Syllabus
Introduction to bioinformatics:
Bioinformatics - its subject, history and main problems
Introduction to molecular biology - the structure of DNA and its analysis, genes, proteins
Classical bioinformatics algorithms:
Exhaustive search – restriction mapping, motif finding
Greedy algorithms – genome rearrangements, motif finding
Dynamic Programming Algorithms – similarity of DNA sequences, sequence alignment, alignment with gap penalties, gene prediction
Divide-and-conquer algorithms – space and time efficient sequence alignment
Graph algorithms – DNA sequencing, protein sequencing and identification, peptide sequencing
Combinatorial pattern matching – exact pattern matching, keywords trees, suffix trees, heuristic similarity search, approximate pattern matching, BLAST and FASTA
Advanced bioinformatics algorithms:
Hidden Markov Models – decoding algorithm, HMM parameter estimation
Randomized algorithms – Gibbs sampling, random projections
Computing similarity by compression
Annotation
In recent decades, biology has raised a lot of challenging mathematical problems aiming at deciphering the language of DNA sequences. Bioinformatics is a rapidly developing area of computer science driving further biological developments.
This course is focused on explaining the main algorithmic principles applicable to the solution of various biological problems. This shall provide the students with a solid foundation to understand more easily also other parts of this emerging field.
The lecture is for students of computer science without background in biology.