Andmehaldus on oluline osa teadustööst ja on hädavajalik andmetest maksimumi saamiseks.
Andmehaldus 101 on sissejuhatav kursus, mis varustab teadlasi vajalike oskustega uurimisandmete tõhusaks organiseerimiseks ja haldamiseks. Osalejad saavad teadmisi parimatest praktikatest andmete dokumenteerimise, varundamise ja jagamise kohta. Teeme läbi harjutusi ning ülesandeid uute teadmiste kinnistamiseks.
Here we offer a two-day workshop with the primary aim of introducing participants to good enough practices for managing their data. On the first day, participants will learn about the basics of data management, good research practices, Common European Data Spaces, data management and governance in industry and data management plans. On the second day, participants will learn how to organise their data, how to make it FAIR, about electronic lab notebooks and how to make their computational results reproducible.
Registration deadline: February 25, 2024
“Python algajatele” kursus tutvustab Pythoni programmeerimist, mis on mõeldud neile, kellel on vähe või puudub varasem kogemus. Kursuse lõpus on õpilastel kindel arusaam Pythoni põhitõdedest ja saavad alustada oma teekonda Pythoni programmeerimises.
R on tasuta tarkvarakeskkond statistilise arvutamise ja graafika jaoks. See kompileerib ja töötab erinevatel UNIX platvormidel, Windowsil ja MacOS-il.
RStudio on oluline tööriist kõigile, kes tegelevad R programmeerimisega. See mängib olulist rolli andmeanalüüsis, hõlbustades andmete importimist, juurdepääsu, transformeerimist, uurimist, visualiseerimist ja modelleerimist. Kui alustate oma teekonda R-i õppimisel, siis on nüüd ideaalne hetk tutvuda RStudioga. Avastage, mis on RStudio, ja alustage selle võimaluste kasutamist oma andmeanalüüsi ja programmeerimisoskuste täiustamiseks.
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.
Application deadline: 26 February 2024
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.
Application deadline: 15 February 2024
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.
Application deadline: 06 March 2024
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.
Applications close: 25 February 2024