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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/28290?offset=1250</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36239/scilifelab-tutorial-for-bioinformatics-analysis</guid>
	<pubDate>Tue, 17 Apr 2018 04:33:00 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36239/scilifelab-tutorial-for-bioinformatics-analysis</link>
	<title><![CDATA[SciLifeLab tutorial for bioinformatics analysis !]]></title>
	<description><![CDATA[<p>SciLifeLab is a national center for molecular biosciences with focus on health and environmental research.</p>
<h2 id="courses">Courses</h2>
<p><a href="http://uppnex.se/twiki/bin/view/Courses/">Old courses (2012-2014)</a></p>
<h3 id="metagenomics-workshop">Metagenomics Workshop</h3>
<p><a href="https://scilifelab.github.io/courses/Metagenomics/1511/">2015 November - Uppsala</a><br><a href="https://scilifelab.github.io/courses/Metagenomics/1611/">2016 November - Uppsala</a><br><a href="https://scilifelab.github.io/courses/Metagenomics/1711/">2017 November - Uppsala</a></p>
<h3 id="introduction-to-bioinformatics-using-ngs-data">Introduction to Bioinformatics Using NGS Data</h3>
<p><a href="https://scilifelab.github.io/courses/ngsintro/1502/">2015 February - Uppsala</a>&nbsp;<br><a href="https://scilifelab.github.io/courses/ngsintro/1505/">2015 May - Gothenburg</a><br><a href="https://scilifelab.github.io/courses/ngsintro/1509/">2015 September - Uppsala</a><br><a href="https://scilifelab.github.io/courses/ngsintro/1511/">2015 November - Lund</a><br><a href="https://scilifelab.github.io/courses/ngsintro/1601/">2016 January - Uppsala</a><br><a href="https://scilifelab.github.io/courses/ngsintro/1604/">2016 April - Link&ouml;ping</a><br><a href="https://scilifelab.github.io/courses/ngsintro/1609/">2016 September - Uppsala</a><br><a href="https://scilifelab.github.io/courses/ngsintro/1611/">2016 November - Ume&aring;</a><br><a href="https://scilifelab.github.io/courses/ngsintro/1701/">2017 January - Uppsala</a><br><a href="https://scilifelab.github.io/courses/ngsintro/1705/">2017 May - Gothenburg</a><br><a href="https://scilifelab.github.io/courses/ngsintro/1709/">2017 September - Lund</a><br><a href="https://scilifelab.github.io/courses/ngsintro/1711/">2017 November - Uppsala</a><br><a href="https://scilifelab.github.io/courses/ngsintro/1802/">2018 February - Uppsala</a></p>
<h3 id="introduction-to-genome-annotation">Introduction to Genome Annotation</h3>
<p><a href="https://scilifelab.github.io/courses/annotation/2015/">2015 April - Uppsala</a><br><a href="https://scilifelab.github.io/courses/annotation/2016/">2016 April - Uppsala</a><br><a href="https://scilifelab.github.io/courses/annotation/2017/">2017 April - Uppsala</a><br><a href="https://scilifelab.github.io/courses/annotation/2018/">2018 May - Uppsala</a></p>
<h3 id="de-novo-genome-assembly">De Novo Genome Assembly</h3>
<p><a href="https://scilifelab.github.io/courses/assembly/1611/">2016 November - Uppsala</a><br><a href="https://scilifelab.github.io/courses/assembly/2017-11-15/">2017 November - Uppsala</a></p>
<h3 id="rna-seq-course">RNA-seq course</h3>
<p><a href="https://scilifelab.github.io/courses/rnaseq/1510/">2015 October - Uppsala</a><br><a href="https://scilifelab.github.io/courses/rnaseq/1604/">2016 April - Uppsala</a><br><a href="https://scilifelab.github.io/courses/rnaseq/1610/">2016 October - Uppsala</a><br><a href="https://scilifelab.github.io/courses/rnaseq/1703/">2017 March - Uppsala</a><br><a href="https://scilifelab.github.io/courses/rnaseq/1711/">2017 November - Uppsala</a><br><a href="https://scilifelab.github.io/courses/rnaseq/labs">RNAseq tutorials</a></p>
<h3 id="r-programming-foundations-for-life-scientists">R Programming Foundations for Life Scientists</h3>
<p><a href="https://scilifelab.github.io/courses/r_programming/1611/">2016 November - Uppsala</a><br><a href="https://scilifelab.github.io/courses/r_programming/1703/">2017 Mars - Uppsala</a></p>
<h3 id="single-cell-rna-sequencing-analysis">Single cell RNA sequencing analysis</h3>
<p><a href="https://scilifelab.github.io/courses/scrnaseq/1710/">2017 October - Uppsala</a></p><p>Address of the bookmark: <a href="https://scilifelab.github.io/courses/" rel="nofollow">https://scilifelab.github.io/courses/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44539/bactopia-a-flexible-pipeline-for-complete-analysis-of-bacterial-genomes</guid>
	<pubDate>Wed, 15 May 2024 14:36:12 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44539/bactopia-a-flexible-pipeline-for-complete-analysis-of-bacterial-genomes</link>
	<title><![CDATA[Bactopia: a Flexible Pipeline for Complete Analysis of Bacterial Genomes]]></title>
	<description><![CDATA[<p dir="auto">Bactopia is a flexible pipeline for complete analysis of bacterial genomes. The goal of Bactopia is to process your data with a broad set of tools, so that you can get to the fun part of analyses quicker!</p>
<p dir="auto">Bactopia can be split into two main parts:&nbsp;<a href="https://bactopia.github.io/latest/beginners-guide/">Bactopia Analysis Pipeline</a>, and&nbsp;<a href="https://bactopia.github.io/latest/bactopia-tools/">Bactopia Tools</a>.</p>
<p dir="auto">Bactopia Analysis Pipeline is the main&nbsp;<em>per-isolate</em>&nbsp;workflow in Bactopia. Built with&nbsp;<a href="https://www.nextflow.io/">Nextflow</a>, input FASTQs (local or available from SRA/ENA) are put through numerous analyses including: quality control, assembly, annotation, minmer sketch queries, sequence typing, and more.</p>
<p dir="auto"><a href="https://github.com/bactopia/bactopia/blob/master/data/bactopia-workflow.png" target="_blank"><img src="https://github.com/bactopia/bactopia/raw/master/data/bactopia-workflow.png" alt="Bactopia Overview" style="border: 0px;"></a></p>
<p dir="auto">Bactopia Tools are a set a independent workflows fo</p><p>Address of the bookmark: <a href="https://github.com/bactopia/bactopia" rel="nofollow">https://github.com/bactopia/bactopia</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37636/department-of-genetics-genomics-and-bioinformatics-national-biotechnology-development-agency-nigeria</guid>
	<pubDate>Wed, 05 Sep 2018 10:48:25 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37636/department-of-genetics-genomics-and-bioinformatics-national-biotechnology-development-agency-nigeria</link>
	<title><![CDATA[DEPARTMENT OF GENETICS, GENOMICS AND BIOINFORMATICS, National Biotechnology Development Agency, Nigeria]]></title>
	<description><![CDATA[<p>The Genetics, Genomics &amp; Bioinformatics Department (GBBD) at NABDA is unique, encompassing all facets of modern genetics and bioinformatics research. Trans-disciplinary research being conducted in our laboratories would lead to cures for human diseases; improvements to crop and livestock quality and yield; creation of new technologies with applications to medicine; agriculture; environment; and industry.</p>
<p>Our capacity building activities covers both general and specialized topics in translational genetics, and is designed to better acquaint scientists and clinicians with the tools and technologies of genetics and genomics.</p>
<p><span>OUR RESEARCH ACTIVITIES INCLUDE:</span></p>
<div>
<ul>
<li>Biomedical Genetics: investigating genetic and environmental factors contributing to phenotypes with relevance to human health and disease.</li>
<li>Computation and Bioinformatics: develop new approaches for the management, analysis, and modelling of large, complex data sets.</li>
<li>Population and Quantitative Genetics: study of how genetic processes evolve to generate genetic variation in populations of organisms, and the effects on the patterning of variation within and between populations and specie, and</li>
<li>Genetic Engineering and Biotechnology: focuses on the research and innovation for industrial enzymes, biologics and biosimilars production.</li>
</ul>
<p>https://www.h3abionet.org/nabda</p>
</div><p>Address of the bookmark: <a href="http://www.nabda.gov.ng/departments/genetics-genomics-and-bioinformatics" rel="nofollow">http://www.nabda.gov.ng/departments/genetics-genomics-and-bioinformatics</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/38590/senior-bioinformatics-scientist-strand-life-sciences-bangalore-india</guid>
  <pubDate>Wed, 02 Jan 2019 09:23:49 -0600</pubDate>
  <link></link>
  <title><![CDATA[Senior Bioinformatics Scientist @ Strand Life Sciences -- Bangalore, India]]></title>
  <description><![CDATA[
<p>RESPONSIBILITIES<br />The candidate is expected to work on a variety of projects related to analysis of data from NGS, Mass Spectrometry, Flow Cytometry and other related modalities. The position expects hands-on work and a strong eye for detail. The candidate will be able to contribute to impactful work spanning patient care, clinical research, and new assay and method development.<br />REQUIREMENTS<br />A PhD in a quantitative field (statistics, math, bioinformatics, computer science, physics or similar) and work experience or post-doc experience handling high throughout genomics data.<br />PREFERENCES<br />Experience in working in inter-disciplinary groups and ability to author research publications are additional desired qualities.<br />LOCALE<br />The position is in Bangalore and reports to the Chief Scientific Officer.<br />HOW TO APPLY<br />Write to ramesh[at]strandls.com.</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33912/mesquite-a-modular-system-for-evolutionary-analysis</guid>
	<pubDate>Tue, 18 Jul 2017 07:42:46 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33912/mesquite-a-modular-system-for-evolutionary-analysis</link>
	<title><![CDATA[Mesquite: A modular system for evolutionary analysis]]></title>
	<description><![CDATA[<p><span>Mesquite is modular, extendible software for evolutionary biology, designed to help biologists organize and analyze comparative data about organisms. Its emphasis is on phylogenetic analysis, but some of its modules concern population genetics, while others do non-phylogenetic multivariate analysis. Because it is modular, the analyses available depend on the modules installed.</span></p>
<p><span>http://mesquiteproject.wikispaces.com/</span></p><p>Address of the bookmark: <a href="https://github.com/MesquiteProject/MesquiteCore/releases" rel="nofollow">https://github.com/MesquiteProject/MesquiteCore/releases</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/34465/rnaseq-data-analysis-links</guid>
	<pubDate>Mon, 27 Nov 2017 16:28:11 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/34465/rnaseq-data-analysis-links</link>
	<title><![CDATA[RNAseq data analysis links !]]></title>
	<description><![CDATA[<p>RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping.</p><p><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4728800/" target="_blank">A survey of best practices for RNA-seq data analysis</a></p><p><a href="http://www.bioconductor.org/help/workflows/rnaseqGene/" target="_blank">RNA-seq workflow: gene-level exploratory analysis and DE</a></p><p><a href="https://github.com/crazyhottommy/RNA-seq-analysis" target="_blank">RNAseq analysis notes from Tommy Tang</a></p><p><a href="http://web.stanford.edu/group/wonglab/doc/RNA-seq-talk-JSM2010.pdf" target="_blank">Analysis of RNA ‐ Seq Data</a></p><p><a href="https://f1000research.com/articles/5-1408/v2" target="_blank">RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR</a></p><p><a href="http://www.nature.com/nprot/journal/v7/n3/full/nprot.2012.016.html" target="_blank">Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks.</a></p><p><a href="https://www.ebi.ac.uk/training/online/course/ebi-next-generation-sequencing-practical-course/rna-sequencing/rna-seq-analysis-transcriptome" target="_blank">EBI RNA-Seq exercise</a></p><p><a href="https://f1000research.com/articles/5-1574/v1" target="_blank">An open RNA-Seq data analysis pipeline tutorial with an example</a></p><p><a href="https://ycl6.gitbooks.io/rna-seq-data-analysis/rna-seq_analysis_workflow.html" target="_blank">RNA-Seq Analysis Workflow</a></p><p><a href="http://www.nature.com/nprot/journal/v11/n9/full/nprot.2016.095.html" target="_blank">Transcript-level expression analysis of RNA-seq experiments</a></p>]]></description>
	<dc:creator>Robert M Willioms</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40489/machine-learning-training-and-courses-in-bioinformatics</guid>
	<pubDate>Tue, 31 Dec 2019 19:33:07 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40489/machine-learning-training-and-courses-in-bioinformatics</link>
	<title><![CDATA[Machine learning training and courses in bioinformatics !]]></title>
	<description><![CDATA[<p>Machine learning techniques have been successful in analyzing biological data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. In this class, we will learn basics about probabilistic models and machine learning techniques. We will focus on probabilistic models (Markov models, Hidden Markov models, and Bayesian networks) for biological sequence analysis and systems biology. Other machine learning techniques, such as Naive bayes, neural networks and SVMs will only be covered briefly.</p>
<p>More at&nbsp;http://homes.sice.indiana.edu/yye/lab/teaching/spring2017-I529/</p>
<p>More tutorial at&nbsp;</p>
<p><a href="http://calla.rnet.missouri.edu/cheng_courses/mlbioinfo/mlbioinfo.htm">http://calla.rnet.missouri.edu/cheng_courses/mlbioinfo/mlbioinfo.htm</a></p>
<p><a href="http://www.raetschlab.org/lectures/MLBioinformatics">http://www.raetschlab.org/lectures/MLBioinformatics</a></p>
<p><a href="http://www.raetschlab.org/lectures/bertinoro08">http://www.raetschlab.org/lectures/bertinoro08</a></p>
<p>Book at&nbsp;</p>
<p><a href="https://personal.utdallas.edu/~pradiptaray/teaching/7_deep_learning_bioinfo.pdf">https://personal.utdallas.edu/~pradiptaray/teaching/7_deep_learning_bioinfo.pdf</a></p><p>Address of the bookmark: <a href="http://homes.sice.indiana.edu/yye/lab/teaching/spring2017-I529/" rel="nofollow">http://homes.sice.indiana.edu/yye/lab/teaching/spring2017-I529/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/fun/view/42877/bioinformatician-on-valentines-day</guid>
	<pubDate>Sun, 14 Feb 2021 11:36:32 -0600</pubDate>
	<link>https://bioinformaticsonline.com/fun/view/42877/bioinformatician-on-valentines-day</link>
	<title><![CDATA[Bioinformatician on Valentine's Day]]></title>
	<description><![CDATA[<p>Once asked to a bioinformatician "How is ur Valentine's Day?"</p><blockquote><p>Bioinformatician replied:</p><p>if ($date == "Valentine's Day" &amp;&amp; $me =! Bioinformatician) {</p><p>rose_day(); promise_day(); kiss_day();</p><p>}</p><p>else {</p><p>hack_genome(); ko-fi(); youtube(); do_scripting(); sleep();</p><p>)</p></blockquote>]]></description>
	<dc:creator>BioQueen</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/42141/dbt-biotechnology-eligibility-test-bet-2020</guid>
	<pubDate>Fri, 21 Aug 2020 09:17:24 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/42141/dbt-biotechnology-eligibility-test-bet-2020</link>
	<title><![CDATA[DBT BIOTECHNOLOGY ELIGIBILITY TEST (BET) 2020]]></title>
	<description><![CDATA[<p><span>Ministry of Science &amp;Technology, Govt. of India</span></p><p><span>DBT-Junior Research Fellowship (DBT-JRF) in Biotechnology (2020)</span></p><p><span><span>BIOTECHNOLOGY ELIGIBILITY TEST (BET) 2020</span></span></p><p>Applications are invited from bonafide Indian citizens, residing in India for award of &ldquo;DBT-Junior Research Fellowship&rdquo; (DBT-JRF) for pursuing research in frontier areas of Biotechnology and Life Sciences. The candidates will be selected through &ldquo;Biotechnology Eligibility Test (BET)&rdquo;. Based on the performance in BET, two categories of merit list will be prepared (Category-I and Category-II). Government of India norms for reservation will be followed for selection. Candidates selected under category-I will be eligible to avail fellowship under the programme. These will be tenable at any University/Institute in India where the selected candidate registers for PhD Programme. Candidates selected under Category-II will be eligible to be appointed in any DBT sponsored project and avail fellowship from the project equivalent to NET/GATE, subject to selection through institutional selection process. There will be no binding on Principal Investigators of DBT sponsored projects to select JRF for their project from category-II list. Selection in category-II will not entitle student for any fellowship from DBT-JRF programme.</p><p><span>ELIGIBILITY</span></p><p><span>Qualification</span>: M.Sc./ M.Tech./ M.V.Sc. or equivalent degree/ Integrated BS-MS/ B.E./ B.Tech. in any discipline of&nbsp;<a href="https://www.biotecnika.org/category/jobs/biotech-jobs/">Biotechnology</a>, M.Sc./ M.Tech. Bioinformatics/ Computational Biology, students admitted under DBT supported Postgraduate Teaching Programs. M.Sc. Life Science/ Bioscience/ Zoology/ Botany/ Microbiology/ Biochemistry/ Biophysics and Masters in Allied areas of Biology/Life Sciences. Candidates appearing in the final year examination are also eligible to apply.</p><p><span>Marks</span>: Minimum 60% marks for General, EWS &amp; OBC category and 55% for SC/ ST/ Differently abled in aggregate (or equivalent grade).</p><p><span>Age Limit</span>: Upto 28 years as on the last date of application for General &amp; EWS category. Age relaxation of up to 5 years (33 years) for SC/ ST/ Differently Abled/ women candidates and upto 3 years (31 years) for OBC (Non-Creamy Layer) candidates.</p><p>For detailed procedure for filling the application form, payment of application fee and uploading of required documents/ certificates in the prescribed format, please visit:&nbsp;<span><a href="http://rcb.res.in/BET2020" target="_blank">http://rcb.res.in/BET2020</a></span>. A non-refundable and non-transferable application fee of Rs. 1000/-is payable online by General/ OBC/ EWS candidates and Rs 250/- by SC/ ST/ Differently abled candidates.</p><p><span>IMPORTANT DATES</span></p><table width="691">
<tbody>
<tr>
<td>Online Registration Start</td>
<td><span>April 20, 2020</span></td>
</tr>
<tr>
<td>Online Registration Close</td>
<td><span>May 18, 2020</span></td>
</tr>
<tr>
<td>BET 2020</td>
<td><span>June 30, 2020 (Tuesday)* Tentative</span></td>
</tr>
<tr>
<td>Display of question paper and answer key on website</td>
<td><span>June 30, 2020</span></td>
</tr>
<tr>
<td>Last date of accepting representation of any discrepancy in Question paper &amp; Answer key</td>
<td><span>July 03, 2020</span></td>
</tr>
<tr>
<td>Declaration of BET 2020 Result</td>
<td><span>July 20, 2020</span></td>
</tr>
</tbody>
</table>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38462/egad-ultra-fast-functional-analysis-of-gene-networks</guid>
	<pubDate>Fri, 14 Dec 2018 04:10:35 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38462/egad-ultra-fast-functional-analysis-of-gene-networks</link>
	<title><![CDATA[EGAD: Ultra-fast functional analysis of gene networks]]></title>
	<description><![CDATA[<p><span>With the EGAD (Extending &lsquo;Guilt-by-Association&rsquo; by Degree) package, we present a series of highly efficient tools to calculate functional properties in networks based on the guilt-by-association principle. These allow rapid controlled comparisons and analyses. Two of the core features are: a function prediction algorithm which is fully vectorized (neighbor_voting), allowing network characterization across even thousands of functional groups to be accomplished in minutes in cross-validation and an analytic determination of the optimal prior to guess candidates genes across multiple functional sets (calculate_multifunc, auc_multifunc).</span></p><p>Address of the bookmark: <a href="https://github.com/sarbal/EGAD" rel="nofollow">https://github.com/sarbal/EGAD</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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