Algorithms in Computational Biology (236522) Fall 2005-6 Lecture #1

1. Algorithms in Computational Biology (236522) Fall 2005-6 Lecture #1 Lecturer: Shlomo Moran, Taub 639, tel 4363 Office hours: Wednesday 12:30-13:30 (or 15:30-16:30) TA: Ilan Gronau, Taub 700, tel 4894 Office hours Monday 1530-1630 Lecture: Wednesday 10:30-12:30, Taub 6 Tutorial: Monday 14:30-15:30, Taub 6 This class has been initially edited from Nir Friedman’s lecture at the Hebrew University. Changes made by Dan Geiger, then by Shlomo Moran.

2. Course Information Requirements & Grades: • 15-25% homework, in five assignments. [Submit in two weeks time]. Homework is obligatory. • 75-85% test. Must pass beyond 55 for the homework’s grade to count • Exam date: 1.3.06/to be coordinated.

3. Bibliography • Biological Sequence Analysis, R.Durbin et al. , Cambridge University Press, 1998 • Introduction to Molecular Biology, J. Setubal, J. Meidanis, PWS publishing Company, 1997  • Phylogenetics, C. Semple, M. Steel, Oxford press, 2003 • url:webcourse.cs.technion.ac.il/~cs236522

4. Course Prerequisites Computer Science and Probability Background • Data structure 1 (cs234218) • Algorithms 1 (cs234247) • Probability (any course) Some Biology Background • Formally: None, to allow CS students to take this course. • Recommended: Molecular Biology 1 (especially for those in the Bioinformatics track), or a similar Biology course, and/or a serious desire to complement your knowledge in Biology by reading the appropriate material (see the course web site).

5. Biological Background First home work assignment: Read the first chapter (pages 1-30) of Setubal et al., 1997. (copies are available in the Taub building library, and in the central library). Answer the questions of the first assignment in the course site. Due time: Tutorial class of 21.11.05 (<3 weeks from today).

6. Computational Biology Computational biology is the application of computational tools and techniques to (primarily) molecular biology.  It enables new ways of study in life sciences, allowing analytic and predictive methodologies that support and enhance laboratory work. It is a multidisciplinary area of study that combines Biology, Computer Science, and Statistics. Computational biology is also called Bioinformatics, although many practitioners define Bioinformatics somewhat narrower by restricting the field to molecular Biology only.

7. Examples of Areas of Interest • Building evolutionary trees from molecular (and other) data • Efficiently constructing genomes of various organisms • Understanding the structure of genomes (SNP, SSR, Genes) • Understanding function of genes in the cell cycle and disease • Deciphering structure and function of proteins _____________________ SNP: Single Nucleotide Polymorphism SSR: Simple Sequence Repeat

8. Exponential growth of biological information: growth of sequences, structures, and literature.

9. Course Goals • Learning about computational tools for (primarily) molecular biology. • Cover computational tasks that are posed by modern molecular biology • Discuss the biological motivation and setup for these tasks • Understand the kinds of solutions that exist and what principles justify them

10. Topics I Dealing with DNA/Protein sequences: • Informal biological background. (1 week) • Finding similar sequence (~3 weeks) • Models of sequences: Hidden Markov Models (~2 weeks) • Parameter estimation: ML methods and the EM algorithm (~4 weeks)

11. Topics II Reconstructing evolutionary trees: • Background: Darwin’s theory of evolution • Distance based methods (~2 weeks) • Character based methods (~2 weeks) The presentations are similar to these given in the fall Semester 04-05, and can be found in the site of that semester. Updated presentations will be uploaded to the course site before the lectures.

12. Human Genome Most human cells contain 46 chromosomes: • 2 sex chromosomes (X,Y): XY – in males. XX – in females. • 22 pairs of chromosomes named autosomes.

13. DNA Organization Source: Alberts et al

14. The Double Helix Source: Alberts et al

15. DNA Components Four nucleotide types: • Adenine • Guanine • Cytosine • Thymine Hydrogen bonds (electrostatic connection): • A-T • C-G

16. Genome Sizes • E.Coli (bacteria) 4.6 x 106 bases • Yeast (simple fungi) 15 x 106 bases • Smallest human chromosome 50 x 106 bases • Entire human genome 3 x 109 bases

17. Genetic Information • Genome – the collection of genetic information. • Chromosomes – storage units of genes. • Gene – basic unit of genetic information. They determine the inherited characters.

18. Genes The DNA strings include: • Coding regions (“genes”) • E. coli has ~4,000 genes • Yeast has ~6,000 genes • C. Elegans has ~13,000 genes • Humans have ~32,000 genes • Control regions • These typically are adjacent to the genes • They determine when a gene should be “expressed” • “Junk” DNA (unknown function - ~90% of the DNA in human’s chromosomes)

19. The Cell All cells of an organism contain the same DNA content (and the same genes) yet there is a variety of cell types.

20. Example: Tissues in Stomach How is this variety encoded and expressed ?

21. Transcription Translation mRNA Protein Gene Central Dogma שעתוק תרגום cells express different subset of the genes In different tissues and under different conditions

22. Transcription • Coding sequences can be transcribed to RNA • RNA • Similar to DNA, slightly different nucleotides: different backbone • Uracil (U) instead of Thymine (T) Source: Mathews & van Holde

23. Transcription: RNA Editing • Transcribe to RNA • Eliminate introns • Splice (connect) exons • * Alternative splicing exists Exons hold information, they are more stable during evolution. This process takes place in the nucleus. The mRNA molecules diffuse through the nucleus membrane to the outer cell plasma.

24. RNA roles • Messenger RNA (mRNA) • Encodes protein sequences. Each three nucleotide acids translate to an amino acid (the protein building block). • Transfer RNA (tRNA) • Decodes the mRNA molecules to amino-acids. It connects to the mRNA with one side and holds the appropriate amino acid on its other side. • Ribosomal RNA (rRNA) • Part of the ribosome, a machine for translating mRNA to proteins. It catalyzes (like enzymes) the reaction that attaches the hanging amino acid from the tRNA to the amino acid chain being created. • ...

25. Translation • Translation is mediated by the ribosome • Ribosome is a complex of protein & rRNA molecules • The ribosome attaches to the mRNA at a translation initiation site • Then ribosome moves along the mRNA sequence and in the process constructs a sequence of amino acids (polypeptide) which is released and folds into a protein.

26. Genetic Code There are 20 amino acids from which proteins are build.

27. Protein Structure • Proteins are poly-peptides of 70-3000 amino-acids • This structure is (mostly) determined by the sequence of amino-acids that make up the protein

28. Protein Structure

29. Evolution • Related organisms have similar DNA • Similarity in sequences of proteins • Similarity in organization of genes along the chromosomes • Evolution plays a major role in biology • Many mechanisms are shared across a wide range of organisms • During the course of evolution existing components are adapted for new functions

30. Evolution Evolution of new organisms is driven by • Diversity • Different individuals carry different variants of the same basic blue print • Mutations • The DNA sequence can be changed due to single base changes, deletion/insertion of DNA segments, etc. • Selection bias

31. The Tree of Life Source: Alberts et al

32. Example of a graph theoretic problem related to evolution trees: the perfect phylogeny problem

33. Characters in Species • A (discrete)character is a property which distinguishes between species (e.g. dental structure, a certain gene) • A characters state is a value of the character (human dental structure). • Problem: Given set of species, specified by their characters, reconstruct their evolutionary tree.

34. Species ≡ VerticesCharacters ≡ Colorings States ≡ Colors Each species is identified by its states Evolutionary tree ≡ A tree with many colorings, containing the given vertices B D A C = No teeth = teeth

35. Another tree Which tree is more reasonable? C D A B = No teeth = teeth

36. Evolutionary trees should avoidreversal transitions • A species regains a state it’s direct ancestor has lost. • Famous (and rare) examples: • Teeth in birds. • Legs in snakes.

37. Evolutionary trees should avoidconvergence transitions • Two species possess the same state while their least common ancestor possesses a different state. • Famous example: The marsupials.

39. Common Assumption:Characters with Reversal or Convergent transitions are highly unlikely in the Evolutionary Tree A character that exhibits neither reversals nor convergence is denoted homoplasy free.

40. A character is Homoplasy Free ↕ The corresponding coloring is convex(each color induces a connected subtree)

41. A partial coloring is convex if it can be completed to a (total) convex coloring

42. The Perfect Phylogeny Problem • Input: a set of species, and many characters, each assign states (colors) to the species. • Question: is there a tree T containing the species as vertices, in which all the characters (colorings) are convex?

43. RRB BBR RRR RBR The Perfect Phylogeny Problem(combinatorial setting) Input: Some colorings (C1,…,Ck) of a set of vertices (in the example: 3 colorings: left, center, right, each by (the same) two colors). Problem: Is there a tree T which includes these vertices, s.t. (T,Ci) is convex for i=1,…,k? NP-Hard In general, in P for some special cases