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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/2518?offset=150</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44503/entire-human-genome-sequencing</guid>
	<pubDate>Tue, 02 Apr 2024 01:19:29 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44503/entire-human-genome-sequencing</link>
	<title><![CDATA[Entire Human Genome Sequencing !]]></title>
	<description><![CDATA[<p>Cost-effective whole human genome sequencing has revolutionized the landscape of genetic research and personalized medicine by making comprehensive genetic analysis accessible to a wider population. Through advancements in sequencing technologies, such as next-generation sequencing (NGS), costs have significantly decreased, enabling researchers and healthcare providers to analyze an individual's complete genetic makeup with greater efficiency and affordability. This has profound implications for disease diagnosis, prognosis, and treatment, as it allows for the identification of genetic predispositions and the customization of healthcare interventions based on an individual's unique genetic profile. Moreover, as the cost continues to decline, the potential for population-scale genomic studies and large-scale screening programs becomes increasingly feasible, promising to further enhance our understanding of human genetics and improve healthcare outcomes on a global scale.</p><p>Here are few companies:</p><p>https://mynucleus.com/</p><p>https://myome.com/</p><p>https://nebula.org/whole-genome-sequencing-dna-test/</p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/2631/what-junk-dna-it%E2%80%99s-an-operating-system</guid>
	<pubDate>Mon, 19 Aug 2013 15:24:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/2631/what-junk-dna-it%E2%80%99s-an-operating-system</link>
	<title><![CDATA[What Junk DNA? It’s an Operating System]]></title>
	<description><![CDATA[<p>The report adds to growing experimental support for the idea that all that extra stuff in the human genes, once referred to as &ldquo;junk DNA,&rdquo; is more than functionless, space-filling material that happens to make up nearly 98% of the genome. The paper adds to a growing body of knowledge establishing a considerable role for this material in the regulation of gene expression and its potential role in human disease.</p><p>Address of the bookmark: <a href="http://www.genengnews.com/keywordsandtools/print/3/32115/" rel="nofollow">http://www.genengnews.com/keywordsandtools/print/3/32115/</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/2991/illumina-reveals-first-dataset-of-long-reads</guid>
	<pubDate>Fri, 23 Aug 2013 06:29:14 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/2991/illumina-reveals-first-dataset-of-long-reads</link>
	<title><![CDATA[Illumina reveals first dataset of long reads]]></title>
	<description><![CDATA[<p>With the help of Moleculo technology , acquired by Illumina releases new service for long reads sequencing i.e., &nbsp;<a href="http://www.illumina.com/services/long-read-sequencing-service.ilmn">FastTrack Long Reads</a>.</p><p>Average read length is around<span>&nbsp;8,500 base pairs in release dataset.</span>&nbsp;Best thing about this, there is not much effect on cost and quality of data.</p><p>You can also check following pages for publications on long reads and more:</p><p><a href="http://www.illumina.com/services/long-read-sequencing-service.ilmn">http://www.illumina.com/services/long-read-sequencing-service.ilmn</a></p><p><a href="http://blog.basespace.illumina.com/2013/07/22/first-data-set-from-fasttrack-long-reads-early-access-service/">http://blog.basespace.illumina.com/2013/07/22/first-data-set-from-fasttrack-long-reads-early-access-service/</a></p><p>&nbsp;</p><p>&nbsp;</p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/4212/eivind-hovigs-lab</guid>
  <pubDate>Tue, 03 Sep 2013 19:06:29 -0500</pubDate>
  <link></link>
  <title><![CDATA[Eivind Hovig's Lab]]></title>
  <description><![CDATA[
<p>Bioinformatics relevant research topics are:</p>

<p>genomic scale studies<br />endogenous mechanisms of mutations, germ line and somatic <br />computational aspects of immunology in cancer <br />signalling networks<br />three-dimensional organization of information in the nucleus<br />gene silencing<br />metastatic cross-talk<br />kinase signaling<br />personalized medicine<br />detection of biomarkers in cancer <br />historical DNA variation</p>

<p>From : http://www.ous-research.no/hovig/</p>

<p>Group address:<br />Eivind Hovig, The Norwegian Radium Hospital, Montebello, 0310 Oslo,Norway<br />Email: ehovig@radium.uio.no</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/5350/introduction-of-epigenomics</guid>
	<pubDate>Sun, 06 Oct 2013 04:59:30 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/5350/introduction-of-epigenomics</link>
	<title><![CDATA[Introduction of Epigenomics]]></title>
	<description><![CDATA[<ul>
<li><a href="http://www.genome.gov/27532724#al-1">What is the epigenome?</a></li>
<li><a href="http://www.genome.gov/27532724#al-2">What does the epigenome do?</a></li>
<li><a href="http://www.genome.gov/27532724#al-3">What makes up the epigenome?</a></li>
<li><a href="http://www.genome.gov/27532724#al-4">Is the epigenome inherited?</a></li>
<li><a href="http://www.genome.gov/27532724#al-5">What is imprinting?</a></li>
<li><a href="http://www.genome.gov/27532724#al-6">Can the epigenome change?</a></li>
<li><a href="http://www.genome.gov/27532724#al-7">What makes the epigenome change?</a></li>
<li><a href="http://www.genome.gov/27532724#al-8">How do changes in the epigenome contribute to cancer?</a></li>
<li><a href="http://www.genome.gov/27532724#al-9">How are researchers exploring the epigenome?</a></li>
</ul><p>Address of the bookmark: <a href="http://www.genome.gov/27532724" rel="nofollow">http://www.genome.gov/27532724</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/10379/your-stressdepression-came-from-ancestor</guid>
	<pubDate>Sun, 04 May 2014 18:46:20 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/10379/your-stressdepression-came-from-ancestor</link>
	<title><![CDATA[Your stress/depression came from ancestor]]></title>
	<description><![CDATA[<p>"A study published in&nbsp;<em>Nature Neuroscience</em>&nbsp;finds that stress in early life alters the production of small RNAs, called microRNAs, in the sperm of mice. The mice show depressive behaviours that persist in their progeny."</p><p>Source:</p><p>http://www.nature.com/news/sperm-rna-carries-marks-of-trauma-1.15049</p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33826/geneprof-analysis-of-high-throughput-sequencing-experiment</guid>
	<pubDate>Wed, 05 Jul 2017 16:47:05 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33826/geneprof-analysis-of-high-throughput-sequencing-experiment</link>
	<title><![CDATA[GeneProf: analysis of high-throughput sequencing experiment]]></title>
	<description><![CDATA[<div>GeneProf is a web-based, graphical software suite that allows users to analyse data produced using high-throughput sequencing platforms (RNA-seq and ChIP-seq; "Next-Generation Sequencing" or NGS): Next-gen analysis for next-gen data!</div>
<p>Some of GeneProf's highlights include:</p>
<ul>
<li><strong>Easy-to-use web-based interface:</strong>Access your data at any time from any computer with a working internet connection -- no need to install software! (cp.&nbsp;<a href="https://www.geneprof.org/GeneProf/help_introduction.jsp#section:SystemRequirements">Section 'System Requirements'</a>).</li>
<li><strong>Analysis wizards make your life easy:</strong>Step-by-step workflows make it easy to analyse high-throughput data within a minimum of hands-on time. (cp.&nbsp;<a href="https://www.geneprof.org/GeneProf/help_conceptsexplained.jsp#subconcept:AnalysisWizards">SubConcept 'Analysis Wizards'</a>).</li>
<li><strong>Versatile modules:</strong>Advanced users and data analysis experts benefit from GeneProf's broad range of analysis modules, which can be combined freely into sophisticated workflows (cp.&nbsp;<a href="https://www.geneprof.org/GeneProf/help_conceptsexplained.jsp#concept:Workflows">Concept 'Workflows'</a>).</li>
<li><strong>Integrated Analysis:</strong>Analysis of&nbsp;<em>ChIP-seq</em>&nbsp;and&nbsp;<em>RNA-seq</em>&nbsp;data in one place, plus support for the integration of other external data (e.g. from microarrays).</li>
<li><strong>Comprehensive Resource:</strong>GeneProf provides a comprehensive resource of&nbsp;<em>fully analyzed</em>&nbsp;next-generation sequencing data. Experimental results can be easily accessed and compared and the analysis procedures employed to produce the data are fully transparent (cp.&nbsp;<a href="https://www.geneprof.org/GeneProf/help_tutorials.jsp#tutorial:ExaminingPublicNext-GenDatausingGeneProf">Tutorial 'Examining Public Next-Gen Data..'</a>).</li>
<li><strong>Extensibility:</strong>Algorithm developers and computer programmers can develop their own modules and extend GeneProf. Existing software can be easily wrapped in the workflow framework (cp.&nbsp;<a href="https://www.geneprof.org/GeneProf/help_advancedtopics.jsp#section:ModuleDevelopment:AddingnewFunctionalitytoGeneProf">Section 'Module Development: Adding new..'</a>) and data from GeneProf may be used externally (cp.&nbsp;<a href="https://www.geneprof.org/GeneProf/help_advancedtopics.jsp#section:WebAPI:RetrievingDatafromGeneProf">Section 'Web API: Retrieving Data from ..'</a>).</li>
</ul>
<p>&nbsp;</p>
<p>GeneProf is academic software developed at the&nbsp;<a href="http://www.crm.ed.ac.uk/">Centre for Regenerative Medicine</a>&nbsp;/&nbsp;<a href="http://www.crm.ed.ac.uk/about/institute-stem-cell-research">Institute for Stem Cell Research</a>,&nbsp;<a href="http://www.ed.ac.uk/">University of Edinburgh</a>&nbsp;and has benefited from funding by the&nbsp;<a href="http://www.mrc.ac.uk/">Medical Research Council</a>&nbsp;and the&nbsp;<a href="http://www.eurosystemproject.eu/">EU Framework 7 Project "EuroSyStem"</a>.</p><p>Address of the bookmark: <a href="https://www.geneprof.org/GeneProf/index.jsp" rel="nofollow">https://www.geneprof.org/GeneProf/index.jsp</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34445/inc-seq-accurate-single-molecule-reads-using-nanopore-sequencing</guid>
	<pubDate>Mon, 27 Nov 2017 10:38:56 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34445/inc-seq-accurate-single-molecule-reads-using-nanopore-sequencing</link>
	<title><![CDATA[INC-Seq: accurate single molecule reads using nanopore sequencing]]></title>
	<description><![CDATA[<p><span>INC-Seq reads enabled accurate species-level classification, identification of species at 0.1&nbsp;% abundance and robust quantification of relative abundances, providing a cheap and effective approach for pathogen detection and microbiome profiling on the MinION system.</span></p><p>Address of the bookmark: <a href="https://github.com/CSB5/INC-Seq" rel="nofollow">https://github.com/CSB5/INC-Seq</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36621/hapcut2-robust-and-accurate-haplotype-assembly-for-diverse-sequencing-technologies</guid>
	<pubDate>Tue, 15 May 2018 07:35:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36621/hapcut2-robust-and-accurate-haplotype-assembly-for-diverse-sequencing-technologies</link>
	<title><![CDATA[HapCUT2: robust and accurate haplotype assembly for diverse sequencing technologies]]></title>
	<description><![CDATA[HapCUT2 is a maximum-likelihood-based tool for assembling haplotypes from DNA sequence reads, designed to "just work" with excellent speed and accuracy. We found that previously described haplotype assembly methods are specialized for specific read technologies or protocols, with slow or inaccurate performance on others. With this in mind, HapCUT2 is designed for speed and accuracy across diverse sequencing technologies, including but not limited to:

NGS short reads (Illumina HiSeq)
clone-based sequencing (Fosmid or BAC clones)
SMRT reads (PacBio)
Oxford Nanopore reads
10X Genomics Linked-Reads
proximity-ligation (Hi-C) reads
high-coverage sequencing (&gt;40x coverage-per-SNP) using above technologies
combinations of the above technologies (e.g. scaffold long reads with Hi-C reads)
See below for specific examples of command line options and best practices for some of these technologies.

NOTE: At this time HapCUT2 is for diploid organisms only. VCF input should contain diploid variants.

If you use HapCUT2 in your research, please cite:

Edge, P., Bafna, V. &amp; Bansal, V. HapCUT2: robust and accurate haplotype assembly for diverse sequencing technologies. Genome Res. gr.213462.116 (2016). doi:10.1101/gr.213462.116<p>Address of the bookmark: <a href="https://github.com/vibansal/HapCUT2" rel="nofollow">https://github.com/vibansal/HapCUT2</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37498/nextsv-a-meta-caller-for-structural-variants-from-low-coverage-long-read-sequencing-data</guid>
	<pubDate>Mon, 06 Aug 2018 17:24:53 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37498/nextsv-a-meta-caller-for-structural-variants-from-low-coverage-long-read-sequencing-data</link>
	<title><![CDATA[NextSV: a meta-caller for structural variants from low-coverage long-read sequencing data]]></title>
	<description><![CDATA[<p>NextSV, a meta SV caller and a computational pipeline to perform SV calling from low coverage long-read sequencing data. NextSV integrates three aligners and three SV callers and generates two integrated call sets (sensitive/stringent) for different analysis purpose. The output of NextSV is in ANNOVAR-compatible bed format. Users can easily perform downstream annotation using ANNOVAR and disease gene discovery using Phenolyzer.</p>
<p>&nbsp;</p>
<h2>&nbsp;</h2><p>Address of the bookmark: <a href="https://github.com/Nextomics/NextSV" rel="nofollow">https://github.com/Nextomics/NextSV</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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