BIOWEEK 2020
The Bioweek 2020 is a five-day comprehensive training in bioinformatics consisting of three separate courses:
3.2.2020 Using modern HPC environment efficiently
4.2-5.2.2020 Data analysis with R
6.2-7.2.2020 Introduction to RNA-seq data analysis
You can participate in ALL or only SELECTED Bioweek's courses. We have planned the Bioweek 2020 so that we are building on top of the previous knowledge. For example, the last course (basics of RNA-seq data analysis) has command line, R/RStudio and CSC services usage as prerequisites.
Lecturers (CSC, Finland): Ari-Matti Saren, Kimmo Mattila, Jesse Harrison, Anni Pyysing, Maria Lehtivaara, Laxmana Yetukuri
Language: English
Price: Free of charge
Using modern HPC environment efficiently
Description
Using a modern HPC environment efficiently requires not only a good understanding of your scientific problem, but also some familiarity with the technical aspects of the system. On this course we will take a look at some of the most essential ones: Using modern storage services, running jobs through a batch job system and some solutions for getting your analysis environment running on a HPC platform.
Learning outcomes
After the course, participants:
- will have basic skills in using object storage
- can use object storage services as part of their analysis workflows
- can run batch jobs using Slurm
- will have basic skills in using Singularity containers in HPC environment
Prerequisites
The participants are required to have basic skills/knowledge in Command line usage / Linux basics
Program: see Timetable menu
Data analysis with R
Description
R is a language that has become one of the most popular tools for data manipulation, visualization and statistics. While there are many R courses, learning these skills can involve a steep learning curve, especially for people with no experience in programming or data analysis. This two-day course aims to help with this initial difficulty by equipping learners with essential skills in data wrangling, plotting and running commonly used statistical tests in R.
The course topics include data importing and exporting, handling complex data sets and creating publication-ready plots with R. We also cover statistical theory and tests including t-tests, linear regression and the Chi-squared test. The course materials are available on GitHub (feel free to have a look at them before the course starts!)
Learning outcomes
After attending this course, participants will be able to:
- Navigate RStudio
- Understand R syntax and how to write R code
- Import and export data using R
- Use tidyverse for data wrangling
- Use ggplot2 for creating high-quality plots
- Employ t-tests, linear regression and Chi-squared tests in R
Prerequisites
- No prior experience of programming or using R is expected
- No data analysis or statistical experience is required (but is likely to be beneficial)
Program: see Timetable menu
Introduction to RNA-seq data analysis
Description
After learning the basics of command line usage, HPC and R/Rstudio, it's time to put your new skills in use and use them in RNA-seq data analysis! This course is aimed at bioscientists who are planning on analysing their RNA-seq data. During the course, we will learn the basic steps in RNA-seq analysis and how to use some of the most common analysis tools on command line and in R.
Learning outcomes
After the course, participants:
- can name and discuss the different stages of a basic RNA-seq analysis and common tools used in these steps,
- can run some RNA-seq analysis tools on command line
- import and start analysing their RNA-seq data in R
Prerequisites
The participants are required to have basic skills/knowledge in:
- Command line usage / Linux basics
- Current CSC services
- R and Rstudio
Bioweek is structured so that this course builds on top of the earlier courses. This means that the prerequisite skills can be learned on the two courses organised earlier on the same week. If you have no prior knowledge or suspect your knowledge on these subjects, please consider participating in all the three courses.
Program: see Timetable menu
