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Literature

  • BV: Brejová, Vinař: Metódy v bioinformatike. (preliminary version of lecture notes in Slovak, only several lectures) pdf
  • DEKM: Durbin, Eddy, Krogh, Mitchison: Biological sequence analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press 1998. Can be studied in the FMFI library under code I-INF-D-21
  • ZB: Zvelebil, Baum: Understanding Bioinformatics. Taylor & Francis 2008. Can be studied in the FMFI library under code I-INF-Z-2

For each lecture, we list the book chapters best corresponding to the covered material. However, the lecture may differ substantially from the listed chapters which serve as the source of additional information.

Recordings of lectures in Slovak from 2018/19

Notes and presentations

L: lecture (everybody), TI: tutorial for computer science/informatics students, TB: tutorial for biology/chemistry/physics students

Sept. 25

L: Introduction, course rules, sequencing and genome assembly

BV chapter 1

TI: Introduction to biology

ZB chapter 1

TB: Introduction to computer science, UCSC genome browser

Oct. 2

L: Genome assembly 2

TI: Introduction to probability, genome coverage by sequencing reads

TB: Introduction to dynamic programming, introduction to probability

Oct. 9

L: Sequence alignment: Smith-Waterman, Needleman-Wunsch, scoring

BV chapter 2, DEKM chapter 2.1-2.4, 2.8, ZB chapter 4.1-4.4, 5.1-5.2

TI: Introduction to dynamic programming, proteomics

TB: Dynamic programming for sequence alignment, dotplots

Oct. 16

L: Sequence alignment: heuristic alignment (BLAST), statistical significance of alignments, whole genome alignments, multiple alignments

BV chapter 2, DEKM chapter 2.5, 2.7, 6.1-6.3; ZB chapter 4.5-4.7, 5.3-5.5

TI: Advanced algorithms for sequence alignment

TB: Programs for sequence alignment, scoring schemes, introduction to projects

Projects

Oct. 23

L: Gene finding, hidden Markov models

BV chapter 4, DEKM chapter 3; ZB chapter 9.3, 10.4-10.7

TI: Fast similarity search, BLAST, MinHash

TB: Hidden Markov models, E-value

Oct. 30

L: Phylogenetic tree reconstruction (parsimony, neighbor joining, models of evolution)

BV chapter 3, DEKM chapter 7,8; ZB chapter 7, 8.1-8.2, video

TI: Algorithms for HMM

TB: Substitution models, bootstrap, tree rooting

Nov. 6

L: Comparative genomics, detection of positive and purification selection, comparative gene finding, phylogenetic HMMs

BV chapter 5, ZB chapter 9.8, 10.8,

TI: Substitution models

TB: Practical phylogenetic trees

Nov. 13

L: Gene expression, clustering, classification, transcription factors, sequence motifs

DEKM chapter 5.1, 11.5, ZB chapter 6.6,15.1,16.1-16.5,17.1

TI: Felsenstein algorithm, algorithms for HMM and phyloHMM

TB: K-means clustering, enrichment, multiple testing correction

Nov. 20

L: Protein structure and function

DEKM chapter 5; ZB chapter 4.8-4.10, 6.1-6.2, 13.1-13.2

TI: Motif finding by EM and Gibbs sampling

TB: Introduction to context-free grammars, enrichment, motifs

Nov. 27

L: RNA, secondary structure, Nussinov algorithm, stochastic context-free grammars, RNA family profiles

DEKM chapter 10, ZB chapter 11.9

TI: Examples of biological databases, introduction to context-free grammars, HMM topology

TB: Proteins, example of command-line tools

Dec. 4

L: Population genetics

TI: RNA structure

TB: Course summary, graphs, population genetics, RNA structure, expression data

Dec. 11

L: Optional journal club presentations (no lecture)

TI: Integer linear programming, course summary

TB: Project consultations in January

Dec. 16