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Keyword
- RNA-Seq8
- Transcriptomics7
- Expression Atlas6
- Galaxy6
- Bioinformatics4
- RNA-seq4
- bioinformatics4
- Gene Expression3
- Transcriptome assembly3
- RNA-Seq2
- ChIP-seq2
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- single cell RNA-seq analysis2
- R-programming1
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- TtT, Train the Trainer, Educational psychology, cognitive science, teaching practices 1
- VLSCI1
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Scientific topic
- RNA-Seq analysis
- Bioinformatics1099
- Genome annotation302
- Exomes299
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- Proteomics152
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- Data management125
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- Aerobiology98
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- Comparative transcriptomics90
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- DNA-Seq35
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- High throughput sequencing35
- NGS35
- NGS data analysis35
- Next gen sequencing35
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Operation
- Data visualisation3
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- Plotting3
- Rendering3
- Visualisation3
- Analysis2
- Consensus-based sequence alignment2
- Constrained sequence alignment2
- Expression analysis2
- Expression data analysis2
- Gene expression analysis2
- Gene expression data analysis2
- Gene expression regulation analysis2
- Genetic variation analysis2
- Genetic variation annotation2
- Metagenomic inference2
- Microarray data analysis2
- Multiple sequence alignment (constrained)2
- Oligonucleotide alignment2
- Oligonucleotide alignment construction2
- Oligonucleotide alignment generation2
- Oligonucleotide mapping2
- Peak calling2
- Peak-pair calling2
- Protein binding peak detection2
- Protein expression analysis2
- Read alignment2
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- Read pre-processing2
- Sequence alignment2
- Sequence alignment (constrained)2
- Sequence alignment construction2
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- Sequence variation analysis2
- Short oligonucleotide alignment2
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- Short read mapping2
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- Transcript variant analysis2
- Variant analysis2
- Allele calling1
- Biological pathway analysis1
- Biological pathway modelling1
- Biological pathway prediction1
- Differential expression analysis1
- Differential gene analysis1
- Differential gene expression analysis1
- Differential gene expression profiling1
- Differentially expressed gene identification1
- Exome variant detection1
- Functional clustering1
- Functional pathway analysis1
- Functional sequence clustering1
- Genome variant detection1
- Germ line variant calling1
- Mutation detection1
- Pathway analysis1
- Pathway comparison1
- Pathway modelling1
- Pathway prediction1
- Pathway simulation1
- Somatic variant calling1
- Variant calling1
- Variant mapping1
- de novo mutation detection1
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Event type
- Workshops and courses40
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Venue
- European Bioinformatics Institute, Hinxton6
- Craik-Marshall Building2
- Earlham Institute (EI), Colney Lane2
- Via Nizza, 522
- Arts West North Wing Room 355 Professors Walk University of Melbourne Carlton, VIC 30531
- Campus di Fisciano, Università degli Studi di Salerno, Via Giovanni Paolo II, n. 1321
- Campus di Fisciano, Università di Salerno1
- Computer Room (aula Calcolo) Department of Biosciences, University of Milan, Via Celoria 261
- EMBL-EBI, EMBL EBI1
- Feinberg Room B1
- Instituto de Biología Agrícola de Mendoza / Institute of Agricultural biology (IBAM), Almirante Brown 500, Chacras de Coria1
- Lab-14, VLSCI1
- Leuven - Campus Gasthuisberg, Herestraat 491
- Ole-Johan Dahls hus, 23B, Gaustadalléen1
- Southbank campus of Griffith University1
- Station Biologique De Roscoff1
- Station Biologique De Roscoff, Place Georges Teissier1
- University of Leicester - College Court Conference Centre1
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Organizer
- European Bioinformatics Institute (EBI)15
- Australian BioCommons6
- Earlham Institute2
- University of Cambridge2
- <a href="http://biont-training.eu/">BioNT</a> (BIO Network for Training) 1
- ABiMS (http://abims.sb-roscoff.fr/), AVIESAN (http://www.aviesan.fr/en), BiGEst (http://bigest.unistra.fr/), IFB (https://www.france-bioinformatique.fr/), IFB Core (https://www.france-bioinformatique.fr/), MIGALE (https://migale.inrae.fr)1
- Allegra Via, Giulio Pavesi, Federico Zambelli, Matteo Chiara1
- Allegra Via, Pedro Fernandes, Anna Marabotti, Roberto Tagliaferri, Loredana Le Pera1
- Anna Marabotti (University of Salerno and ELIXIR-IIB Training Team, Italy), Roberto Tagliaferri (University of Salerno, Italy), Alessandro Weisz (University of Salerno, Italy), Loredana Le Pera (ELIXIR-IIB Training Team and IIT, Italy), Allegra Via (ELIXIR-IIB Training Coordinator, CNR-IBPM, Italy) 1
- Aviesan ITMO GGB, Inserm, IFB1
- B&Gu, University of Turin, EuroClone 1
- Bioc20201
- Dr. Dena Leshkowitz1
- ELIXIR UiO node1
- Melbourne Bioinformatics 1
- Raffaele Calogero, Marco Beccuti, Loredana Le Pera, Allegra Via 1
- ResBaz1
- University of Leicester1
- VIB1
- VLSCI - University of Melbourne1
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Target audience
- post-docs3
- Cette formation est destinée aux biologistes (ingénieurs, doctorants, chercheurs, enseignants-chercheurs, praticiens…) confrontés à l’analyse de données NGS, et qui ne disposent pas des compétences bioinformatiques suffisantes.2
- Graduate students2
- Institutions and other external Institutions or individuals2
- PhD students2
- Postdocs and Staff members from the University of Cambridge2
- Anyone who wants to become a teacher / trainer or a better one1
- Beginners1
- Early Career Researchers (ECRs)1
- Job seeker1
- Life Science Researchers1
- PhD Students1
- PhD Students or young researchers in molecular biology and/or genetics with little or no background in bioinformatics. 1
- PhD students and young researchers in the life science and computational biology field who are planning to use RNA-seq data and are looking for the best practices to analyze these types of data1
- Small and Medium-sized Enterprises (SMEs)1
- This course is aimed at life science researchers wanting to learn more about processing RNA-Seq data and later downstream analysis. It will help those wanting a basic introduction to handling RNA-Seq data, guiding them through several common approaches that can be applied to their own datasets. It features taught and practical sessions that cover how to interpret gene expression data and learn more about the biological significance of certain results. Participants will require a basic knowledge of the Unix command line, the Ubuntu 18 operating system and the R statistical packages. We recommend these free tutorials: Basic introduction to the Unix environment: www.ee.surrey.ac.uk/Teaching/Unix Introduction and exercises for Linux: https://training.linuxfoundation.org/free-linux-training Basic R concept tutorials: www.r-tutor.com/r-introduction Regardless of your current knowledge we encourage successful participants to use these, and other materials, to prepare for attending the course and future work in this area.1
- This course is aimed at researchers from Masters-level upwards within Latin America who are working with and/or generating their own plant genomic and transcriptomic datasets. Prerequisites: Some basic computational or previous bioinformatics experience is required for this workshop, particularly using the UNIX operating system (basic command line skills) and R. You may find the resources below useful: Basic introduction to the Unix environment: www.ee.surrey.ac.uk/Teaching/Unix Introduction and exercises for Linux: https://training.linuxfoundation.org/free-linux-training Basic R concept tutorials: www.r-tutor.com/r-introduction Important: All participants must bring a laptop for the course. We will use a virtual machine (VM) provided by instructors for the course practical sessions. All laptops must be of 64-bit architecture with any Operating System and have at least 60 GB free space. Please also note: this course will be taught in Spanish, however the trainers are fluent in English and can offer language support where feasible. A number of travel fellowships are available for this course - early-stage researchers and researchers from underrepresented groups are especially encouraged to apply for CABANA travel fellowships. You can apply for travel fellowships via the course application form.1
- This course is aimed at researchers who are generating, planning on generating, or working with single cell RNA sequencing data. Prerequisites Participants will be using a Galaxy resource in-depth. Participants may also be asked to do brief coding in R. Please ensure that you complete the free tutorials before you attend the course: Introduction to Galaxy: https://galaxyproject.org/tutorials/g101/ Basic R concept tutorials: www.r-tutor.com/r-introduction There are other tutorials here, although they are not required: https://galaxyproject.org/learn/1
- This course is aimed at researchers who are generating, planning on generating, or working with single cell RNA sequencing data. Prerequisites Participants will be using a Galaxy resource in-depth. Participants may also be asked to do brief coding in R. Please ensure that you complete the free tutorials before you attend the course: Introduction to Galaxy: https://galaxyproject.org/tutorials/g101/ Basic R concept tutorials: www.r-tutor.com/r-introduction There are other tutorials here, although they are not required: https://galaxyproject.org/learn/1
- This course is aimed at researchers who are generating, planning on generating, or working with single cell RNA sequencing or image-based transcriptomics data. This course will not cover any aspects of data analysis, therefore no prior computational knowledge is required.1
- This course is aimed at researchers with little to no experience in big data analysis and who are generating, planning on generating, or working with single cell RNA sequencing data.1
- This course is aimed at researchers with little-to-no experience in big data analysis and who are generating, planning on generating, or working with single-cell RNA sequencing data.1
- Wet-lab Researchers1
- bioinformaticians1
- biologists1
- teachers / trainers1
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