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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/29284?offset=1310</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/29407/live-webinar-on-rna-seq-data-analysis-on-9-nov-2016</guid>
	<pubDate>Wed, 19 Oct 2016 05:25:27 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/29407/live-webinar-on-rna-seq-data-analysis-on-9-nov-2016</link>
	<title><![CDATA[Live Webinar on RNA-Seq Data Analysis on 9 Nov 2016]]></title>
	<description><![CDATA[<p><strong><a href="http://www.strand-ngs.com/webinar_registration">Live Webinar on RNA-Seq Data Analysis</a></strong></p><p><a href="http://www.strand-ngs.com/webinar_registration">Abstract: </a>Strand NGS supports an extensive workflow for the analysis and visualization of RNA-Seq data. The workflow includes Transcriptome / Genome alignment, Differential expression analysis with Statistical approach and Splicing events detection. Strand NGS also supports novel discovery like identification of novel genes, exons and Novel splice junctions, alongside it can also detect gene fusion events. Further downstream analysis such as GO and pathway analysis can be performed on the set of interesting genes. 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 and also highlights on parameters within each feature that can be optimized depending on datasets and analysis needs.</p><p><a href="http://www.strand-ngs.com/webinar_registration">Speaker:</a> Mr. Sugandan Sivamani, Senior Application Scientist, Strand Life Sciences</p><p>Date: 9th Nov, <a href="http://www.strand-ngs.com/webinar_registration">Session 1</a> for SAPK/ APFO: 2:30 PM IST Date: 9th Nov, <a href="http://www.strand-ngs.com/webinar_registration">Session 2</a> for AFO/ EMEA: 9:00 AM PST</p><p>Register here <a href="http://www.strand-ngs.com/webinar_registration">http://www.strand-ngs.com/webinar_registration</a></p>]]></description>
	<dc:creator>Strand</dc:creator>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/22787/senior-technical-assistant-at-pondicherry-university</guid>
  <pubDate>Wed, 17 Jun 2015 20:46:12 -0500</pubDate>
  <link></link>
  <title><![CDATA[Senior Technical Assistant at Pondicherry University]]></title>
  <description><![CDATA[
<p>Senior Technical Assistant</p>

<p>Eligibility : BE/B.Tech(CSE, ECE, IT)</p>

<p>Location : Pondicherry</p>

<p>Last Date : 26 Jun 2015</p>

<p>Hiring Process : Face to Face Interview<br />Pondicherry University - Job DetailsDate of posting:19 May 15</p>

<p>Senior Technical Assistant Job position in Pondicherry University on temporary basis  </p>

<p>Project Title : "Bioinformatics National Certification (BINC) for certifying quality human resource in Bioinformatics"</p>

<p>Qualification : i) B.E/ B.Tech Computer Science/ Electronics &amp; Communication Engineering/ Information Technology with 55% or equivalent marks. ii) One year Experience in relevant field in Government/ Public Sector or reputed Private Organizations. Desirable : Working experience in JSP and ASP/PHP</p>

<p>No. of Post : 01</p>

<p>Department : Biotechnology</p>

<p>Pay : Rs. 27,800/- </p>

<p>Age Limit : 30 Yrs<br />How to apply</p>

<p>Both above-mentioned posts are purely on temporary basis, extended by one year based on performance and will be terminated with the completion of BINC Program at Pondicherry University. Interested Candidates may send their application in the prescribed format with self-attested copies of all mark sheets and certificates to Dr. Basant K. Tiwary, Coordinator (BINC), Centre for Bioinformatics, Pondicherry University, Puducherry-605 014 before June 26, 2015.</p>

<p>Click Here http://www.pondiuni.edu.in/news/requirement-post-one-senior-technical-assistant-one-computer-assistant-–-dept-biotechnologygove</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/33486/quick-next-generation-sequencing-ngs-terms-definition</guid>
	<pubDate>Fri, 09 Jun 2017 04:52:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/33486/quick-next-generation-sequencing-ngs-terms-definition</link>
	<title><![CDATA[Quick next generation sequencing (NGS) terms definition]]></title>
	<description><![CDATA[<p><strong>fragment size:</strong><span>&nbsp;the Illumina WGS protocol generates paired-end reads from both ends of longer fragments. The lengths of these fragments are assumed to be sampled from a normal distribution. Therefore, in the absence of structural variants, mapping locations of the paired ends span within an interval [&delta;min,&delta;max]. Most (&gt;90%) of paired-end reads are sampled from no-SV regions, therefore the fragment size distribution can be learned empirically for each WGS data set separately.</span><br /><br /><strong>concordant reads:</strong><span>&nbsp;a read pair is called concordant if they can be mapped to the reference genome as &ldquo;expected&rdquo;: (a) mapped to opposing strands where the upstream read is mapped to the forward strand and the downstream read is mapped to the reverse strand2, (b) the distance between ends is between the minimum and maximum expected fragment size.</span><br /><br /><strong>discordant reads:</strong><span>&nbsp;briefly, any non-concordant read pair is considered discordant. Note that, by definition, the discordant read pairs signal potential SVs. The sequence signature produced by these type of reads is known as read-pair signature.</span><br /><br /><strong>split reads:</strong><span>&nbsp;a read that can only be mapped to the reference genome by breaking into two sub-reads is called a split-read. These types of reads also indicate a potential SV or a short insertion or deletion (indel).</span><br /><br /><strong>read depth:</strong><span>&nbsp;number of reads that map within a region of the genome. Overall genome-wide read depth is also referred to as depth of coverage. It is expected that the number of reads that &ldquo;cover&rdquo; each base-pair to follow a Poisson distribution. Therefore, if the read depth over a certain region deviates significantly from this distribution, it signals for a potential copy number variation (CNV).</span></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/poll/view/22920/how-long-have-you-been-a-bioinformatics-scientist-for</guid>
	<pubDate>Tue, 23 Jun 2015 10:55:33 -0500</pubDate>
	<link>https://bioinformaticsonline.com/poll/view/22920/how-long-have-you-been-a-bioinformatics-scientist-for</link>
	<title><![CDATA[How long have you been a bioinformatics scientist for?]]></title>
	<description><![CDATA[<p>Most of the researcher have been a scientist whole life, but infact they actually started paying&nbsp; it with at certain time.So, how long have you been in bioinformatics domain now?</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36518/mix-combining-multiple-assemblies-from-ngs-data</guid>
	<pubDate>Tue, 08 May 2018 04:58:05 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36518/mix-combining-multiple-assemblies-from-ngs-data</link>
	<title><![CDATA[MIX: Combining multiple assemblies from NGS data]]></title>
	<description><![CDATA[<p>Mix is a tool that combines two or more draft assemblies, without relying on a reference genome and has the goal to reduce contig fragmentation and thus speed-up genome finishing. The proposed algorithm builds an extension graph where vertices represent extremities of contigs and edges represent existing alignments between these extremities. These alignment edges are used for contig extension. The resulting output assembly corresponds to a path in the extension graph that maximizes the cumulative contig length.</p>
<p>The Mix algorithm, approach and results were published in BMC bioinformatics :&nbsp;<a href="http://www.biomedcentral.com/1471-2105/14/S15/S16">http://www.biomedcentral.com/1471-2105/14/S15/S16</a>.</p><p>Address of the bookmark: <a href="https://github.com/cbib/MIX" rel="nofollow">https://github.com/cbib/MIX</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/22966/ra-bioinformatics-at-icged</guid>
  <pubDate>Sun, 28 Jun 2015 12:24:01 -0500</pubDate>
  <link></link>
  <title><![CDATA[RA Bioinformatics at ICGED]]></title>
  <description><![CDATA[
<p>Research Associate Position at ICGEB, New Delhi with Dr. Amit Sharma</p>

<p>Starting 15th July 2015, the position relates to a project specifically for in silico drug docking, screening, design, optimisation and linkage with active chemists. </p>

<p>Experience in many docking softwares and operating systems is essential. </p>

<p>Additional experience in bioinformatics and computational biology tools will be useful. </p>

<p>Submit curriculum vitae to: sb.icgeb@gmail.com</p>

<p>Closing date: 5 July 2015</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36884/halc-high-throughput-algorithm-for-long-read-error-correction</guid>
	<pubDate>Fri, 08 Jun 2018 10:47:41 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36884/halc-high-throughput-algorithm-for-long-read-error-correction</link>
	<title><![CDATA[HALC: High throughput algorithm for long read error correction]]></title>
	<description><![CDATA[HALC, a high throughput algorithm for long read error correction. HALC aligns the long reads to short read contigs from the same species with a relatively low identity requirement so that a long read region can be aligned to at least one contig region, including its true genome region’s repeats in the contigs sufficiently similar to it (similar repeat based alignment approach)

HALC was able to obtain 6.7-41.1% higher throughput than the existing algorithms while maintaining comparable accuracy. The HALC corrected long reads can thus result in 11.4-60.7% longer assembled contigs than the existing algorithms.<p>Address of the bookmark: <a href="https://github.com/lanl001/halc" rel="nofollow">https://github.com/lanl001/halc</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/23160/opencpu</guid>
	<pubDate>Sun, 05 Jul 2015 18:34:46 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/23160/opencpu</link>
	<title><![CDATA[OpenCPU]]></title>
	<description><![CDATA[<p>OpenCPU is a system for embedded scientific computing and reproducible research. The OpenCPU server provides a reliable and interoperable <a href="https://www.opencpu.org/api.html">HTTP API</a> for data analysis based on R.</p><p>The OpenCPU <a href="https://www.opencpu.org/jslib.html">JavaScript client library</a> provides the most seamless integration of R and JavaScript available today.</p><p>OpenCPU uses standard R packaging to develop, ship and deploy web applications. Several open source <a href="https://www.opencpu.org/apps.html">example apps</a> are available from Github.</p><p>Installing your own OpenCPU server is <a href="https://www.opencpu.org/download.html">super easy</a> and only takes a few minutes.</p><p>More at https://www.opencpu.org/</p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/28051/convert-ensembl-gtf-to-annotation-table-geneid-genesymbol-genewisechrlocation-geneclass-strand-raw</guid>
	<pubDate>Fri, 24 Jun 2016 18:08:49 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/28051/convert-ensembl-gtf-to-annotation-table-geneid-genesymbol-genewisechrlocation-geneclass-strand-raw</link>
	<title><![CDATA[Convert EnsEMBL GTF to Annotation table (Geneid, GeneSymbol, GeneWiseChrLocation, GeneClass, Strand) Raw]]></title>
	<description><![CDATA[<p><strong>Bash Script source:</strong></p><p>https://gist.github.com/santhilalsubhash/367befcf5216be4b1fd9</p><p>&nbsp;</p><p><strong>Information</strong>:</p><p>This script converts EnsEMBL GTF (Ex:&nbsp;<a href="https://gist.githubusercontent.com/santhilalsubhash/1e7cca357e52a181dc25/raw/cfb803e07900a2baefbb6534f1299fd30cb57a29/sample.GTF">https://gist.githubusercontent.com/santhilalsubhash/1e7cca357e52a181dc25/raw/cfb803e07900a2baefbb6534f1299fd30cb57a29/sample.GTF</a>) file to annotation table format. It generated two files<br />1) Transcript wise chromosome location with information about transcripts (Ex:&nbsp;<a href="https://gist.githubusercontent.com/santhilalsubhash/c7dec516e0338503a4b6/raw/de0af1a39f0005c4ce7321c5ae57fc8b4a14c7f4/sample.GTF_enst_annotation.txt">https://gist.githubusercontent.com/santhilalsubhash/c7dec516e0338503a4b6/raw/de0af1a39f0005c4ce7321c5ae57fc8b4a14c7f4/sample.GTF_enst_annotation.txt</a>)<br />2) Gene wise chromosome location with information about genes (Ex:&nbsp;<a href="https://gist.githubusercontent.com/santhilalsubhash/c92006c5080f0333bec2/raw/d16e0b2440d73b09b486d3c9751cdb248a73fa0b/sample.GTF_ensg_annotation.txt">https://gist.githubusercontent.com/santhilalsubhash/c92006c5080f0333bec2/raw/d16e0b2440d73b09b486d3c9751cdb248a73fa0b/sample.GTF_ensg_annotation.txt</a>)</p><p>Note: You can download GTF files from&nbsp;<a href="http://www.ensembl.org/info/data/ftp/index.html">http://www.ensembl.org/info/data/ftp/index.html</a></p>]]></description>
	<dc:creator>EagleEye</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/24042/research-associate-bioinformatician-university-of-bristol</guid>
  <pubDate>Wed, 26 Aug 2015 05:46:29 -0500</pubDate>
  <link></link>
  <title><![CDATA[Research Associate Bioinformatician @ University of Bristol]]></title>
  <description><![CDATA[
<p>This 0.5 fte role will have specific responsibility for the bioinformatic side of a Health Innovation Challenged Fund (HICF) research project investigating the application of Next Generation Sequencing (NGS) technologies to the analysis of Minimal Residual Disease (MRD) in childhood Acute Lymphoblastic Leukaemia (ALL). The successful candidate will be responsible for designing and implementing an analysis pipeline primarily to fit with the clinical need, but with the capacity to answer innovative research questions.</p>

<p>For informal enquiries please contact Anne Walsh via email: anne.walsh@bristol.ac.uk.</p>

<p>Apply at http://www.bris.ac.uk/jobs/find/details.html?nPostingID=3639&amp;nPostingTargetID=13346&amp;option=28&amp;sort=DESC&amp;respnr=1&amp;ID=Q50FK026203F3VBQBV7V77V83&amp;JobNum=ACAD101624&amp;Resultsperpage=10&amp;lg=UK&amp;mask=uobext</p>
]]></description>
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