Bi5444 Analysis of sequencing data

Faculty of Science
autumn 2021
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).
Taught partially online.
Teacher(s)
Mgr. Eva Budinská, Ph.D. (lecturer)
doc. MUDr. Mgr. Marek Mráz, Ph.D. (lecturer)
Ing. Stanislav Smatana (lecturer)
Mgr. Jan Oppelt, Ph.D. (lecturer)
Mgr. Václav Hejret (assistant)
Guaranteed by
Mgr. Eva Budinská, Ph.D.
RECETOX - Faculty of Science
Contact Person: Mgr. Eva Budinská, Ph.D.
Supplier department: RECETOX - Faculty of Science
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.
  • 2. The basic scheme of data analysis: how the data look like, definition of general steps in NGS data analysis, differences in dependence on the application (eg. variant calling vs RNA-Seq …).
  • 3. Introduction to software for data analysis: a brief introduction to work with Linux, Bash and R, data formats and the differences between them, on-line courses
  • 4. Quality control, data processing, specifications and start of work on projects: tools for quality control, Phred score, data pre-processing, examples on sample data.
  • 5. 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.
  • 6. Analysis of RNAseq data
  • 7. Analysis of RNAseq data
  • 8. Targeted sequencing
  • 9. Analysis of ChipSeq data
  • 10. Metagenomics
  • 11. Statistics and visualisation
  • 12. Detection of biomarkers from omics experiments
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
Students with an examination (as completion of the course) must take the final test, which will consist of 10 questions scored in total by 20 points. For successful completion of the course, students must achieve at least 20 points.
Language of instruction
English
Further comments (probably available only in Czech)
The course is taught annually.
The course is taught: every week.
The course is also listed under the following terms Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020.
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