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	<link>https://bioinformaticsonline.com/related/4003?offset=480</link>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/13338/protein-function-annotation-and-machine-learning-upmc-paris-france</guid>
  <pubDate>Sat, 02 Aug 2014 01:22:52 -0500</pubDate>
  <link></link>
  <title><![CDATA[Protein function annotation and machine learning - UPMC - Paris, France]]></title>
  <description><![CDATA[
<p>Protein function annotation and machine learning - UPMC - Paris, France</p>

<p>Job Description: 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>Title: A novel integrative platform for large scale protein annotation that exploits a multitude of diversified probabilistic models in several protein signature databases.</p>

<p>We propose a novel integrated approach for large scale protein annotation that will exploit an unprecedented amount of genomic data as well as sophisticated machine learning techniques and combinatorial optimization approaches taking advantages of High Performance Computing (HPC) environments. The idea is to uncover as much as possible the evolutionary processes of protein sequences that took place throughout the whole tree of life and that affected the evolution of a protein family. We have already demonstrated in a previous work that the problem of functional annotation is inherent to the ability of uncovering such paths. Now, we shall extend this approach to large scale genome annotation by considering 11 different protein databases, constituted by about 10^9 protein sequences, and by producing a large pool of diversified probabilistic models coding for about 10^7 evolutionary protein pathways. Such models will be used to search for specific domains in genomes to be annotated. Our previous methodology needs to be fundamentally improved to deal with this large amount of biological data. In this project, we shall work on the algorithms to reduce the space of models and the search complexity, and we shall implement some important algorithmic changes towards the realization of a powerful integrated annotation tool.</p>

<p>Where: 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>Start date: September 1st, 2014<br />Contact Person: Alessandra Carbone<br />Contact: alessandra.carbone@lip6.fr</p>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36267/pspairwise-sequentially-markovian-coalescent-psmc-model</guid>
	<pubDate>Thu, 19 Apr 2018 05:29:23 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36267/pspairwise-sequentially-markovian-coalescent-psmc-model</link>
	<title><![CDATA[PSPairwise Sequentially Markovian Coalescent (PSMC) model]]></title>
	<description><![CDATA[<p><span>Implementation of the Pairwise Sequentially Markovian Coalescent (PSMC) model</span></p><p>Address of the bookmark: <a href="https://github.com/lh3/psmc" rel="nofollow">https://github.com/lh3/psmc</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/13842/swabs-to-genomes-a-comprehensive-workflow</guid>
	<pubDate>Sun, 10 Aug 2014 03:01:21 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/13842/swabs-to-genomes-a-comprehensive-workflow</link>
	<title><![CDATA[Swabs to Genomes: A Comprehensive Workflow]]></title>
	<description><![CDATA[<p>The sequencing, assembly, and basic analysis of microbial genomes, once a painstaking and expensive undertaking, has become almost trivial for research labs with access to standard molecular biology and computational tools. However, there are a wide variety of options available for DNA library preparation and sequencing, and inexperience with bioinformatics can pose a significant barrier to entry for many who may be interested in microbial genomics. The objective of the present study was to design, test, troubleshoot, and publish a simple, comprehensive workflow from the collection of an environmental sample (a swab) to a published microbial genome; empowering even a lab or classroom with limited resources and bioinformatics experience to perform it.</p><p>Address of the bookmark: <a href="https://peerj.com/preprints/453.pdf" rel="nofollow">https://peerj.com/preprints/453.pdf</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/38649/ngs-platforms-launched-by-bgi%E2%80%99s-mgi-tech</guid>
	<pubDate>Thu, 10 Jan 2019 04:42:06 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/38649/ngs-platforms-launched-by-bgi%E2%80%99s-mgi-tech</link>
	<title><![CDATA[NGS Platforms launched by BGI’s MGI Tech]]></title>
	<description><![CDATA[<p>MGI Tech Co., Ltd. (MGI), a subsidiary of BGI Group, is committed to enabling effective and affordable healthcare solutions for all. Based on its proprietary technology, MGI produces sequencing devices, equipment, consumables and reagents to support life science research, medicine and healthcare. MGI's multi-omics platforms include genetic sequencing, mass spectrometry and medical imaging. Providing real-time, comprehensive, life-long solutions, its mission&nbsp;is to&nbsp;develop and promote advanced life science tools for future healthcare.</p><p>MGI, a subsidiary of global genomics leader BGI Group, announced pricing and its first early access customer for the new ultra high-throughput sequencer, MGISEQ-T7, saying it has driven down sequencing cost to&nbsp;$5&nbsp;per gigabyte, with exceptionally high accuracy. Such innovations are helping more people to realize the benefits of genomic information.</p><p>In October, MGI launched the MGISEQ-T7, a highly flexible production-scale platform that is the most powerful sequencer to date. It can produce as many as 60 whole human genomes in one day. The instrument sells for&nbsp;$1 million.</p><p>The T7 enables simultaneous but independent operation of up to four flow cells, which means different applications such as single-cell RNA sequencing, whole exome sequencing and whole genome sequencing can be run in different flow cells at the same time. This helps to reduce costs, allowing MGI to offer the most competitive sequencing price in the market.</p><p><span>Powered by DNBseq&trade;, MGISEQ delivers quality data with accuracy for SNP and Indel calling rate of 99.9% and 99%, respectively, along with decreased duplication rate down to less than 2 percent, and almost zero Index mis-assignment rate.</span></p><p><span><span>SOURCE MGI</span></span></p><p>https://www.bgi.com/global/company/news/bgis-mgi-tech-launches-two-new-ngs-platforms/</p><p>http://en.mgitech.cn/</p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40834/nucleus-python-and-c-code-for-reading-and-writing-genomics-data</guid>
	<pubDate>Sun, 02 Feb 2020 08:14:19 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40834/nucleus-python-and-c-code-for-reading-and-writing-genomics-data</link>
	<title><![CDATA[Nucleus: Python and C++ code for reading and writing genomics data.]]></title>
	<description><![CDATA[<p>Nucleus is a library of Python and C++ code designed to make it easy to read, write and analyze data in common genomics file formats like SAM and VCF. In addition, Nucleus enables painless integration with the TensorFlow machine learning framework, as anywhere a genomics file is consumed or produced, a TensorFlow tfrecords file may be used instead.</p><p>Address of the bookmark: <a href="https://github.com/google/nucleus" rel="nofollow">https://github.com/google/nucleus</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/14272/lecturersenior-lecturer-level-bc-in-bioinformatics</guid>
  <pubDate>Fri, 22 Aug 2014 12:45:52 -0500</pubDate>
  <link></link>
  <title><![CDATA[Lecturer/Senior Lecturer (Level B/C) in Bioinformatics]]></title>
  <description><![CDATA[
<p>Lecturer/Senior Lecturer (Level B/C) in Synthetic Biology, Research Fellow (Level B) in Synthetic Biology &amp; Lecturer/Senior Lecturer (Level B/C) in Bioinformatics</p>

<p>Apply now Job no: 494553<br />Work type: Continuing full time<br />Vacancy type: External Vacancy, Internal Vacancy<br />Categories: Academic - Teaching and Research</p>

<p>The Faculty of Science is launching a new and innovative branch of biological science at Macquarie University – Synthetic Biology. Synthetic biology combines engineering principles with molecular biological approaches to design and construct biological devices and systems. Recent highlights in this field include the design and synthesis of a functional bacterial genome and a yeast chromosome, and generation of synthetic bacterial cells. The rational synthesis of "designer" organisms yield important insights into how organisms work and has the potential to revolutionise biotechnological applications in areas such as bioenergy and biomanufacturing.</p>

<p>Find more at http://jobs.mq.edu.au/cw/en/job/494553/lecturersenior-lecturer-level-bc-in-synthetic-biology-research-fellow-level-b-in-synthetic-biology-lecturersenior-lecturer-level-bc-in-bioinformatics</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42143/sibelia-a-comparative-genomics-tool</guid>
	<pubDate>Sat, 22 Aug 2020 02:49:00 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42143/sibelia-a-comparative-genomics-tool</link>
	<title><![CDATA[Sibelia: A comparative genomics tool]]></title>
	<description><![CDATA[<p><strong>Sibelia</strong>: A comparative genomics tool: It assists biologists in analysing the genomic variations that correlate with pathogens, or the genomic changes that help microorganisms adapt in different environments. Sibelia will also be helpful for the evolutionary and genome rearrangement studies for multiple strains of microorganisms.&nbsp;</p>
<p><strong>Sibelia</strong>&nbsp;is useful in finding: (1) shared regions, (2) regions that present in one group of genomes but not in others, (3) rearrangements that transform one genome to other genomes.</p>
<p>More at&nbsp;<a href="http://bioinf.spbau.ru/sibelia">http://bioinf.spbau.ru/sibelia</a></p>
<p>Sibelia docs&nbsp;<a href="http://gensoft.pasteur.fr/docs/Sibelia/3.0.7/SIBELIA.md">http://gensoft.pasteur.fr/docs/Sibelia/3.0.7/SIBELIA.md</a></p><p>Address of the bookmark: <a href="https://github.com/bioinf/Sibelia" rel="nofollow">https://github.com/bioinf/Sibelia</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/14758/phd-opportunity-at-universite-de-liege-belgium</guid>
  <pubDate>Mon, 01 Sep 2014 17:16:22 -0500</pubDate>
  <link></link>
  <title><![CDATA[PhD opportunity at Université de Liège - Belgium]]></title>
  <description><![CDATA[
<p>The Bioinformatics and Systems Biology Unit of Université de Liège (Belgium) is looking for a highly motivated master student with programming skills for a PhD thesis project (4 years, fully funded) with the goal of designing computational tools that use literature, genomic and structural data in order to infer regulatory and metabolic networks.  </p>

<p>Applicants are invited to send their resume and a recommendation letter to Prof. Patrick Meyer (more details at   www.biosys.ulg.ac.be )</p>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/43227/project-associate-i-project-associate-ii-senior-project-associate-igib</guid>
  <pubDate>Thu, 05 Aug 2021 16:11:32 -0500</pubDate>
  <link></link>
  <title><![CDATA[Project Associate-I | Project Associate-II | Senior Project Associate @ IGIB]]></title>
  <description><![CDATA[
<p>Experience in Next Generation Sequencing (NGS) application and interest in Genomics/ Clinical / Translational Applications. OR Good computational programming skills and deep interest in working on interface of Genomics and Clinical application. </p>

<p>Project Scientist-I <br />Experimental / Computation analysis experience in highthroughput genomics/ clinical application.</p>

<p>Project Manager <br />Experience in handling large biological projects involving high-throughput genomics/ clinical application.</p>

<p>Scientific Administrative Assistant <br />Lab Work. </p>

<p>More at https://vinodscaria.genomes.in/positionsopen</p>
]]></description>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/14905/internship-in-computational-biology</guid>
  <pubDate>Thu, 04 Sep 2014 04:19:40 -0500</pubDate>
  <link></link>
  <title><![CDATA[Internship in Computational Biology]]></title>
  <description><![CDATA[
<p>We are looking for a motivated and autonomous intern to study gene expression in hybrid organisms. The student will work on natural hybrids of two or three different species of fungal endosymbionts of grasses. The pupose of this project is to build software allowing us to identify the genomic origin of expressed genes. To do that, the intern will have to analyze expression data (from RNA-seq) to find SNPs on the sequenced mRNAs allowing to identify from which of the parental genome the expressed gene come from. The data will have to be saved in a database using the standard BioSQL schema.</p>

<p>This job will allow the intern to become more familiar with new biological and bioinformatics tools like next generation sequencing, RNA-Seq data analysis and comparative genomics.</p>

<p>To apply for this position, send the following documents (in PDF format) to Dr Pierre-Yves Dupont (email p.y.dupont@massey.ac.nz):</p>

<p>1. A short cover letter.<br />2. A curriculum vitae, with transcript details.<br />3. The names and contact details of two referees willing to provide a confidential letter of recommendation upon request.</p>

<p>Informal enquiries are welcome. Formal applications are due by Sunday 2nd December 2012.<br />Requirements: </p>

<p>This position requires a good understanding of genetic problems, a good command of at least one scripting language (Perl, Python...), a basic knowledge of MySQL or any relational database management system. Knowledge in biological programming libraries (BioPython, BioPerl, BioRuby...), Java, C++ or any compiled language is an asset but not required. Undergraduate or Master degree is required.<br />Contact Information: </p>

<p>Dr. Pierre-Yves Dupont<br />Institute of Molecular BioSciences<br />Massey University<br />Private Bag 11 222<br />Palmerston North 4442<br />NEW ZEALAND</p>

<p>http://massey.genomicus.com/<br />p.y.dupont@massey.ac.nz</p>

<p>Information about the Institute of Molecular BioSciences (http://imbs.massey.ac.nz/) and the Computational Biology Research Group (http://massey.genomicus.com/) is available online. For more information about the position, you can contact Dr Pierre-Yves Dupont (email p.y.dupont@massey.ac.nz).</p>
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