E5444 Analysis of sequencing data

Faculty of Science
Autumn 2023
Extent and Intensity
2/1/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
Teacher(s)
Mgr. Eva Budinská, Ph.D. (lecturer)
prof. MUDr. Mgr. Marek Mráz, Ph.D. (lecturer)
Ing. Vojtěch Bartoň (lecturer)
doc. Ing. Vlad Popovici, PhD (lecturer)
Guaranteed by
Mgr. Eva Budinská, Ph.D.
RECETOX – Faculty of Science
Contact Person: Mgr. Eva Budinská, Ph.D.
Supplier department: RECETOX – Faculty of Science
Timetable
Wed 9:00–10:50 C04/118, Wed 11:00–11:50 C04/118
Prerequisites
At least a basic knowledge of work in Linux system, knowledge of molecular biology and basic programming knowledge is expected. Knowing the basics of statistics and R is an advantage.
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The aim of the course is to acquaint students with basic principles and advanced methods of analysis of data from next generation sequencing experiments, particularly from the Illumina platform.
Learning outcomes
Student at the end of the course will:
- know the latest NGS methods (next and third generation sequencing), their use and the type of data they produce.
- be able to distinguish the type of method based on the data. - know the basic scheme of data analysis.
- able to work with Linux, Bash and R at a level sufficient for analysis of NGS data.
- know how to select tools for data processing and apply them to real data.
- be able to analyze NGS data starting from quality control over alignment to the detection of deferentially expressed genes (in RNA-Seq), variants (CNV with SNP), genome assembly, etc.
Syllabus
  • 1. Introduction to NGS technologies: a brief introduction to biology, sequencing, history, NGS technologies and their applications, sample extraction, library preparation, basic glossary. Course requirements and schedule.
  • 2. Pitfalls of NGS and the consequences for data analysis.
  • 3. Data sources. The basic scheme of data analysis: how the data look like, definition of general steps in NGS data analysis, basic differences in dependence on the application (eg. variant calling vs RNA-Seq …).
  • 4. Introduction to software for data analysis: a brief introduction to work with Linux, Bash and R, data formats and the differences between them.
  • 5. Data preprocessing and quality control: tools for quality control, Phred score, examples on sample data.
  • 6. Alignment and post-processing: reference genome databases, annotations, the differences between them and application, explanations of alignment algorithms, differences between spliced/non-spliced ​​tools and their application, alignment quality control, alignment visualization.
  • 7. Analysis of RNAseq data - differentially expressed genes
  • 8. Variant calling – targeted sequencing, methods for calling, specific QC steps
  • 9. Metagenomics (16S, ITS, WMGS) / algorithms for taxonomy and functional assignment
  • 10. Statistics and visualisation
  • 11 and 12. Project defense
Literature
    recommended literature
  • https://www.nature.com/nrg/series/nextgeneration/index.html
Teaching methods
The course will combine theoretical lectures with practical exercises and demonstrations on sample data.
Assessment methods
During the semester, students will work on their own data analysis project. Student must score at least 10/20 points from the project to be admitted to the exam and to receive credits (if the finalization is just "zápočet"). The project must be submitted before the start of the exam period and consists of the code, data, results and a written report. Students who have to complete the course with an exam must then take a final written test, which will consist of 10 questions valued at a total of 20 points. In order to successfully complete the course, it is necessary to achieve a minimum of 20 points (10 from the project and 10 from the exam).
Language of instruction
English
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2022, Autumn 2024.
  • Enrolment Statistics (Autumn 2023, recent)
  • Permalink: https://is.muni.cz/course/sci/autumn2023/E5444