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Algorithms in Computational Biology (236522) 

Spring 2002 

Lecturer: Prof. Shlomo MoranTA: Ydo Wexler

Lecture: Tuesday12:30-14:30, Taub 6Tutorial: Tuesday11:30-12:30, Taub 6

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Course Information(pages with this and more info will be distributed by next

week)

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: to be decided, after coordination with the

students.

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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 

A brochure of Prof. Geiger course of last Semester will be available at Taub library (this Semester less topics will be covered, some of which, possibly, in more details)

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 29.10.02 (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)

Sou

rce:

Mat

hew

s &

van

Hol

de

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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

Sou

rce:

Alb

erts

et

al

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Example for Phylogenetic AnalysisInput: four nucleotide sequences: AAG, AAA, GGA, AGA taken from four species.

Question: Which evolutionary tree best explains these sequences ?

AGAAAA

GGAAAG

AAA AAA

AAA

21 1

Total #substitutions = 4

One Answer (the parsimony principle): Pick a tree that has a minimum total number of substitutions of symbols between species and their originator in the evolutionary tree (Also called phylogenetic tree).

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Example ContinuedThere are many trees possible. For example:

AGAGGA

AAAAAG

AAA AGA

AAA

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1

Total #substitutions = 3

GGAAAA

AGAAAG

AAA AAA

AAA

11 2

Total #substitutions = 4

The left tree is “better” than the right tree.

Questions:Is this principle yielding realistic phylogenetic trees ? (Evolution)How can we compute the best tree efficiently ? (Computer Science)What is the probability of substitutions given the data ? (Learning)Is the best tree found significantly better than others ? (Statistics)

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Werner’s Syndrome

A successful application of genetic analysis for Gene

Hunting

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The Disease

First references in 1960s Causes premature ageing Autosomal recessive Linkage studies from 1992 WRN gene cloned in 1996 Subsequent discovery of mechanisms involved in

wild-type and mutant proteins

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Marker Distance Distance from prior from first DHS133 0.0D8S136 7.6 7.6D8S137 7.4 15.0D8S131 0.9 15.9D8S339 6.7 22.6D8S259 1.6 24.2FGFR 2.5 26.7D8S255 2.8 29.5ANK 2.1 31.6PLAT 2.8 34.4D8S165 11.4 45.8D8S166 1.0 46.8D8S164 43.8 90.6

Identifying the Marker/s

Match most ‘likely’ cumulative distance against cumulative distances from marker file.

Distance 22.6cM (centi Morgans) fell exactly on the marker D8S339.

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Locating D8S339

Position of marker D8S339 was unknown. But positions of the adjacent markers D8S131 and

D8S259 were known. Recombination distances from D8S339 to both

D8S131 and D8S259 are given. By assuming recombination physical distance, we

estimate position of D8S339 in the next drawing.

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Results

D8S131 Marker KnownPosition

D8S259Marker Known Position

D8S339 Estimated Position (1993)

WRN Actual

Position (1996)

http://genome.ucsc.edu/cgi-bin/hgTracks?position=chr8:32213515-38608031

Linkage accuracy: ~1,250,000 bp