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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/10243?offset=100</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/35747/webinar-on-rna-seq-data-analysis-on-28-feb-2018</guid>
	<pubDate>Thu, 22 Feb 2018 06:38:48 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/35747/webinar-on-rna-seq-data-analysis-on-28-feb-2018</link>
	<title><![CDATA[Webinar on RNA-Seq Data Analysis on 28 Feb 2018]]></title>
	<description><![CDATA[<p>Strand NGS is a biologist friendly NGS analysis tool that allows biologists to analyze their data using a very intuitive workflow for the analysis and visualization of RNA-Seq data. This webinar will give an overview of the workflow which includes Transcriptome/ Genome alignment, Differential expression analysis, Splicing events and gene fusion detection. Strand NGS also supports novel discovery like identification of novel genes, exons and novel splice junctions.<br />We will highlight the use of Strand NGS features such as PCA, sample correlation, clustering, Venn diagrams, CVA, UMI support and elastic genome browser used in RNA-Seq workflow that supports large scale RNA-Seq data analysis too. The tool also supports biological contextualization on the set of interesting genes from the data by allowing downstream analysis such as GO and pathway analysis. The product has an option to create pipelines for time consuming jobs which automates analysis and leaves more time for end data interpretation. This webinar will give an overview of the features in the RNA-Seq data analysis workflow in Strand NGS.</p><p>Details:<br /><a href="http://www.strand-ngs.com/webinar_registration">Session 1: </a>28 Feb 2018, 9 AM CET<br /><a href="http://www.strand-ngs.com/webinar_registration">Session 2:</a> 28 Feb 2018, 8 AM PST<br />Register here: http://www.strand-ngs.com/webinar_registration</p><p><span style="font-size: 12.8px;">About Speaker:</span></p><p>Dr. Suman Kapoor, Manager- Application Science at Strand Life Sciences, has over 11 years experience in molecular biology, next-generation sequencing based testing, clinical genomics, and personalized medicine for disease management and prenatal testing. Dr. Suman holds a Ph.D in Molecular and Cell Biology from Indian Institute of Science, Bangalore. Prior to joining Strand NGS team, Suman has worked extensively on protein synthesis in eubacteria and has experience working in CAP and NABL accredited lab validating and interpreting NGS based diagnostic tests.</p>]]></description>
	<dc:creator>Strand</dc:creator>
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<item>
	<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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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/view/1926</guid>
	<pubDate>Sun, 11 Aug 2013 11:42:32 -0500</pubDate>
	<link>https://bioinformaticsonline.com/view/1926</link>
	<title><![CDATA[Want to Know which genome assembler rule the world ?]]></title>
	<description><![CDATA[<p><span><strong>Assemblathon 2</strong>: evaluating de novo methods of genome assembly&nbsp;</span></p><p><span><a href="http://www.gigasciencejournal.com/content/2/1/10/abstract">http://www.gigasciencejournal.com/content/2/1/10/abstract</a></span></p><p><span><a href="http://blogs.nature.com/news/2013/07/genome-assembly-contest-prompts-soul-searching.html">http://blogs.nature.com/news/2013/07/genome-assembly-contest-prompts-soul-searching.html</a></span></p><p><a href="http://assemblathon.org/post/44431915644/feedback-and-analysis-of-the-assemblathon-2-p">http://assemblathon.org/post/44431915644/feedback-and-analysis-of-the-assemblathon-2-p</a></p><p>&nbsp;</p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/4195/barber-pole-worm-sheep-pathogen-sequenced</guid>
	<pubDate>Tue, 03 Sep 2013 16:32:18 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/4195/barber-pole-worm-sheep-pathogen-sequenced</link>
	<title><![CDATA[Barber pole worm , sheep pathogen sequenced !!!]]></title>
	<description><![CDATA[<p>Haemonchus contortus is a highly pathogenic parasitic nematode of that can infect a large number of wild and domesticated ruminant species and is the most economically important parasite of sheep and goats worldwide. Scientists at the Wellcome Trust Sanger Institute have sequenced the genome of the barber's pole worm (Haemonchus contortus), which will help to explore the this tropical parasite which&nbsp;been disseminated around the world by livestock movement.&nbsp;</p><p>H. contortus is a member of the superfamily trichostrongyloidea (Strongylida) which contains most of the economically important parasitic nematodes of grazing livestock. These parasites cost the global livestock industry billions of dollars per annum in lost production and drug costs.&nbsp;A common type of clover may be a preventative or palliative for the disease. However, some particular breeds of sheep, such as the Gulf Coast Native from the Southern United States, have been shown to have developed special resistance to H. contortus.</p><p>Getting the full genome can help to tackle the problem and understand the resistance mechanism with an ease. Moreover, the genome could now provide a comprehensive understanding of how treatments against parasitic worms work and point to further new treatments and vaccines.&nbsp;By comparing the genome of the barber's pole worm with those of worms that have acquired drug resistance, researchers expect to reveal information about how and why resistance has occurred. Till now, researchers have uncovered essential information in the fight against drug resistance in worms.</p><p>Reference:</p><p><a href="http://www.fwi.co.uk/articles/28/08/2013/140758/researchers-close-in-on-worm-resistance-in-sheep.htm">http://www.fwi.co.uk/articles/28/08/2013/140758/researchers-close-in-on-worm-resistance-in-sheep.htm</a></p><p><a href="http://www.sciencedaily.com/releases/2013/08/130828103351.htm?utm_source=feedburner&amp;utm_medium=feed&amp;utm_campaign=Feed%3A+sciencedaily%2Fplants_animals+(ScienceDaily%3A+Plants+%26+Animals+News)">http://www.sciencedaily.com/releases/2013/08/130828103351.htm?utm_source=feedburner&amp;utm_medium=feed&amp;utm_campaign=Feed%3A+sciencedaily%2Fplants_animals+(ScienceDaily%3A+Plants+%26+Animals+News)</a></p><p>Image source: Wikipedia</p><p><img src="http://upload.wikimedia.org/wikipedia/commons/8/8e/Haemonchus_contortus.jpg" alt="image" width="800" height="533" style="border: 0px; border: 0px;"></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/6896/dna-tale-of-3-to-4-years-old-serbia-boy</guid>
	<pubDate>Tue, 26 Nov 2013 17:34:00 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/6896/dna-tale-of-3-to-4-years-old-serbia-boy</link>
	<title><![CDATA[DNA tale of 3 to 4 years old Serbia boy]]></title>
	<description><![CDATA[<p><span>The genome of a young boy found underground at Mal&rsquo;ta near Lake Baikal of eastern Siberia around 24,000 years ago came out as close relative of Europeans and Native Indians.</span></p><p><span>Link:</span></p><p><span><a href="http://www.nytimes.com/2013/11/21/science/two-surprises-in-dna-of-boy-found-buried-in-siberia.html?_r=0">http://www.nytimes.com/2013/11/21/science/two-surprises-in-dna-of-boy-found-buried-in-siberia.html?_r=0</a></span></p><p>&nbsp;</p><p><a href="http://www.nature.com/nature/journal/vaop/ncurrent/full/nature12736.html">http://www.nature.com/nature/journal/vaop/ncurrent/full/nature12736.html</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/10238/tsetse-fly-genome-sequenced</guid>
	<pubDate>Fri, 25 Apr 2014 10:48:35 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/10238/tsetse-fly-genome-sequenced</link>
	<title><![CDATA[Tsetse Fly Genome sequenced]]></title>
	<description><![CDATA[<p><span><span>As it&nbsp;</span><a href="http://www.sciencemag.org/content/344/6182/380" target="_blank">reported online today</a><span>&nbsp;in&nbsp;</span><em>Science</em><span>, the team used several sequencing approaches to tackle the tsetse fly's 366 million base genome.</span></span></p><p><span>The current study, and companion articles slated to appear in&nbsp;</span><em>PLOS One</em><span>,&nbsp;</span><em>PLOS Genetics</em><span>, and&nbsp;</span><em>PLOS Neglected Tropic Diseases</em><span>, are the result of &nbsp;nearly 150 researchers based in 18 countries.</span></p><p><span>Source:</span></p><p><span>http://www.genomeweb.com/sequencing/international-team-sequences-tsetse-fly-genome</span></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/11249/how-to-sequence-the-human-genome-mark-j-kiel</guid>
	<pubDate>Fri, 30 May 2014 13:24:11 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/11249/how-to-sequence-the-human-genome-mark-j-kiel</link>
	<title><![CDATA[How to sequence the human genome - Mark J. Kiel]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/MvuYATh7Y74" frameborder="0" allowfullscreen></iframe>View full lesson: http://ed.ted.com/lessons/how-to-sequence-the-human-genome-mark-j-kiel

Your genome, every human's genome, consists of a unique DNA sequence of A's, T's, C's and G's that tell your cells how to operate. Thanks to technological advances, scientists are now able to know the sequence of letters that makes up an individual genome relatively quickly and inexpensively. Mark J. Kiel takes an in-depth look at the science behind the sequence.

Lesson by Mark J. Kiel, animation by Marc Christoforidis.]]></description>
	
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	<guid isPermaLink="true">https://bioinformaticsonline.com/file/view/13415/genomics-and-sequencing-approach-for-identification-of-biomarkers-to-assess-the-efficacy-of-tgf-%CE%B2ri-inhibitors-of-liver-cancer-in-vivo</guid>
	<pubDate>Tue, 05 Aug 2014 13:55:32 -0500</pubDate>
	<link>https://bioinformaticsonline.com/file/view/13415/genomics-and-sequencing-approach-for-identification-of-biomarkers-to-assess-the-efficacy-of-tgf-%CE%B2ri-inhibitors-of-liver-cancer-in-vivo</link>
	<title><![CDATA[Genomics and sequencing approach for identification of biomarkers to assess the efficacy of TGF-βRI inhibitors (of liver cancer) in vivo]]></title>
	<description><![CDATA[<p>Liver cancer is third leading cause of deaths and fourth most frequent occuring cancer worldwide. There are multiple signaling pathways responsible for causing cancer amongst which TGFb is most important cytokine whose signaling pathway promote cancer. However, main problem is to cure this cancer at late stage where we still have no treatment strategy to tackle this deadly cancer. &nbsp;Hence we need to find out new therapeutic target. One way is to look the relationships between mRNA, methylation and miRNA data of patients with different pathological conditions (cancer vs control either with inhibitor/not). MiRNA is small RNA molecules known to inhibit mRNA expression of particular gene by binding improperly to 3'UTR region of a gene and hence block binding of TF /translation of gene. CpG regions is known to located at promoter region of gene (5' UTR) and usually hypomethylated which allow to gene to transcribe and translate however sometime this region become hyper-methylated thats prevent expression of host gene. Thus , integration of these three data reveal new targets and pathways important for causing or preventing cancer and also reveal biomarker thats check the effects of inhibitor on signaling pathway underlying liver cancer.</p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
	<enclosure url="https://bioinformaticsonline.com/file/download/13415" length="26423" type="image/jpeg" />
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27323/cutadapt</guid>
	<pubDate>Fri, 13 May 2016 04:54:50 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27323/cutadapt</link>
	<title><![CDATA[cutadapt]]></title>
	<description><![CDATA[<p>Cutadapt finds and removes adapter sequences, primers, poly-A tails and other types of unwanted sequence from your high-throughput sequencing reads.</p>
<p>Cleaning your data in this way is often required: Reads from small-RNA sequencing contain the 3&rsquo; sequencing adapter because the read is longer than the molecule that is sequenced. Amplicon reads start with a primer sequence. Poly-A tails are useful for pulling out RNA from your sample, but often you don&rsquo;t want them to be in your reads.</p>
<p>Cutadapt helps with these trimming tasks by finding the adapter or primer sequences in an error-tolerant way. It can also modify and filter reads in various ways. Adapter sequences can contain IUPAC wildcard characters. Also, paired-end reads and even colorspace data is supported. If you want, you can also just demultiplex your input data, without removing adapter sequences at all.</p>
<p>Cutadapt comes with an extensive suite of automated tests and is available under the terms of the MIT license.</p>
<p>If you use cutadapt, please cite <a href="http://dx.doi.org/10.14806/ej.17.1.200">DOI:10.14806/ej.17.1.200</a> .</p><p>Address of the bookmark: <a href="https://cutadapt.readthedocs.io/en/stable/installation.html#quickstart" rel="nofollow">https://cutadapt.readthedocs.io/en/stable/installation.html#quickstart</a></p>]]></description>
	<dc:creator>Radha Agarkar</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32946/grass-a-generic-algorithm-for-scaffolding-next-generation-sequencing-assemblies</guid>
	<pubDate>Tue, 23 May 2017 05:20:32 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32946/grass-a-generic-algorithm-for-scaffolding-next-generation-sequencing-assemblies</link>
	<title><![CDATA[GRASS: a generic algorithm for scaffolding next-generation sequencing assemblies.]]></title>
	<description><![CDATA[<p><span>GRASS (GeneRic ASsembly Scaffolder)-a novel algorithm for scaffolding second-generation sequencing assemblies capable of using diverse information sources. GRASS offers a mixed-integer programming formulation of the contig scaffolding problem, which combines contig order, distance and orientation in a single optimization objective. The resulting optimization problem is solved using an expectation-maximization procedure and an unconstrained binary quadratic programming approximation of the original problem. We compared GRASS with existing HTS scaffolders using Illumina paired reads of three bacterial genomes. Our algorithm constructs a comparable number of scaffolds, but makes fewer errors. This result is further improved when additional data, in the form of related genome sequences, are used.</span></p><p>Address of the bookmark: <a href="https://github.com/AlexeyG/GRASS" rel="nofollow">https://github.com/AlexeyG/GRASS</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
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