Education and Training
We are keen on fostering a deep understanding of bioinformatics and statistical analysis. Therefore we are involved in arranging courses, workshops, and hands-on sessions.
Hands-on
One-hour training sessions where we show you how to perform a specific analysis or method. We provide four hands-on sessions per year at 11:00.
This year we offer the sessions described below. Registration opens 3 weeks before each hands-on (link to the right).
Thursday 23rd of March, 2023
You will practice how to clean, transform, and summarize data in order to improve the quality and interpretation of data. Tidyverse includes dplyr and other packages.
Thursday 25th of May, 2023
You will practice how to perform linear and logistic regression in R.
Thursday 28th of September, 2023
You will practice how to generate scatter plots to visualize your data, regression lines, confidence intervals, among others.
Thursday 23rd of November, 2023
You will practice how to generate histograms, boxplots, density plots, and violin plots.
Registration open
Click to open the Registration form of the "Linear and logistic regression in R" hands-on
Training Material
PhD Courses in Bioinformatics
We offer the following PhD courses free of charge for national and international PhD students.
Spring courses
Format: Online via Zoom
Course description
The course includes a combination of lectures and practical sessions that introduce the analysis of genomic and proteomic data using bioinformatics tools and public databases (such as NCBI, UCSC and ENSEMBL). It covers DNA sequence alignment, protein expression analysis and pathway analyses.
The following topics covered are:
- Use of molecular biological databases available from NCBI, UCSC and ENSEMBL.
- Work on web-based platforms for data-intensive biomedical research.
- Sequence analysis methods in theory and practice, to understand the function, structure and evolution of sequence data.
- Analysis of protein expression, in order to identify differences between different groups.
- Downstream analysis such as pathway analysis, clustering and gene ontology to analyze how changes in protein expression can affect biological processes in the cell.
Notes
- The course comprises 54 working hours where you are expected to spend time of your own to complete the exercises (self-studies).
- Participants are required to bring their own computer with the corresponding administrator rights.
- This course is a pre-requisite to take Bioinformatics II course.
- If you want to learn how to generate the results from this course, we recommend you to take the Analysis of next generation sequencing data course.
Course dates
- Weeks 5-10
- Schedule for Bioinformatics I (2023)
Documents
Format: Online via Zoom
Course description
The course covers the basics in R programming, a language and environment for statistical computing and graphics. The course includes a combination of lectures and computer exercises focused on visualization and statistical analysis. No prior knowledge is required..
The following topics covered are:
- Introduction to R programming and scripting
- Experimental design
- Introduction to statistical analysis in R
- Visualization in R
Notes
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During the course you will learn how to perform: power calculations, t-tests, non-parametric tests, ANOVA, linear and logistic regressions. Thus, you are expected to be familiar with these statistical concepts since they will not be covered in depth during the sessions.
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The course comprises 54 working hours where you are expected to spend time of your own to complete the exercises (self-studies)
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Participants are required to bring their own computer with the corresponding administrator rights.
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This course is a pre-requisite to take Gene expression analysis using R .
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This course is recommended when taking the Analysis of Next Generation Sequencing course.
Course dates
- Weeks 10-13 (14-17 project work, one extra week due to Easter)
- Schedule for R programming (2023)
Documents
Format: Online via Zoom
Course description
The course covers the basics in Unix and how to work in the command line to aid in the analysis of large amounts of data and automate such tasks. The course includes a combination of lectures and practical exercises focused on handling genomic data. No prior knowledge is required.
The following topics covered are:
- The shell
- File system and permissions
- Text editors
- Handling files
- Regular expressions
- Piping
- For loops
- File compression
- Program installation
- Bash scripting
Notes
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The course comprises 54 working hours, and you are expected to spend time on your own to complete the exercises (self-studies).
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Participants are required to bring their own computer with the corresponding administrator rights
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This course is a pre-requisite to take Analysis of next generation sequencing data course.
Course dates
- Weeks 16-20 (21-23 project work)
- Schedule for Unix applied to genomic data (2023)
Documents
Autumn courses
Format: Online via Zoom
Course description
The course contains a combination of lectures and practical sessions that introduce the analysis of DNA sequencing (variant analysis) and RNA sequencing (gene expression analysis). Total RNA and single cell RNA are covered.
The following topics covered are:
- Use of programs to analyze the quality of DNA and RNA sequence data.
- Mapping DNA and RNA sequence data against a reference genome.
- Identify genetic variants in relation to a reference.
- Compare gene expression for groups of different phenotype using RNA sequence data.
- Analyze difference in gene expression, between different cell types using single-cell data.
- Clustering and geneontology to analyze how changes in RNA expression can affect biological processes in the cell
Notes
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The course comprises 54 working hours where you are expected to spend time of your own to complete the exercises (self-studies).
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Participants are required to bring their own computer with the corresponding administrator rights.
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If you want to learn how to generate the results from this course, we recommend you to take the Analysis of next generation sequencing data course
Course prerequisites
- Bioinformatics I
Course dates
- Weeks 34-39
- Schedule for Bioinformatics II (2023)
Documents
Format: On-site at University of Gothenburg
Course description
The course includes a combination of lectures and practical sessions to introduce Python as a tool for handling large datasets. No prior Python knowledge is required; however Unix experience is highly recommended.
The following topics covered are:
- Variables
- Data types such as dictionaries, lists, sets
- In-built functions
- Control flow tools such as if, for and pass
- Statements and Comments
- Arithmetic operations
- Own functions
- Input and output
Notes
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The course comprises 60 working hours where you are expected to spend time of your own to complete the exercises (self-studies).
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Participants are required to bring their own computer with the corresponding administrator rights.
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If you are a linux beginner or have not worked in this environment we recommend you to first take the Unix applied to genomic data course.
Course dates
- Weeks 36-41
- Schedule for Python for biologists (2023)
Documents
Format: Online via Zoom
Course description
The course includes a combination of lectures, group discussions and computer exercises. Participants are directly performing the analyses of gene expression data from different applications. We focus on developing practical skills; therefore, you need to have experience in R programming.
The following topics covered are:
- Experimental design
- qPCR analysis
- Microarray analysis
- RNAseq analysis
- Single cell RNAseq analysis
Notes
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The course comprises 54 working hours where you are expected to spend time of your own to complete the exercises (self-studies).
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Participants are required to bring their own computer with the corresponding administrator rights.
Prerequisites
- To get the most of the course, students are expected to be familiar working with R. To assess this, participants must complete a task to be accepted.
- Experience with R programming is a requirement. If you are an R beginner or have not worked with R before, we recommend taking our “R programming” course.
Course dates
Documents
Format: Online via Zoom
Course description
The course includes a combination of lectures and practical sessions where participants are directly performing bioinformatics analysis of NGS data. From raw data (fastq files) to variant analysis (DNA sequencing), differential gene expression analysis (RNA sequencing) and pathway analysis. We focus on developing practical skills; therefore, you need to have experience in the Linux environment. Experience in R is not mandatory but recommended.
The following topics covered are:
- Experimental design
- NGS quality assessment
- SNP analysis in targeted resequencing
- Differential gene analysis from RNAseq data
- Downstream analysis such as pathway analysis, clustering and gene ontology
Notes
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The course comprises 54 working hours where you are expected to spend time of your own to complete the exercises (self-studies).
-
Participants are required to bring their own computer with the corresponding administrator rights.
Prerequisites
- Experience within the Linux environment is a requirement. An entry assignment must be completed to be accepted.
- If you are a Linux beginner or have not worked in this environment we recommend taking our "Unix applied to genomic data" course.
- Experience with R programming is recommended. If you are a beginner or have not worked in this environment we recommend you take our "R programming" course.
Course dates
- Weeks 43-47
- Schedule for Analysis of NGS data (2023)
Documents
Not a PhD student?
We offer the same content of the courses as a workshop for non-PhD students. The workshop runs simultaneously with the PhD course and has a registration fee of 2000 SEK. We can give any workshop at any other time if there are at least 10 participants.
To register submit a training request via our iLab page (Instructions under "Workshop registration" to the right).
We suggest to take the courses in the following order:
Master Course in Bioinformatics
Next Generation Sequencing Analysis with clinical applications, 7,5 credits (ECTS) - BMA231
The course includes a combination of lectures and practical sessions where participants will focus on the analysis and interpretation of clinical “Next Generation Sequencing” (NGS) data applying various bioinformatics webtools. The statistical tool R will be introduced to perform and visualize the results. The course covers essential concepts in molecular biology and genetics as well as the principles on “Next Generation Sequencing”-applications, with a focus on targeted resequencing and RNA-sequencing. Students will perform in depth analysis of “Next Generation Sequencing”-quality assessment and interpretation of mutation and gene expression analyses.
In collaboration with the Institute of Biomedicine.
