SubjectsSubjects(version: 901)
Course, academic year 2022/2023
  
Analytical methods in cancer and population genomics and transcriptomics - MB151P113E
Title: Analytical methods in cancer and population genomics and transcriptomics
Czech title: Analytické metody v nádorové a populační genomice a transkriptomice
Guaranteed by: Department of Cell Biology (31-151)
Faculty: Faculty of Science
Actual: from 2022
Semester: winter
E-Credits: 4
Examination process: winter s.:combined
Hours per week, examination: winter s.:1/0 Ex [weeks/semester]
Capacity: unlimited
Min. number of students: unlimited
Virtual mobility / capacity: no
State of the course: taught
Language: Czech
Note: enabled for web enrollment
Guarantor: Tobias Rausch, Ph.D.
Teacher(s): Mgr. Marian Novotný, Ph.D.
Opinion survey results   Examination dates   Schedule   
Annotation -
Last update: RNDr. Nataša Šebková, Ph.D. (24.10.2019)
The course consists of two parts. The first part presents basic analytical methods and computational approaches for efficient analysis of celene phenomenon sequencing data in the context of human sequencing experiments. In the second part, basic approaches to the analysis of transcriptomic data will be presented. Great emphasis will be placed on tumor genomics and its limitations. The course will seek to close the gap between computer science and genetics and will work with real-life examples from the field of tumor re-sequencing, rare diseases and population-genomic studies.
Literature -
Last update: RNDr. Nataša Šebková, Ph.D. (24.10.2019)

Cormen, Leiserson, Rivest and Stein. Introduction to Algorithms.

1000 Genomes Project Consortium, A global reference for human genetic variation. Nature. 2015 Oct 1;526(7571):68-74.

Rausch et al., Genome sequencing of pediatric medulloblastoma links catastrophic DNA rearrangements with TP53 mutations. Cell. 2012 Jan 20;148(1-2):59-71.

Medvedev et al., Computational methods for discovering structural variation with next-generation sequencing. Nat Methods. 2009 Nov;6(11 Suppl):S13-20.

Garber M, Grabherr MG, Guttman M, Trapnell C. Computational methods for transcriptome annotation and quantification using RNA-seq. Nat Methods. 2011 Jun;8(6):469-77. doi: 10.1038/nmeth.1613.

Requirements to the exam -
Last update: RNDr. Nataša Šebková, Ph.D. (24.10.2019)

The exam is a form of home project testing the acquired knowledge and skills after completing the course

Syllabus -
Last update: RNDr. Nataša Šebková, Ph.D. (25.10.2019)

Areas covered:

i) basic alignment, indexing and graph algorithms, data structures

ii) tumor genomics - tumor purity, ploidy and heterogeneity

(iii) point mutations and 'variant calling'

iv) visualization of tumor genomic characteristics

(v) approaches to 'comparing' reading to a reference transcriptome or genome

vi) approaches to identifying expressed genes and isoforms

approaches to estimating isoform frequency and differential expression

Entry requirements -
Last update: RNDr. Nataša Šebková, Ph.D. (24.10.2019)

This course is taught in English.

 
Charles University | Information system of Charles University | http://www.cuni.cz/UKEN-329.html