Algorithms for genomic data analysis 1000-718ADG
1. Mapping of sequencing reads
◦ pattern matching algorithms, text indexing
◦ approximate pattern matching based on text indexes
◦ techniques for finding approximate occurrences of a pattern with low similarity
2. Structural variant calling
◦ based on sequencing reads
◦ based on optical mapping data
3. RNA-seq data processing
◦ read mapping vs determination of k-mer spectrum
4. Metagenomic data analysis
◦ composition- and homology-based read classification
◦ linked reads deconvolution
5. De novo genome assembly
◦ Overlap-Layout-Consensus approach
◦ de Bruijn graphs approach
◦ contig merging and scaffolding
6. Pangenomics
◦ pangenome models and their construction methods
◦ pangenome-based sequencing data analysis
Course coordinators
Learning outcomes
Knowledge:
- knowledge of algorithmic techniques used in DNA sequence analysis
- knowledge of methods of analysis of high-throughput DNA sequencing data
Skills:
- the ability to choose the proper sequencing technique for a given biological problem
- the ability to properly design experiments using large-scale genomic technologies and to analyze the output data
- the ability to implement selected algorithms for the analysis of data from next generation sequencing
Competences:
- knows the limitations of his own knowledge, is able to formulate questions to deepen the understanding of the issue under consideration
- understands the need for a critical analysis of the study he created
Assessment criteria
Final assesment is based on lab projects and (optionally) oral exam.
Bibliography
V. Mäkinen, D. Belazzougui, F. Cunial, A. Tomescu, Genome-Scale Algorithm Design. Cambridge University Press 2015.
X. Wang, Next-Generation Sequencing Data Analysis, CRC Press 2016.
Additional information
Information on level of this course, year of study and semester when the course unit is delivered, types and amount of class hours - can be found in course structure diagrams of apropriate study programmes. This course is related to the following study programmes:
- Bachelor's degree, first cycle programme, Computer Science
- Master's degree, second cycle programme, Computer Science
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: