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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/19820?offset=970</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40583/trelliscope-flexibly-visualize-large-complex-data-in-great-detail-from-within-the-r-statistical-programming-environment</guid>
	<pubDate>Tue, 21 Jan 2020 04:22:49 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40583/trelliscope-flexibly-visualize-large-complex-data-in-great-detail-from-within-the-r-statistical-programming-environment</link>
	<title><![CDATA[Trelliscope: flexibly visualize large, complex data in great detail from within the R statistical programming environment.]]></title>
	<description><![CDATA[<p>Trelliscope provides a way to flexibly visualize large, complex data in great detail from within the R statistical programming environment. Trelliscope is a component in the<span>&nbsp;</span><a href="http://deltarho.org/docs-trelliscope/deltarho.org">DeltaRho</a><span>&nbsp;</span>environment.</p>
<p>For those familiar with<span>&nbsp;</span><a href="http://cm.bell-labs.com/cm/ms/departments/sia/project/trellis/">Trellis Display</a>,<span>&nbsp;</span><a href="http://docs.ggplot2.org/0.9.3.1/facet_wrap.html">faceting in ggplot</a>, or the notion of<span>&nbsp;</span><a href="http://en.wikipedia.org/wiki/Small_multiple">small multiples</a>, Trelliscope provides a scalable way to break a set of data into pieces, apply a plot method to each piece, and then arrange those plots in a grid and interactively sort, filter, and query panels of the display based on metrics of interest. With Trelliscope, we are able to create multipanel displays on data with a very large number of subsets and view them in an interactive and meaningful way.</p><p>Address of the bookmark: <a href="http://deltarho.org/docs-trelliscope/#introduction" rel="nofollow">http://deltarho.org/docs-trelliscope/#introduction</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/41043/postdoctoral-scientist-genome-analytics-genome-bioinformatics-mf</guid>
  <pubDate>Sun, 16 Feb 2020 02:57:40 -0600</pubDate>
  <link></link>
  <title><![CDATA[Postdoctoral scientist genome analytics/ genome bioinformatics (m/f/*)]]></title>
  <description><![CDATA[
<p>https://www.uksh.de/jobs/Stellenangebote-nr-20190570-p-8.html<br />Your profile:<br />Degree in bioinformatics, biostatistics, or equivalent<br />Experience in the processing and analysis of large-scale genomics data using compute clusters / high-performance computing<br />Strong competence in working in Unix/Linux environments (shell)<br />Strong programming skills (in particular: Python, R, Perl)<br />Experience with using git and snakemake<br />Fluent English language skills, both spoken and written<br />Strong communication skills and motivation to work in a young, interdisciplinary, dynamic team</p>

<p>Additional Information:</p>

<p>If you have any questions about scientific aspects of this position, please contact Prof. Lars Bertram, head of LIGA (lars.bertram@uni-luebeck.de).</p>

<p>Please contact Ms. Anna Wolbert for further questions about administrative details (recruiting@uksh.de).</p>

<p>Weitere Informationen erhalten Sie auch unter www.uksh.de/karriere.</p>

<p>Wir freuen uns auf Ihre Bewerbung bis zum 15.03.2020 unter Angabe unserer Ausschreibungsnummer 20190570.119.CL.</p>
]]></description>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/41496/new-machine-learning-packages-in-r</guid>
	<pubDate>Fri, 27 Mar 2020 12:11:21 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/41496/new-machine-learning-packages-in-r</link>
	<title><![CDATA[New Machine Learning Packages in R]]></title>
	<description><![CDATA[<h3 id="machine-learning">Machine Learning</h3><p><a href="https://cran.r-project.org/package=autokeras">autokeras</a>&nbsp;v1.0.1: Implements an interface to&nbsp;<a href="https://autokeras.com/">AutoKeras</a>, an open source software library for automated machine learning. See&nbsp;<a href="https://cran.r-project.org/web/packages/autokeras/readme/README.html">README</a>&nbsp;for an example.</p><p><a href="https://cran.r-project.org/package=MTPS">MTPS</a>&nbsp;v0.1.9: Implements functions to predict simultaneous multiple outcomes based on revised stacking algorithms as described in&nbsp;<a href="denied:doi:10.1093/bioinformatics/btz531">Xing et al. (2019)</a>. See the&nbsp;<a href="https://cran.r-project.org/web/packages/MTPS/vignettes/Guide.html">vignette</a>&nbsp;to get started.</p><p><a href="https://cran.r-project.org/package=quanteda.textmodels">quanteda.textmodels</a>&nbsp;v0.9.1: Implements methods for scaling models and classifiers based on sparse matrix objects representing textual data. It includes implementations of the&nbsp;<a href="denied:doi:10.1017/S0003055403000698">Laver et al. (2003)</a>&nbsp;wordscores model, the&nbsp;<a href="denied:arxiv:1710.08963">Perry &amp; Benoit&rsquo;s (2017)</a>&nbsp;class affinity scaling model, and the&nbsp;<a href="denied:doi:10.1111/j.1540-5907.2008.00338.x">Slapin &amp; Proksch (2008)</a>&nbsp;wordfish model. See the&nbsp;<a href="https://cran.r-project.org/web/packages/quanteda.textmodels/vignettes/textmodel_performance.html">vignette</a>&nbsp;to get started.</p><p><a href="https://cran.r-project.org/package=SeqDetect">SeqDetect</a>&nbsp;v1.0.7: Implements the automaton model found in&nbsp;<a href="https://ieeexplore.ieee.org/document/8910574">Krleža, Vrdoljak &amp; Brčić (2019)</a>&nbsp;to detect and process sequences. See the&nbsp;<a href="https://cran.r-project.org/web/packages/SeqDetect/vignettes/SequentialDetector.pdf">vignette</a>&nbsp;for examples and theory.</p><p><a href="https://cran.r-project.org/package=studyStrap">studyStrap</a>&nbsp;v1.0.0: Implements multi-Study Learning algorithms such as Merging, Study-Specific Ensembling (Trained-on-Observed-Studies Ensemble), the Study Strap, and the Covariate-Matched Study Strap. and offers over 20 similarity measures. See&nbsp;<a href="denied:doi:10.1101/856385">Kishida, et al. (2019)</a>&nbsp;for background and the&nbsp;<a href="https://cran.r-project.org/web/packages/studyStrap/vignettes/vignette.html">vignette</a>&nbsp;for how to use the package.</p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43254/quasr-quantification-and-annotation-of-short-reads-in-r</guid>
	<pubDate>Fri, 13 Aug 2021 07:44:05 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43254/quasr-quantification-and-annotation-of-short-reads-in-r</link>
	<title><![CDATA[QuasR: Quantification and annotation of short reads in R]]></title>
	<description><![CDATA[<p>The <em><a href="https://bioconductor.org/packages/3.14/QuasR">QuasR</a></em> package (short for <em>Qu</em>antify and <em>a</em>nnotate <em>s</em>hort reads in <em>R</em>) integrates the functionality of several <strong>R</strong> packages (such as <em><a href="https://bioconductor.org/packages/3.14/IRanges">IRanges</a></em> <span>(Lawrence et al. 2013)</span> and <em><a href="https://bioconductor.org/packages/3.14/Rsamtools">Rsamtools</a></em>) and external software (e.g.&nbsp;<code>bowtie</code>, through the <em><a href="https://bioconductor.org/packages/3.14/Rbowtie">Rbowtie</a></em> package, and <code>HISAT2</code>, through the <em><a href="https://bioconductor.org/packages/3.14/Rhisat2">Rhisat2</a></em> package). The package aims to cover the whole analysis workflow of typical high throughput sequencing experiments, starting from the raw sequence reads, over pre-processing and alignment, up to quantification. A single <strong>R</strong> script can contain all steps of a complete analysis, making it simple to document, reproduce or share the workflow containing all relevant details.</p><p>Address of the bookmark: <a href="https://www.bioconductor.org/packages/devel/bioc/vignettes/QuasR/inst/doc/QuasR.html" rel="nofollow">https://www.bioconductor.org/packages/devel/bioc/vignettes/QuasR/inst/doc/QuasR.html</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44362/biostats-book</guid>
	<pubDate>Mon, 14 Aug 2023 03:11:39 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44362/biostats-book</link>
	<title><![CDATA[Biostats book !]]></title>
	<description><![CDATA[<p>https://practical-stats-med-r.netlify.app/</p><p>Address of the bookmark: <a href="https://practical-stats-med-r.netlify.app/" rel="nofollow">https://practical-stats-med-r.netlify.app/</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44663/svbyeye-r-package-to-visualize-alignments-between-two-or-multiple-dna-sequences</guid>
	<pubDate>Tue, 17 Sep 2024 02:34:57 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44663/svbyeye-r-package-to-visualize-alignments-between-two-or-multiple-dna-sequences</link>
	<title><![CDATA[SVbyEye: R Package to visualize alignments between two or multiple DNA sequences]]></title>
	<description><![CDATA[<p dir="auto">R Package to visualize alignments between two or multiple DNA sequences including<br>a number of functionalities to facilitate processing of alignments in PAF format.</p>
<p dir="auto"><span>SVbyEye, an open-source R package to visualize and annotate sequence-to-sequence alignments along with various functionalities to process alignments in PAF format. The tool facilitates the characterization of complex SVs in the context of sequence homology helping resolve the mechanisms underlying their formation. Availability and implementation SVbyEye is available at https://github.com/daewoooo/SVbyEye.</span></p>
<p dir="auto">Author: David Porubsky</p><p>Address of the bookmark: <a href="https://github.com/daewoooo/SVbyEye" rel="nofollow">https://github.com/daewoooo/SVbyEye</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/17504/postdoc-scientist-bioinformatics-at-ccmb</guid>
  <pubDate>Fri, 26 Sep 2014 19:58:41 -0500</pubDate>
  <link></link>
  <title><![CDATA[PostDoc Scientist Bioinformatics at CCMB]]></title>
  <description><![CDATA[
<p>1. Project Assistant/Junior Research Fellow/ Project Fellow [PA_JRF_PF]</p>

<p>a) M.Sc/or equivalent in biological sciences/related areas [Position Code: PA_JRF_PF_a]<br />b) B.E/B.Tech/ M.Sc in biotechnology/bioinformatics/computer science/Chemistry/Physics or MCA [Position Code: PA_JRF_PF_b]<br />c) M.Sc/or equivalent in wildlife sciences/ecology/environmental sciences or MBBS/BVSc/MVSc. [Position Code: PA_JRF_PF_c]</p>

<p>(Candidates with result awaited are NOT eligible to apply)</p>

<p>Upper Age limit 28years</p>

<p>Rs.12000 / Rs.16000 (as sanctioned by the funding agency)</p>

<p>2. Post Doctoral Fellow/Research Associate in multiple research areas [PDF_RA]</p>

<p>Ph.D. (submitted/awarded) in any branch of biological Sciences. Candidates with Ph.D. in other sciences are also encouraged to apply.</p>

<p>Experience in molecular biology, biochemistry, structural biology, cell biology, infectious disease, conservation genetics, veterinary science, reproductive biology, and molecular diagnostics is desired but not mandatory.</p>

<p>[Position Code: PDF_RA]</p>

<p>UpperAge limit 35years</p>

<p>Rs. 22000- 26000 (as sanctioned by the funding agency)</p>

<p>3. Post Doctoral Scientist Fellow [PDSF]</p>

<p>Ph.D in any of the following areas: bioinformatics, next generation sequencing, high throughput data analysis, proteomics, bio-statistics, computer science, information technology, computer hardware and networking/clustering, parallel processing.<br />[Position Code: PDSF]</p>

<p>Upper Age limit 40 years</p>

<p>Rs. 40000 consolidated (as sanctioned by the funding agency)</p>

<p>Download Application: Last date for apply online: 09th Oct 2014</p>

<p>Advertisement: www.ccmb.res.in//index.php?view=notifications&amp;mid=0&amp;id=71&amp;nid=38</p>

<p>Apply online http://www.ccmb.res.in/positions/temp_notif/online_form.html</p>

<p>More at http://www.ccmb.res.in//index.php?view=notifications&amp;mid=0&amp;id=71&amp;nid=38</p>
]]></description>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/17652/arraygen-bioinformatics-genomics-group</guid>
  <pubDate>Sun, 28 Sep 2014 14:09:55 -0500</pubDate>
  <link></link>
  <title><![CDATA[ArrayGen Bioinformatics Genomics Group]]></title>
  <description><![CDATA[
<p>ArrayGen is a global bioinformatics company which is a one stop solution for microarray designing and genomics data analysis. Our novel Array Design Approach Strategy (ADAS) aims to condense the time lag between demands of scientific community and manufacture industry, thereby expediting research processes.</p>

<p>ArrayGen specializes in Genomics data analysis and research, as we believe in the level of precision, predictability, benchmark-ability, and data analysis capability of genomics data over other forms of biological data. ArrayGen constantly strives to develop new solutions, and plug the existing gaps in the technological advancement of the field.</p>

<p>More http://www.arraygen.com/</p>
]]></description>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/17873/postdoc-position-in-protein-annotation-and-machine-learning-paris-france</guid>
  <pubDate>Sat, 04 Oct 2014 08:10:45 -0500</pubDate>
  <link></link>
  <title><![CDATA[Postdoc position in protein annotation and machine learning - Paris, France]]></title>
  <description><![CDATA[
<p>We are interested in finding an excellent postdoc with interests in protein functional annotation, machine learning and computer grids. The position is open for 3.5 years at the Université Pierre et Marie Curie, in the heart of Paris.</p>

<p>Research topic: Protein function annotation, multiple probabilistic models, domain architecture, machine learning, combinatorial optimization, computer grid.</p>

<p>This project is run on the Laboratoire de Biologie Computationnelle et Quantitative UMR7238 CNRS-UPMC – Analytical Genomics team, headed by A.Carbone. It is co-advised with Pierre-Henri Wuillemin, Laboratoire d’Informatique de Paris 6 – Equipe DECISION.</p>

<p>The postdoc will be payed under a contract of Ingénieur de Recherche lasting 3.5 years and it is available from September 1st, 2014.</p>

<p>Group Web Page: http://www.lcqb.upmc.fr/AnalGenom/home.html</p>

<p>Ref. E-Mail: Alessandra Carbone alessandra.carbone@lip6.fr</p>
]]></description>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/18819/jrfsrf-at-jawaharlal-nehru-institute-ofadvanced-studies-jnias-hyderabad</guid>
  <pubDate>Fri, 31 Oct 2014 08:48:23 -0500</pubDate>
  <link></link>
  <title><![CDATA[JRF/SRF at Jawaharlal Nehru Institute ofAdvanced Studies (JNIAS), Hyderabad]]></title>
  <description><![CDATA[
<p>Applications for Academic Projects in Biotechnology, Bioinformatics, Environmental Sciences and Computer Science &amp; Engineering</p>

<p>About JNIAS<br />Jawaharlal Nehru Institute of Advanced Studies (JNIAS), Hyderabad has been established by Dr. D. Swaminadhan Research Foundation (DSRF), Hyderabad as a Research and Educational Institution with a view to contribute in developing advanced technologies and build „core competence‟ in specific areas. The activities of JNIAS involves: Education, Research Training and Innovations in the fields of Sciences, Technologies, Humanities and Social Sciences. It aims to blossom into an Advanced Institute of education and research with a reservoir of expertise and experience in the relevant fields and the necessary capability to harness multi-disciplinary research and studies. JNIAS has been recognized as an Advanced Research Institute by Jawaharlal Nehru Technological University Hyderabad (JNTUH), Hyderabad and Jawaharlal Nehru Technological University Anantapur (JNTUA), for offering Ph.D., P.G M.Phil, P.G Diploma and Training Programmes in Sciences and Engineering &amp; Technology.</p>

<p>Jawaharlal Nehru Architecture and Fine Arts University (JNAFAU) Hyderabad also recognized JNIAS for offering UG, PG degree in Architecture.</p>

<p>Projects &amp; Facilities</p>

<p>JNIAS offers wide range of projects:</p>

<p>Biotechnology area:</p>

<p>Molecular Biology<br />Microbiology<br />Nanotechnology<br />Bioinformatics (Schrodinger Software)<br />In Silico studies &amp; Drug Designing<br />Sequence analysis<br />Protein structure function studies</p>

<p>Registration<br />Tuition Fees: Interested students need to pay the following tuition fees:<br />1. Six Month’s Project: Rs. 20,000/-<br />2. Four Month’s Project: Rs. 15,000/-<br />3. Three Month’s Project: Rs. 10,000/-<br />4. One Month - Hands on Training : Rs. 8,000/-</p>

<p>For enquires call:<br />91-7893203414 (Biotechnology), 91-9949582263 (Environmental Sciences) 91-8977369305 (Computer Science)</p>

<p>Interested student may download the application from the website (www.jnias.in) and send the hard copy of the completed application forms and Curriculum Vitae along with the Demand Draft drawn on any nationalized Banks in favor of “The Registrar, JNIAS, Secunderabad”. Application forms can be sent through email to academicprojects@jnias.in</p>

<p>Address<br />Jawaharlal Nehru Institute of Advanced Studies (JNIAS)<br />6th Floor, Buddha Bhavan, M.G Road,<br />Secunderabad - 500 003<br />Andhra Pradesh, India<br />Tele/Fax: 040- 27541551; 27541553<br />Mobile: 08885541554<br />Web site: www.jnias.in</p>

<p>Brochure : https://drive.google.com/file/d/0B3zPwhgA-u-nU0dyMFd2OWcxNUpSTWNYc0xDSGs5UDI4UDNB/view?usp=sharing</p>
]]></description>
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