Selle kursuse eesmärk on pakkuda põhiteadmisi Linuxi ja terminalikeskkonna kohta. Õpetame teile, kuidas juurde pääseda failidele ja kaustadele, liikuda nende vahel ja loodetavasti vabaneda hirmust kuskil tee peal kinni jääda. Eelnevaid teadmisi pole vaja.
Experiments designed to quantify gene expression often yield hundreds of genes that show statistically significant differences between groups of interest. Once differentially expressed genes are identified, enrichment analysis (EA) methods can be used to explore the biological functions associated with these genes. EA methods allow us to identify groups of genes (e.g. particular pathways) that are over-represented, thereby offering insights into biological mechanisms. One of the EA methods frequently used for high-throughput gene expression data analysis is Gene Set Enrichment Analysis (GSEA). This course will cover GSEA and alternative enrichment methods. Because the implementation of GSEA is directly linked to databases that annotate the function of genes in a cell, the course will also give an overview of functional annotation databases such as Gene Ontology.
The detection of genetic variation is of major interest in various disciplines spanning from ecology and evolution research to inherited disease discovery and precision oncology. Next generation sequencing (NGS) methods are very powerful for the detection of genomic variants. Thanks to its throughput and cost-efficiency it enables the detection of a large number of variants in a large number of samples. In this two-day course we will cover the steps from read alignment to variant calling and annotation. We will mainly focus on the detection of germline mutations by following the GATK best practices.
The 9th edition of the RNA-seq Data Analysis course will be held on 8-12 April – 2024 in Breda, The Netherlands. This course covers the basic concepts and methods required for RNA-seq analysis. Particular attention is given to the data analysis pipelines for differential transcript expression and variant calling. The course consists of a mixture of lectures and Galaxy, Linux and R practicals. Also the potential of long-read based RNA-seq and AI based analysis enrichments will be explored.
Single-cell RNA sequencing (scRNAseq) allows researchers to study gene expression at the single cell level. For example, scRNAseq can help to identify expression patterns that differ between conditions within a cell-type. To generate and analyze scRNAseq data, several methods are available, all with their strengths and weaknesses depending on the researchers’ needs. This 3-day course will cover the main technologies as well as the main aspects to consider while designing a scRNAseq experiment. In addition, it will cover the theoretical background of analysis methods with hands-on practical data analysis sessions applied to droplet-based methods.
European Bioinformatics Institute United Kingdom in association with Elixir Estonia are organising summer school in bioinformatics. This course provides an introduction to the use of bioinformatics in biological research, giving you guidance for using bioinformatics in your work whilst also providing hands-on training in tools and resources appropriate to your research.
Applicants are expected to be at an early stage of using bioinformatics in their research with the need to develop their knowledge and skills further. No previous knowledge of programming is required for this course.
The “Python for Beginners” course introduces Python programming, designed for those with little to no prior experience. Upon completion, students will have a firm grasp of Python fundamentals and be equipped with the skills to start their journey in Python programming.