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  • RNA-Seq Data Analysis Workshop

    21 - 24 September 2020

    Leipzig, Germany

    RNA-Seq Data Analysis Workshop https://tess.elixir-europe.org/events/rna-seq-data-analysis-workshop-28ebaa59-8138-4d5b-887f-289b6a27f990 Advance your research. Understand RNA-Seq analyses challenges and solve them yourself. In a nutshell - Learn the essential computing skills for NGS bioinformatics - Understand NGS analysis algorithms (e.g. read alignment) and data formats - Use bioinformatics tools for handling RNA-Seq data - Create diagnostic graphics and statistics - Compare different approaches for differential expression analysis The purpose of this workshop is to get a deeper understanding in Next-Generation Sequencing (NGS) with a special focus on bioinformatics issues. Advantages and disadvantages of current sequencing technologies and their implications on data analysis will be discovered. You will be trained on understanding NGS data formats and handling potential problems/errors therein. In the course we will use a real-life RNA-seq dataset from the current market leader illumina. All workshop attendees will be enabled to perform important first tasks of NGS data analysis themselves. The course layout has been adapted to the needs of beginners in the field of NGS bioinformatics and allows scientists with no or little background in computer science to get a first hands-on experience in this new and fast evolving research topic. 2020-09-21 09:00:00 UTC 2020-09-24 17:00:00 UTC ecseq Bioinformatics GmbH iad Pc-Pool, Leipzig, Germany iad Pc-Pool Leipzig Leipzig Germany Sequence analysis Sequencing Bioinformatics [] ecSeq Bioinformatics GmbH Sternwartenstr. 29 D-04103 Leipzig Germany Email: events@ecSeq.com [] BiologistsBiologists, Genomicists, Computer ScientistsMolecular BiologistsbioinformaticiansPathologistsPhD studentspostdoc 12 workshops_and_courses first_come_first_served NGS bioinformatics RNA-Seqtranscriptomics
  • Emerging Technologies in Single Cell research

    19 - 20 November 2020

    Leuven, Belgium

    Emerging Technologies in Single Cell research https://tess.elixir-europe.org/events/emerging-technologies-in-single-cell-research Biomedical research is at a turning point. Single cell technologies are opening extraordinary opportunities to address today’s medical challenges. These new tools will enhance our molecular understanding of functional cell states. They will give us major insights into when, how and why diseases of all kinds arise. Working at the cutting edge of this field, VIB has teamed up with LifeTime, a pan-European research initiative that wants to combine single-cell multi-omics, advanced imaging, artificial intelligence/machine learning and personalized disease models to fundamentally change the current medical practice. At the first edition of Emerging Technologies in Single Cell research, a two-day conference, VIB and LifeTime will bring together top researchers in the fields of multi-omics and artificial intelligence. Travel Grants We offer 5 travel grants to international attendees, which will reimburse travel costs up to 400 Euros. Are you interested in attending SingleCell20 and want to have a chance on a travel grant? Submit your motivation below by clicking on the 'Apply for a grant' button before 27 September 2020. The organizing committee will notify you of your travel grant acceptance by the end of June. No registration is needed before submitting a travel grant proposal. When you’re awarded with a travel grant you will receive a practical email containing the next steps. Note that being awarded a travel grant does not mean being registered for the conference. Scroll down to apply for your grant. 2020-11-19 08:00:00 UTC 2020-11-20 18:00:00 UTC VIB KU Leuven Universiteitshal, 22, Naamsestraat, Leuven, Belgium KU Leuven Universiteitshal, 22, Naamsestraat Leuven Vlaams-Brabant Belgium VIBLifeTime VIB Conferences team - +32 9 244 66 11 - Conferences@vib.be [] AcademicsPhD studentsIndustry 300 meetings_and_conferences [] Single cell sequencing Single Cell GenomicsSingle Cell technologiessingle cell rna-seq

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