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Algorithms in Computational Biology (236522) 

Spring 2002 

Lecturer: Shlomo Moran, Taub 639, tel 4363 Office hours Wednesday 1630-1730TA: Ydo Wexler, Taub 431, tel 4927Office hours Monday 1030-1130

Lecture: Tuesday 11:30-13:30, Taub 2Tutorial: Monday 9:30-10:30, Taub 4

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Course Information

Requirements & Grades: 15-25% homework, in five theoretical question

sets. [Submit in two weeks time]. Homework is obligatory.

75-85% test. Must pass beyond 55 for the homework’s grade to count

Exam date: 7.7.04.

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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: www.cs.technion.ac.il/~cs236522

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Course PrerequisitesComputer 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).

Studying the algorithms in this course while acquiring enough biology background is far more rewarding than ignoring the biological context.

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Biological Background

This class has been edited from Nir Friedman’s lecture which is available at www.cs.huji.ac.il/~nir. Changes made by Dan Geiger, then Shlomo Moran.

Solve questions 1-3, p. 30 (to be on the course web site)

Due time: Tutorial class of 22.3.04 (~2 weeks from today), or earlier in the teaching assistant’s mail slot.

First home work assignment: Read the first chapter (pages 1-30) of Setubal et al., 1997. (a copy is available in the Taub building library, and one for loan at Fishbach).

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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.

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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 PolymorphismSSR: Simple Sequence Repeat

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

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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

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Topics I

Dealing with DNA/Protein sequences: Genome projects and how sequences are found Finding similar sequences Models of sequences: Hidden Markov Models Transcription regulation Protein Families Gene finding

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Topics II

Models of genetic change: Long term: evolutionary changes among species Reconstructing evolutionary trees from sequences Short term: genetic variations in a population Finding genes by linkage and association

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Topics III (if time allows)

Protein World: How proteins fold - secondary & tertiary structure How to predict protein folds from sequences data How to analyze proteins changes from raw

experimental measurements (MassSpec)

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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.

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DNA OrganizationS

ourc

e: A

lber

ts e

t al

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The Double HelixS

ourc

e: A

lber

ts e

t al

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DNA Components

Four nucleotide types: Adenine Guanine Cytosine Thymine

Hydrogen bonds(electrostatic connection): A-T C-G

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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

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Genetic Information

Genome – the collection of genetic information.

Chromosomes – storage units of genes.

Gene – basic unit of genetic information. They determine the inherited characters.

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GenesThe 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)

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The Cell

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

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Example: Tissues in Stomach

How is this variety encoded and expressed ?

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Central Dogma

Transcription

mRNA

Translation

ProteinGene

cells express different subset of the genesIn different tissues and under different conditions

שעתוק תרגום

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Transcription

Coding sequences can be transcribed to RNA

RNA nucleotides: Similar to DNA, slightly different backbone Uracil (U) instead of Thymine (T)

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Transcription: RNA Editing

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.

1. Transcribe to RNA2. Eliminate introns3. Splice (connect) exons* Alternative splicing exists

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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.

...

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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.

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Genetic Code

There are 20 amino acids from which proteins are build.

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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

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Protein Structure

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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

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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

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The Tree of Life

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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.

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Species ≡ VerticesStates ≡ Colors

Characters ≡ Colorings

Evolutionary tree ≡ A tree with many colorings, containing the given vertices

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Evolutionary trees should avoid

reversal transitions

A species regains a state it’s direct ancestor has lost. Famous examples:

Teeth in birds. Legs in snakes.

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Evolutionary trees should avoid convergence transitions

Two species possess the same state while their least common ancestor possesses a different state.

Famous example: The marsupials.

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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.

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A character is Homoplasy Free

↕ The corresponding coloring is convex

(each color induces a block)

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

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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?

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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?

RBRRRRBBRRRB

The Perfect Phylogeny Problem(combinatorial setting)

NP-Hard In general, in P for some special cases