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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/23590?offset=90</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40598/mitoz-a-toolkit-for-animal-mitochondrial-genome-assembly-annotation-and-visualization</guid>
	<pubDate>Fri, 24 Jan 2020 04:09:15 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40598/mitoz-a-toolkit-for-animal-mitochondrial-genome-assembly-annotation-and-visualization</link>
	<title><![CDATA[MitoZ: a toolkit for animal mitochondrial genome assembly, annotation and visualization]]></title>
	<description><![CDATA[<p><span>MitoZ is a Python3-based toolkit which aims to automatically filter pair-end raw data (fastq files), assemble genome, search for mitogenome sequences from the genome assembly result, annotate mitogenome (genbank file as result), and mitogenome visualization. MitoZ is available from&nbsp;</span><code>https://github.com/linzhi2013/MitoZ</code><span>.</span></p>
<p><span><a href="https://academic.oup.com/nar/article/47/11/e63/5377471">https://academic.oup.com/nar/article/47/11/e63/5377471</a></span></p><p>Address of the bookmark: <a href="https://github.com/linzhi2013/MitoZ" rel="nofollow">https://github.com/linzhi2013/MitoZ</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41009/genomics-public-data-links</guid>
	<pubDate>Thu, 13 Feb 2020 00:20:00 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41009/genomics-public-data-links</link>
	<title><![CDATA[genomics public data links !]]></title>
	<description><![CDATA[<p>List of publically available databases on google server.</p>
<p>More at <a href="https://software.broadinstitute.org/gatk/download/bundle">https://software.broadinstitute.org/gatk/download/bundle</a></p>
<p><a href="ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606/VCF/GATK/">ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606/VCF/GATK/</a>.</p>
<p><a href="ftp://ftp.broadinstitute.org/bundle/hg38/hg38bundle/">ftp://ftp.broadinstitute.org/bundle/hg38/hg38bundle/</a></p><p>Address of the bookmark: <a href="https://console.cloud.google.com/storage/browser/genomics-public-data/resources/broad/hg38/v0?pli=1" rel="nofollow">https://console.cloud.google.com/storage/browser/genomics-public-data/resources/broad/hg38/v0?pli=1</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/20363/postdoctoral-researcher-in-cancer-systems-biology</guid>
  <pubDate>Mon, 12 Jan 2015 01:44:11 -0600</pubDate>
  <link></link>
  <title><![CDATA[Postdoctoral Researcher in Cancer Systems Biology]]></title>
  <description><![CDATA[
<p>Postdoctoral Researcher in Cancer Systems Biology<br />Department of Oncology, Old Road Campus Research Building, Roosevelt Drive, Oxford<br />Grade 7: £30,434 - £37,394 with a discretionary range to £40,847 p.a.<br />Applications are invited for a Postdoctoral Researcher in Cancer Systems Biology to join a rapidly developing Bioinformatics Research Core group headed by Dr Anastasia Samsonova. The purpose of the role is to develop and deliver integrative approaches to dissect the complexity of cancer as a genomic disease. The research will focus on development and application of effective strategies for mining and integration of complex human *omics datasets and clinical/phenotypic data in cancer studies.</p>

<p>The role sits at the critical interface between genetics and cancer systems biology, and would suit a candidate who is interested in developing a career at the confluence of Statistics/Data Mining/Machine Learning and Biology. Ideally, you will have experience in development of analytical approaches to high-throughput and multivariate data mining and integration gained through a PhD (or equivalent) in a quantitative subject (eg mathematics, statistics, physics, engineering or computer science).</p>

<p>Experience of statistics and/or machine learning techniques is highly desirable as is evidence of prior experience of developing bioinformatics software and/or analysing complex *omics data sets. You will be able to work as part of a team and independently and deliver results to the required standard and schedule. You should be able to organise and prioritise your own work, as well as have excellent communication skills, both written and oral. The post will involve interactions with collaborators from such diverse fields as applied mathematics, statistics, computer science and medicine.</p>

<p>This is a full-time post, fixed-term until 31 March 2017. For informal enquiries, contact Dr Anastasia Samsonova (bioinformatics@oncology.ox.ac.uk).</p>

<p>All applicants must complete a short application form and upload a CV and supporting statement.</p>

<p>The closing date for applications is 12.00 noon on 26 January 2015.</p>

<p>More at https://www.recruit.ox.ac.uk/pls/hrisliverecruit/erq_jobspec_version_4.display_form</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/23582/integrative-rna-and-chip-seq-analysis-of-regulatory-t-cells</guid>
	<pubDate>Tue, 04 Aug 2015 05:03:19 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/23582/integrative-rna-and-chip-seq-analysis-of-regulatory-t-cells</link>
	<title><![CDATA[Integrative RNA and ChIP-Seq analysis of regulatory T-cells]]></title>
	<description><![CDATA[<p><a href="http://www.strand-ngs.com/learn/white-papers#rna-chip" target="_blank" title="strand ngs white paper">Integrative RNA and ChIP-Seq analysis of regulatory T-cells&nbsp;</a><span>, a Strand NGS application note describes how integrated multi-omics functionality in Strand NGS was used to find the regulatory role of FoxP3 in T-regulatory and T-helper cells. Learn how the gene expression profiles from RNA-Seq and FoxP3 DNA-protein binding sites from ChIP-Seq are integrated. For mor information,&nbsp;</span><a href="http://www.strand-ngs.com/contact/sales" target="_blank" title="strand ngs contact">please write to us</a></p>]]></description>
	<dc:creator>Strand</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/33917/webinar-on-leukocyte-immunobiology-helps-us-predict-post-operative-risk-using-pre-operative-markers-on-9-aug-8-am-pst</guid>
	<pubDate>Tue, 18 Jul 2017 08:21:50 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/33917/webinar-on-leukocyte-immunobiology-helps-us-predict-post-operative-risk-using-pre-operative-markers-on-9-aug-8-am-pst</link>
	<title><![CDATA[Webinar on Leukocyte immunobiology helps us predict post-operative risk using pre-operative markers on 9 Aug, 8 am PST]]></title>
	<description><![CDATA[<h2><strong><a href="http://www.strand-ngs.com/webinar_registration#registration-form">Free Live Webinar on Leukocyte immunobiology helps us predict post-operative risk using pre-operative markers on 9 Aug, 8 am PST</a></strong></h2><h2 id="Next-gen-seq"><em><a href="http://www.strand-ngs.com/webinar_registration">Speaker:</a></em></h2><p><strong>Mario Deng</strong><span>&nbsp;MD FACC FESC</span><br /><span>Professor of Medicine</span><br /><span>Advanced Heart Failure/Mechanical</span><br /><span>Support/Heart Transplant</span><br /><span>David Geffen School of&nbsp;</span><br /><span>Medicine at UCLA</span><br /><span>Ronald Reagan UCLA Medical Center</span></p><h2><em><a href="http://www.strand-ngs.com/webinar_registration">Abstract:</a></em></h2><div id="more-webinar"><p>Strand NGS supports a comprehensive and flexible RNA-Seq data analysis workflow consisting of Alignment, Quality Assessment, Filters, and a range of analysis and visualization options that help in studying a variety of samples and answering long-standing biological questions.</p></div><div><p>In this webinar, Dr. Deng will discuss the analysis of transcriptome, flow cytometry and cytokine data from pre-operative blood samples of advanced heart failure patients undergoing Mechanical Circulatory Support (MCS) surgery. He will discuss in detail the identification of prominent clinical variables, a set of transcriptome biomarkers, and their role in the context of systems biology. Finally, the application of Class Prediction algorithms in Strand NGS for identification of high-risk patients will be illustrated.</p><p>This immunobiology based study highlights the potential of machine learning techniques in clinical risk prediction and patient management, and from a clinician&rsquo; s perspective, the utility of biomarker discovery studies in helping patients make more informed decisions as a step towards personalized precision medicine.</p><p><em><a href="http://www.strand-ngs.com/webinar_registration#registration-form">Register here</a></em></p></div>]]></description>
	<dc:creator>Yeshodari</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/32253/webinar-on-fast-and-accurate-dna-variant-calling-on-26-apr-2017</guid>
	<pubDate>Fri, 21 Apr 2017 06:14:37 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/32253/webinar-on-fast-and-accurate-dna-variant-calling-on-26-apr-2017</link>
	<title><![CDATA[Webinar on Fast and Accurate DNA Variant Calling on 26 Apr 2017]]></title>
	<description><![CDATA[<p>Continuing our&nbsp;<a href="http://www.strand-ngs.com/webinar_registration">DNA-Seq webinar series</a>, we'll present Strand NGS v3.0 best-practices: a workflow that identifies highly accurate variants from raw reads. Our best practices workflow is twice as fast as its GATK counterpart, and results in precision/recall rates of up to 99%/98% on whole exome and whole genome samples. We'll also&nbsp;<a href="http://www.strand-ngs.com/webinar_registration">speak briefly</a>&nbsp;about some of the other features in v3.0 including one-shot pipelines, TSS plots, RNA-Seq performance improvements, and, for the first time, HGVS notations for SNP effect analysis.</p><p>Register here:&nbsp;<a href="http://www.strand-ngs.com/webinar_registration"></a><a href="http://www.strand-ngs.com/webinar_registration">http://www.strand-ngs.com/webinar_registration</a></p>]]></description>
	<dc:creator>Yeshodari</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/28940/webinar-on-implications-of-next-generation-sequencing-in-molecular-diagnosis-of-cancer-on-28-sep-2016</guid>
	<pubDate>Thu, 01 Sep 2016 01:52:25 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/28940/webinar-on-implications-of-next-generation-sequencing-in-molecular-diagnosis-of-cancer-on-28-sep-2016</link>
	<title><![CDATA[Webinar on Implications of Next Generation Sequencing in Molecular Diagnosis of Cancer on 28 Sep 2016]]></title>
	<description><![CDATA[<h3>Abstract:</h3><p>Genetic testing requires screening of the entire gene, which by conventional sequencing is time consuming and expensive. Next Generation Sequencing (NGS) based approaches increase the sensitivity of mutation detection, making it fast and cost-effective compared to the conventional tests performed in a reflex-testing mode. Strand NGS includes workflows with quality assessment and filter sections that do not require any manual intervention. Post-analytical workflows in Strand NGS allow users to execute sequence analysis with stringent filtering to eliminate false positive and low quality reads. This simplifies the analysis in large scale cohort settings, where every sample needs to be processed identically.</p><p>In this webinar we will discuss the implications of next generation sequencing based tests in multi-gene testing. We will also show how NGS based tests help to identify copy number variations, split read analysis and breakpoint identification. Finally, we will show a brief glimpse of Indian cohort data, where NGS based tests have shown improved mutation detection. In this webinar, we will present clinical case studies in on Hereditary Breast and Ovarian Cancer (HBOC) and Retinoblastoma patients to demonstrate how CNV analysis in Strand NGS enables researchers to detect and visualize copy number changes ranging from single exon to full gene.</p><h3>Speaker:</h3><p>Dr. Jaya Singh, Senior Scientist, Strand Life Sciences</p><p><strong>Date:</strong> <a href="http://www.strand-ngs.com/webinar_registration">28 September 2016</a></p><p><strong>Session1:</strong> <a href="http://www.strand-ngs.com/webinar_registration">2:30 PM IST</a></p><p><strong>Session2:</strong> <a href="http://www.strand-ngs.com/webinar_registration">10 PM IST</a></p><p><a href="http://www.strand-ngs.com/webinar_registration"><strong>Register here:</strong></a>&nbsp;http://www.strand-ngs.com/webinar_registration</p>]]></description>
	<dc:creator>Strand</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34504/minion-gc-an-r-script-to-do-some-qc-on-minion-data</guid>
	<pubDate>Sun, 03 Dec 2017 15:19:18 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34504/minion-gc-an-r-script-to-do-some-qc-on-minion-data</link>
	<title><![CDATA[MinION_GC: An R script to do some QC on MinION data]]></title>
	<description><![CDATA[<p><span>Other tools focus on getting data out of the fastq or fast5 files, which is slow and computationally intensive. The benefit of this approach is that it works on a single, small, .txt summary file. So it's a lot quicker than most other things out there: it takes about a minute to analyse a 4GB flowcell on my laptop.</span></p>
<p>https://github.com/roblanf/minion_qc</p><p>Address of the bookmark: <a href="https://github.com/roblanf/minion_qc" rel="nofollow">https://github.com/roblanf/minion_qc</a></p>]]></description>
	<dc:creator>Radha Agarkar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36478/the-marvel-assembler</guid>
	<pubDate>Fri, 04 May 2018 19:18:41 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36478/the-marvel-assembler</link>
	<title><![CDATA[The MARVEL assembler]]></title>
	<description><![CDATA[<p><span>MARVEL consists of a set of tools that facilitate the overlapping, patching, correction and assembly of noisy (not so noisy ones as well) long reads.</span></p>
<p>The assembly process can be summarized as follows:</p>
<ol>
<li>overlap</li>
<li>patch reads</li>
<li>overlap (again)</li>
<li>scrubbing</li>
<li>assembly graph construction and touring</li>
<li>optional read correction</li>
<li>fasta file creation</li>
</ol><p>Address of the bookmark: <a href="https://github.com/schloi/MARVEL" rel="nofollow">https://github.com/schloi/MARVEL</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36895/npscarf-real-time-scaffolder-using-spades-contigs-and-nanopore-sequencing-reads</guid>
	<pubDate>Mon, 11 Jun 2018 05:14:57 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36895/npscarf-real-time-scaffolder-using-spades-contigs-and-nanopore-sequencing-reads</link>
	<title><![CDATA[npScarf: real-time scaffolder using SPAdes contigs and Nanopore sequencing reads]]></title>
	<description><![CDATA[npScarf (jsa.np.npscarf) is a program that connect contigs from a draft genomes to generate sequences that are closer to finish. These pipelines can run on a single laptop for microbial datasets. In real-time mode, it can be integrated with simple structural analyses such as gene ordering, plasmid forming.<p>Address of the bookmark: <a href="http://japsa.readthedocs.io/en/latest/tools/jsa.np.npscarf.html" rel="nofollow">http://japsa.readthedocs.io/en/latest/tools/jsa.np.npscarf.html</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
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