Education and Training

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 5-9
• Schedule for Bioinformatics I (2023)
• Schedule for Bioinformatics I (2024)

Small changes in the schedule may take place

Documents

• Syllabus for Bioinformatics I (en)
• Syllabus for Bioinformatics I (sv)

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

• 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.

• 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 Gene expression analysis using R .

• This course is recommended when taking the Analysis of Next Generation Sequencing course.

Course dates

• Weeks 17-22 
• Schedule for R programming (2023)
• Schedule for R programming (2024)

Small changes in the schedule may take place

Documents

• Syllabus for R programming (en)
• Syllabus for R programming (sv)

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

• The course comprises 54 working hours, and you are expected to spend time on 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 Analysis of next generation sequencing data course.

Course dates

• Weeks 10-17 (21-23 project work)
• Schedule for Unix applied to genomic data (2023)
• Schedule for Unix applied to genomic data (2024)

Small changes in the schedule may take place

Documents

• Syllabus for Unix applied to genomic data (en)
• Syllabus for Unix applied to genomic data (sv)

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

• 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.  

• 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 34-38
• Schedule for Bioinformatics II (2023)
• Schedule for Bioinformatics II (2024)

Small changes in the schedule may take place

Documents

• Syllabus for Bioinformatics II (en)
• Syllabus for Bioinformatics II (sv)

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

• The course comprises 60 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.

• 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 36-42
• Schedule for Python for biologists (2023)
• Schedule for Python for biologists (2024)

Small changes in the schedule may take place

Documents

• Syllabus for Python for biologists (en)
• Syllabus for Python for biologists (sv)

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

• 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

• 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

• Weeks 38-42
• Schedule for Gene expression analysis using R (2023)
• Schedule for Gene expression analysis using R (2024)

Small changes in the schedule may take place

Documents

• Syllabus for Gene expression analysis using R (en)
• Syllabus for Gene expression analysis using R (sv)

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

• 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 43-46
• Schedule for Analysis of NGS data (2023)
• Schedule for Analysis of NGS data (2024)

Small changes in the schedule may take place

Documents

• Syllabus for Analysis of NGS data (en)
• Syllabus for Analysis of NGS data (sv)