv.org/pdf/1902.04341.pdf</a></p>
<p>More at&nbsp;<a href="https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btaa179/5804978?rss=1">https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btaa179/5804978?rss=1</a></p><p>Address of the bookmark: <a href="https://github.com/CMU-SAFARI/Apollo" rel="nofollow">https://github.com/CMU-SAFARI/Apollo</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/43913/lsugenomics-lab</guid>
  <pubDate>Thu, 07 Jul 2022 05:26:37 -0500</pubDate>
  <link></link>
  <title><![CDATA[lsugenomics Lab]]></title>
  <description><![CDATA[
<p>﻿In our lab, we seek to characterize and to compare genomes in order to better understand genetic and evolutionary processes linking genotypes to phenotypes.  <br /> <br />Sequencing and decoding plant genomes have been integral in our approaches.</p>

<p>The overarching goal of our research is to understand how to interpret complex and fascinating messages embedded in genomes.</p>

<p>https://www.lsugenomics.org/</p>
]]></description>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/19695/china-university-of-macau-phd-position-2015-in-bioinformatics-computer-science</guid>
  <pubDate>Mon, 22 Dec 2014 00:12:49 -0600</pubDate>
  <link></link>
  <title><![CDATA[China University of Macau PhD Position 2015 in Bioinformatics, Computer Science]]></title>
  <description><![CDATA[
<p>The Computational Biology and Bioinformatics Group at the University of Macau is inviting applications for PhD Position. Applicants will work on a research project focusing on the flexible receptor protein-ligand docking algorithms for computer-aided drug design.  The candidate will be working as part of a team in developing novel metaheuristic algorithms and scoring functions for large-scale, highly flexible protein-ligand docking problems. The duration of this PhD position is 2-3 years, starting in August 2015. Remuneration paid to candidate is MOP 11000-14000/month (~USD 1375-1750/month). The applications should be submitted before March 2015.</p>

<p>Study Subject(s): PhD position is award in the field of Bioinformatics/Computer Science.<br />Course Level: Position is available for pursuing PhD degree level at the University of Macau.<br />Scholarship Provider: University of Macau<br />Scholarship can be taken at: China</p>

<p>Eligibility: The ideal candidate would be a master degree holder in Bioinformatics or related disciplines with knowledge in Medical sciences or Life sciences (with GPA of at least 3.0 on a 4-point scale or equivalent) . Knowledge in programming (C and C++) and Linux scripting are necessary; experience in molecular docking, molecular dynamics simulations or molecular modeling is an advantage. The candidate should be fluent in spoken and written English; preference will be given to applicants with good publication records in relevant areas.</p>

<p>Scholarship Open for International Students: Researchers from China can apply for this PhD position.</p>

<p>Scholarship Description:</p>

<p>The Computational Biology and Bioinformatics Group at the University of Macau is looking for a motivated PhD student in Bioinformatics or Computer Science to work on a research project focusing on the flexible receptor protein-ligand docking algorithms for computer-aided drug design.  The candidate will be working as part of a team in developing novel metaheuristic algorithms and scoring functions for large-scale, highly flexible protein-ligand docking problems.</p>

<p>Number of award(s): There is only one PhD position available.</p>

<p>Duration of award(s): The duration of this PhD position is 2-3 years.</p>

<p>What does it cover? Remuneration paid to candidate is  MOP 11000-14000/month (~USD 1375-1750/month).</p>

<p>Selection Criteria: Not Known</p>

<p>Notification: Not Known</p>

<p>How to Apply: Send your current CV, your academic transcripts, a letter of motivation and research interests, two letters of recommendations from academic faculty to Dr. Shirley Siu at shirleysiu[at]umac.mo before March 2015.</p>

<p>Scholarship Application Deadline: The applications should be submitted before March 2015.</p>
]]></description>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/38682/bourque-lab</guid>
  <pubDate>Mon, 14 Jan 2019 15:39:25 -0600</pubDate>
  <link></link>
  <title><![CDATA[Bourque Lab]]></title>
  <description><![CDATA[
<p>The goal of the lab is to understand mammalian genomes using comparative genomic and epigenomic analyses. Areas of interest include: the evolution of regulatory sequences, the role of transposable elements in gene regulation and the impact of genome rearrangements in evolution and cancer.</p>

<p>As a computational genomicists our work involves examining billions of DNA base pairs and interpreting how variation impacts basic biology and disease. We develop computational methods and resources for the functional annotation of genomes with a special emphasis on sequencing-based assays (e.g. ChIP-seq, RNA-Seq, exome- and whole-genome sequencing, single-cell analysis).</p>

<p>http://www.computationalgenomics.ca</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43645/corona-virus-genome-and-data-download</guid>
	<pubDate>Sun, 12 Dec 2021 23:34:54 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43645/corona-virus-genome-and-data-download</link>
	<title><![CDATA[Corona Virus Genome and Data Download !]]></title>
	<description><![CDATA[<p>Genes and its related metadata could be found on&nbsp;https://www.ncbi.nlm.nih.gov/datasets/coronavirus/genomes/</p><p>Address of the bookmark: <a href="https://www.ncbi.nlm.nih.gov/datasets/coronavirus/genomes/" rel="nofollow">https://www.ncbi.nlm.nih.gov/datasets/coronavirus/genomes/</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43650/rules-for-pango-lineage</guid>
	<pubDate>Tue, 14 Dec 2021 04:40:26 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43650/rules-for-pango-lineage</link>
	<title><![CDATA[Rules for Pango Lineage !]]></title>
	<description><![CDATA[<p>All the rules to classify a Lineage !</p>
<p>https://www.pango.network/the-pango-nomenclature-system/statement-of-nomenclature-rules/</p><p>Address of the bookmark: <a href="https://www.pango.network/the-pango-nomenclature-system/statement-of-nomenclature-rules/" rel="nofollow">https://www.pango.network/the-pango-nomenclature-system/statement-of-nomenclature-rules/</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
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	<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>
	
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/20585/dna-transcription-advanced</guid>
	<pubDate>Thu, 29 Jan 2015 05:31:42 -0600</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/20585/dna-transcription-advanced</link>
	<title><![CDATA[DNA Transcription (Advanced)]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/SMtWvDbfHLo" frameborder="0" allowfullscreen></iframe><p>Transcription is the process by which the information in DNA is copied into messenger RNA (mRNA) for protein production. Originally created for DNA Interactive ( http://www.dnai.org ). TRANSCRIPT: The Central Dogma of Molecular Biology: "DNA makes RNA makes protein" Here the process begins. Transcription factors assemble at a specific promoter region along the DNA. The length of DNA following the promoter is a gene and it contains the recipe for a protein. A mediator protein complex arrives carrying the enzyme RNA polymerase. It manoeuvres the RNA polymerase into place... inserting it with the help of other factors between the strands of the DNA double helix. The assembled collection of all these factors is referred to as the transcription initiation complex... and now it is ready to be activated. The initiation complex requires contact with activator proteins, which bind to specific sequences of DNA known as enhancer regions. These regions may be thousands of base pairs distant from the start of the gene. Contact between the activator proteins and the initiation-complex releases the copying mechanism. The RNA polymerase unzips a small portion of the DNA helix exposing the bases on each strand. Only one of the strands is copied. It acts as a template for the synthesis of an RNA molecule which is assembled one sub-unit at a time by matching the DNA letter code on the template strand. The sub-units can be seen here entering the enzyme through its intake hole and they are joined together to form the long messenger RNA chain snaking out of the top.</p>]]></description>
	
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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>
</item>
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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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