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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/29384?offset=240</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43736/odgi-optimized-dynamic-genomegraph-implementation</guid>
	<pubDate>Tue, 01 Feb 2022 23:42:21 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43736/odgi-optimized-dynamic-genomegraph-implementation</link>
	<title><![CDATA[odgi: optimized dynamic genome/graph implementation]]></title>
	<description><![CDATA[<p dir="auto"><code>odgi</code>&nbsp;provides an efficient and succinct dynamic DNA sequence graph model, as well as a host of algorithms that allow the use of such graphs in bioinformatic analyses.</p>
<p dir="auto">Careful encoding of graph entities allows&nbsp;<code>odgi</code>&nbsp;to efficiently compute and transform&nbsp;<a href="https://pangenome.github.io/">pangenomes</a>&nbsp;with minimal overheads.&nbsp;<code>odgi</code>&nbsp;implements a dynamic data structure that leveraged multi-core CPUs and can be updated on the fly.</p>
<p dir="auto">The edges and path steps are recorded as deltas between the current node id and the target node id, where the node id corresponds to the rank in the global array of nodes. Graphs built from biological data sets tend to have local partial order and, when sorted, the deltas be small. This allows them to be compressed with a variable length integer representation, resulting in a small in-memory footprint at the cost of packing and unpacking.</p>
<p dir="auto">The RAM and computational savings are substantial. In partially ordered regions of the graph, most deltas will require only a single byte.</p><p>Address of the bookmark: <a href="https://github.com/pangenome/odgi" rel="nofollow">https://github.com/pangenome/odgi</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/10841/ra-at-iisr-kozhikode</guid>
  <pubDate>Thu, 15 May 2014 10:08:09 -0500</pubDate>
  <link></link>
  <title><![CDATA[RA at IISR Kozhikode]]></title>
  <description><![CDATA[
<p>INDIAN INSTITUTE OF SPICES RESEARCH<br />(Indian Council of Agricultural Research)<br />Marikunnu P.O., Kozhikode – 673 012, Kerala</p>

<p>Walk- in- Test cum Interview (based on test) for the selection of Research Associate</p>

<p>under the scheme “Distributed Information Sub Centre –DISC” &amp; Research Assistant under scheme “Phytophthora, Fusarium and Ralstonia diseases of Horticultural and Field Crops” will be held at this Institute as per details indicated below.</p>

<p>WALK -IN- TEST CUM INTERVIEW</p>

<p>Name of the post : Research Associate</p>

<p>Date of Interview : 21-05-2014 at 10.00 AM</p>

<p>No. of posts : One</p>

<p>Qualifications : a)Essential</p>

<p>Ph.D Degree in Bioinformatics OR :  Masters degree in Bioinformatics with a minimum of<br />60% marks or equivalent OGPA with at least two years research experience as evidenced from fellowship/ associateship/training/published papers etc.</p>

<p>b)Desirable: Experience in NGS data analysis.</p>

<p>Emoluments : Rs. 23,000/- per month + HRA (Masters Degree Holders)</p>

<p>Rs. 24,000/- per month + HRA (Ph.D Degree Holders)</p>

<p>Upper age limit : 40 years for Men &amp; 45 years for Women as on date of Interview (Upper Age limits are relaxable for SC, ST and OBC candidates as per Govt. of India norms (at present 5 years for SC/ST and 3 years for OBC)</p>

<p>Duration of Project : Till 31-03-2017.</p>

<p>Title of Assigment : Research Assistant (on contract basis)</p>

<p>No. of vacancy : One</p>

<p>Qualification : Essential : Post Graduation in Bioinformatics and  Minimum one year experience in NGS data analysis</p>

<p>Desirable : Experience in Perl/Python/R</p>

<p>Remuneration : Rs. 20,000/- per month (consolidated)</p>

<p>Scope of work :</p>

<p>1. Analysis of different file formats and their conversions.</p>

<p>2. Assessing the quality of data and filtering of raw reads.<br />3. Assembling the raw reads-de novo as well as reference  mapping.<br />4. Compression of aligned reads using Jam tools<br />5. RNA-seq. Analysis<br />6. Differential expression testing involving Normalization,  Statistical testing, heat map generation &amp; hierarchical  clustering<br />7. Annotating the assembled genome and geneet testing  and their validation<br />8. Metabolic pathway analysis<br />9. Comparative genomics<br />10. Setting up of genome browsers.</p>

<p>Period of Assigment : Initially for six months.</p>

<p>Date &amp; Venue of Interview : 21-05-2014 at IISR, Kozhikode at 10.00 AM</p>

<p>More at http://www.spices.res.in/pdf/disc-advtmnt.pdf</p>
]]></description>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43806/genomicus-genome-browser-that-enables-users-to-navigate-in-genomes-in-several-dimensions</guid>
	<pubDate>Mon, 28 Feb 2022 23:27:37 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43806/genomicus-genome-browser-that-enables-users-to-navigate-in-genomes-in-several-dimensions</link>
	<title><![CDATA[Genomicus: genome browser that enables users to navigate in genomes in several dimensions]]></title>
	<description><![CDATA[<p>Genomicus is a genome browser that enables users to navigate in genomes in several dimensions: linearly along chromosome axes, transversaly across different species, and chronologicaly along evolutionary time.</p>
<p>Once a query gene has been entered, it is displayed in its genomic context in parallel to the genomic context of all its orthologous and paralogous copies in all the other sequenced metazoan genomes. Moreover, Genomicus stores and displays the predicted ancestral genome structure in all the ancestral species within the phylogenetic range of interest.</p>
<p>All the data on extant species displayed in this browser are from&nbsp;<a href="http://www.ensembl.org/">Ensembl</a>.</p>
<p><br><strong>Summary statistics of Genomicus version 105.01:</strong><span>&nbsp;(view species tree in&nbsp;</span><a href="https://www.genomicus.bio.ens.psl.eu/genomicus-105.01/data/SpeciesTree.pdf">pdf</a><span>&nbsp;or&nbsp;</span><a href="https://www.genomicus.bio.ens.psl.eu/genomicus-105.01/data/SpeciesTree.nwk">newick</a><span>)</span><br><br></p>
<table id="introstats">
<tbody>
<tr><th>Number of extant species</th>
<td>200</td>
</tr>
<tr><th>Number of extant genes</th>
<td>4303993</td>
</tr>
<tr><th>&nbsp;</th></tr>
<tr><th>Number of ancestral species</th>
<td>196</td>
</tr>
<tr><th>Number of ancestral genes</th>
<td>4624213</td>
</tr>
<tr><th>Number of ancestral synteny blocks</th>
<td>83342<br><br></td>
</tr>
</tbody>
</table><p>Address of the bookmark: <a href="https://www.genomicus.bio.ens.psl.eu/genomicus-105.01/cgi-bin/search.pl" rel="nofollow">https://www.genomicus.bio.ens.psl.eu/genomicus-105.01/cgi-bin/search.pl</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/11355/genomics-and-personalized-medicine-breakthroughs</guid>
	<pubDate>Sun, 01 Jun 2014 23:40:14 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/11355/genomics-and-personalized-medicine-breakthroughs</link>
	<title><![CDATA[Genomics and Personalized Medicine Breakthroughs]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/VAR-1vNc0TE" frameborder="0" allowfullscreen></iframe>http://bit.ly/e8QGzY Human genome mapping is now enabling a breakthrough in medical innovation -- personalized medicine. What does this mean for patients? We can now identify predispositions to disease, predict how we metabolize drugs, and figure out what kinds of treatments we may respond to, and even determine when a drug may give us an adverse reaction. All medical specialties benefit from human genome intelligence -- oncology saw the first impacts -- but advances are now being seen in cardiology, obstetrics and gynecology, pediatric diseases, gastroenterology, rheumatology, immunology and other areas. This video covers the areas that genetic medicine is impacting and where the future of genomic medicine is heading.]]></description>
	
</item>
<item>
	<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>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/12288/genomic-medicine-bruce-korf-2014</guid>
	<pubDate>Tue, 24 Jun 2014 07:58:44 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/12288/genomic-medicine-bruce-korf-2014</link>
	<title><![CDATA[Genomic Medicine - Bruce Korf (2014)]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/FYldIrsXHKw" frameborder="0" allowfullscreen></iframe>May 21, 2014 - Current Topics in Genome Analysis 2014
A lecture series covering contemporary areas in genomics and bioinformatics. More: http://www.genome.gov/COURSE2014]]></description>
	
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35571/medusa-a-multi-draft-based-scaffolder</guid>
	<pubDate>Wed, 14 Feb 2018 02:49:00 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35571/medusa-a-multi-draft-based-scaffolder</link>
	<title><![CDATA[MeDuSa: a multi-draft based scaffolder]]></title>
	<description><![CDATA[<p><span>MeDuSa (Multi-Draft based Scaffolder), an algorithm for genome scaffolding. MeDuSa exploits information obtained from a set of (draft or closed) genomes from related organisms to determine the correct order and orientation of the contigs. MeDuSa formalises the scaffolding problem by means of a combinatorial optimisation formulation on graphs and implements an efficient constant factor approximation algorithm to solve it. In contrast to currently used scaffolders, it does not require either prior knowledge on the microrganisms dataset under analysis (e.g. their phylogenetic relationships) or the availability of paired end read libraries.&nbsp;</span></p><p>Address of the bookmark: <a href="https://github.com/combogenomics/medusa" rel="nofollow">https://github.com/combogenomics/medusa</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/12787/integrative-genomics-viewer-igv-tutorial</guid>
	<pubDate>Sat, 12 Jul 2014 15:16:23 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/12787/integrative-genomics-viewer-igv-tutorial</link>
	<title><![CDATA[Integrative Genomics Viewer (IGV) tutorial]]></title>
	<description><![CDATA[<p>The <a href="http://www.broadinstitute.org/igv/">Integrative Genomics Viewer (IGV)</a> from the Broad Center allows you to view several types of data files involved in any NGS analysis that employs a reference genome, including how reads from a dataset are mapped, gene annotations, and predicted genetic variants.</p>
<p>http://www.broadinstitute.org/igv/</p><p>Address of the bookmark: <a href="https://wikis.utexas.edu/display/bioiteam/Integrative+Genomics+Viewer+%28IGV%29+tutorial" rel="nofollow">https://wikis.utexas.edu/display/bioiteam/Integrative+Genomics+Viewer+%28IGV%29+tutorial</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/12963/cosmos-our-workflow-management-system-for-ngs-data</guid>
	<pubDate>Wed, 23 Jul 2014 07:29:14 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/12963/cosmos-our-workflow-management-system-for-ngs-data</link>
	<title><![CDATA[COSMOS, our workflow management system for NGS data]]></title>
	<description><![CDATA[<p><strong>COSMOS</strong>, our Python-based management system for implementing large-scale parallel workflows focusing on, but not restricted to, large-scale short-read "NGS" sequencing data is open-access published via <a href="http://bioinformatics.oxfordjournals.org/content/early/2014/06/29/bioinformatics.btu385.abstract">Advance Access</a> in <em>Bioinformatics</em> (<a href="http://scholar.harvard.edu/lancaster/publications/cosmos-python-library-massively-parallel-workflows">Gafni et al. 2014</a>).&nbsp; It is also available for download for non-commercial academic and research purposes at:</p>
<p><strong>&nbsp;<a href="http://cosmos.hms.harvard.edu/">http://cosmos.hms.harvard.edu/</a></strong>.</p><p>Address of the bookmark: <a href="https://cosmos.hms.harvard.edu/" rel="nofollow">https://cosmos.hms.harvard.edu/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36592/lachesis-genome-assembly-with-hi-c-based-contact-probability-maps-lachesis</guid>
	<pubDate>Mon, 14 May 2018 04:26:30 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36592/lachesis-genome-assembly-with-hi-c-based-contact-probability-maps-lachesis</link>
	<title><![CDATA[LACHESIS: Genome Assembly with Hi-C-based Contact Probability Maps (LACHESIS)]]></title>
	<description><![CDATA[<p>LACHESIS is method that exploits contact probability map data (e.g. from Hi-C) for chromosome-scale&nbsp;<em>de novo</em>&nbsp;genome assembly.</p>
<p>Further information about LACHESIS, including source code, documentation and a user's guide are available at:&nbsp;<a href="http://shendurelab.github.io/LACHESIS/">http://shendurelab.github.io/LACHESIS</a>.</p>
<p>Manuscript describing LACHESIS was published as: Burton JN#, Adey A, Patwardhan RP, Qiu R, Kitzman JO, Shendure J#.&nbsp;<em>Chromosome-scale scaffolding of de novo genome assemblies based on chromatin interactions.</em>&nbsp;Nature Biotechnology 2013 Dec;31(12):1119-25. doi:&nbsp;<a href="http://dx.doi.org/10.1038/nbt.2727">10.1038/nbt.272</a>. PubMed PMID:&nbsp;<a href="http://www.ncbi.nlm.nih.gov/pubmed/24185095">24185095</a>.</p>
<p>&nbsp;</p>
<p>http://shendurelab.github.io/LACHESIS/</p><p>Address of the bookmark: <a href="http://shendurelab.github.io/LACHESIS/" rel="nofollow">http://shendurelab.github.io/LACHESIS/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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