SubjectsSubjects(version: 954)
Course, academic year 2023/2024
  
Gene Expression Data Analysis - M143004
Title: Analýza genové exprese
Guaranteed by: Department of Informatics and Chemistry (143)
Faculty: Faculty of Chemical Technology
Actual: from 2019
Semester: summer
Points: summer s.:4
E-Credits: summer s.:4
Examination process: summer s.:
Hours per week, examination: summer s.:2/1, C+Ex [HT]
Capacity: unlimited / unlimited (unknown)
Min. number of students: unlimited
State of the course: taught
Language: Czech
Teaching methods: full-time
Teaching methods: full-time
Level:  
Note: enabled for web enrollment
Guarantor: Kolář Michal Mgr. Ph.D.
Interchangeability : N143049
Annotation -
This course will present basic types of functional genetics data such as RT-qPCR data, DNA chips profiling data, and high-throughput sequencing data. Students will acquire information on how to preprocess, clean, and standardize data, and will be acquainted with specific statistical and exploratory data analysis methods used for multidimensional genomic data processing. Students will learn how to interpret data using gene ontologies, how to archive, and how to visualize data. During exercises students will practise gained knowledge on the real life data, and will master commonly used online resources and analytical tools.
Last update: Hladíková Jana (04.01.2018)
Aim of the course -

Students will be able to:

  • Process RT-qPCR, DNA chips and high-throughput sequencing functional genetics data.
  • Validate experimental results using exploratory data analysis.
  • Use statistical methods to analyse and interpret functional genetics data.
  • Effectively design experiment taking into account caveats of individual experimental approaches.
Last update: Hladíková Jana (04.01.2018)
Literature -

R: Zvárová J, Mazura I (eds.), Metody molekulární biologie a bioinformatiky, Karolinum, Praha 2013, ISBN: 978-8024621500

A: Tevfik Dorak, M. (ed.), Real-time PCR (Advanced Methods), Taylor & Francis 2006, ISBN: 978-0415377348

A: Cedric Gondro, Primer to Analysis of Genomic Data Using R, Springer International Publishing 2015, ISBN 978-3-319-14475-7

A: Eija Korpelainen, Jarno Tuimala, Panu Somervuo, Mikael Huss, Garry Wong, RNA-seq Data Analysis: A Practical Approach, Chapman and Hall/CRC 2014, ISBN 9781466595002

Last update: Svozil Daniel (31.10.2018)
Learning resources -

none

Last update: Hladíková Jana (04.01.2018)
Requirements to the exam - Czech

Na konci semestru studenti presentují výsledky úkolů a skládají písemnou zkoušku.

Last update: Hladíková Jana (04.01.2018)
Syllabus -

1. Introduction. Types of functional genetics data. Aims of the anlyses.

2. RT-qPCR data preprocessing: Primer and probe design. Standard curve.

3. RT-qPCR data preprocessing: Amplification curve. Threshold cycle. Background correction. Data normalization.

4. Transcription chips data preprocessing: Noise removal. Data normalization. Relative and absolute quantification.

5. Transcription chips data preprocessing: Variance stabilization. Summarization of intensity values.

6. High-throughput sequencing: Reading and mapping.

7. Further applications: Analysis of single nucleotide polymorphisms and chromosomal abberations. DNA methylation.

8. Expolratory data analysis: Dimensionality reduction. Clustering. Control points.

9. Linear models. Problem of test multiplicity.

10. Classification methods.

11. Design of experiments and randomization. Replication.

12. Annotation and results archivation: Genomic browsers and expression databases.

13. Biological interpretation: Gene Set Enrichment Analysis (GSEA). Database of signalling pathways. Gene ontologies.

14. Integration with interaction data: Network analysis. Database of interaction data.

Last update: Hladíková Jana (04.01.2018)
Registration requirements -

Biochemistry, Molecular genetics

Last update: Hladíková Jana (04.01.2018)
Course completion requirements -

During the term students work on various projects. The final exam consists of the presentation of projects and of a written test.

Last update: Svozil Daniel (26.01.2018)
Teaching methods
Activity Credits Hours
Obhajoba individuálního projektu 0.5 14
Účast na přednáškách 1 28
Příprava na přednášky, semináře, laboratoře, exkurzi nebo praxi 1 28
Práce na individuálním projektu 0.5 14
Příprava na zkoušku a její absolvování 1 28
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