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Algorithms in Computational Biology

Fall 2005/6 

Lecturer: Benny Chor (benny AT cs.tau.ac.il)

Lectures: Thursdays 18:00-21:00, Schreiber 007

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Course InformationRequirements & Grades: 20-25% homework, in five-to-six assignments,

containing both “dry” and “wet” problems. Submission - two weeks from posting.

Homework submission is obligatory. You are strongly encouraged to solve the

assignments independently (or at least give it a serious try).

75-80% exam. Must pass beyond 55 for the homework’s grade to count

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Bibliography

Biological Sequence Analysis, R.Durbin et al. , Cambridge University Press, 1998

Introduction to Molecular Biology, J. Setubal and

J. Meidanis, PWS publishing Company, 1997 

Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology, D. Gusfield, Cambridge University Press, 1997.

Post-genome Informatics, M. Kanehisa , Oxford University Press, 2000.

More refs on course page.

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Course PrerequisitesComputer Science and Probability Background Computational Models Algorithms (“efficiency of computation”) Probability (any course)

Some Biology Background Formally: None, to allow CS students to take this course. Recommended: Some molecular biology course, and/or a serious

desire to complement your knowledge in Biology by reading the appropriate material.

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

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ץ אר@ Cת ה Fא Hים, ו Lמ Cש Lת ה Fים, א Pלה Rא א Cר Cית, ב Pאש Fר Hב . 

הום; Hי ת Fנ Hל-פ Lך, ע חש@ Hבהו, ו Cה תהו ו Cת Hי Cץ, ה אר@ Cה Hו

ים. Cמ Lי ה Fנ Hל-פ Lת ע פ@ ח@ Lר Hים, מ Pלה Rא Lרוח Hו

י-אור. Pה Hי Lי אור; ו Pה Hים, י Pלה Rר א יאמ@ Lו

Let Us Start from the Beginning

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13.7 billion years ago: the big bang. 5 billion years ago: our sun. 4.5 billion years ago: planet earth. 3.8 billion years ago: early forms of life. 3.8 bya – present: evolution. Akilia island, Greenland

And if you prefer the scientific version

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EvolutionA process where small random changes rccumulate within species over timetill new ones form.

The Tree of Life: A classical, basic science problem, since Darwin’s

1859 “Origin of Species”.

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Phylogeny ReconstructionGoal: Given a set of species, reconstruct

the tree which best explains their evolutionary history.

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Phylogenetic Trees Based on What?

1. Morphology

2. Single genes

3. Whole genomes

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What is Computational Biology?

Computational biology is the application of computational tools and techniques to molecular biology (primarily).  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 to the application of specialized software for deducing meaningful biological information.

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Accepted Model of Species Evolution

Initial period: primordial soup, where “you are what you eat”. (recombination events, horizontal transfers.) Later period: Formation of distinct taxa. Speciation events induce a tree-like evolution.

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Species Evolution (2)

The affinities of all the beings of the same class have sometimes been represented by a great tree... Charles Darwin, 1859

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Why Bio-informatics ?

An explosive growth in the amount of biological information necessitates the use of computers for cataloging, retrieval and analyzing mega-data (> 3 billion bps, > 30,000 genes).

• The human genome project.

• Improved technologies, e.g. automated sequencing.

• GenBank is now approximately doubling every year !!!

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New Biotechnologies & Data• Micro arrays - gene expression.

• 2D gels – protein expression.

• Multi-level maps - genetic, physical: sequence, annotation.

• Networks of protein-protein interactions.

• Cross-species relationships -• Homologous genes.• Chromosome organization.

http://www.the-scientist.com/yr2002/apr/research020415.html

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BioInformatics Tools are Crucial !

• New biotechnology tools generate

explosive growth in the amount of

biological data.

• Impossible to analyze the data manually.

• Novel mathematical, statistical,

algorithmic and computational tools

are necessary !

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Areas of Interest (very partial list)

• Building evolutionary trees from molecular (and other) data• Efficiently reconstructing the genome sequence from sub-

parts (mapping, assembly, etc.)• Understanding the structure of genomes (Genes, SNP, SSR)• Understanding function of genes in the cell cycle and disease• Deciphering structure and function of proteins• Diagnosing cancer based on DNA microarrays (“chips”)

_____________________SNP: Single Nucleotide PolymorphismSSR: Simple Sequence Repeat

Much of 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, and finally

Benny Chor. Additional slides from Zohar Yakhini and Metsada Pasmanik.

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Growth of DNA Sequence Data: GenBank

42 million sequences(“words”)

45 billion base pairs(“letters”)

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PDB ContentGrowth

http://www.rcsb.org/pdb/

(Experimentallydetermined)

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The Protein Data Bank

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Four Aspects

Biological What is the task?

Algorithmic How to perform the task at hand efficiently?

Learning How to adapt/estimate/learn parameters and

models describing the task from examples

Statistics How to differentiate true phenomena from

artifacts

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Example: Sequence Comparison

Biological Evolution preserves sequences, thus similar genes might

have similar function

Algorithmic Consider all ways to “align” one sequence against

another

Learning How do we define “similar” sequences? Use examples to

define similarity

Statistical When we compare to ~106 sequences, what is a random

match and what is true one

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Topics I

Dealing with DNA/Protein sequences:

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

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Topics II

High throughput biotechnologies –

potentials and computational challenges DNA microarrays applications to diagnostics applications to understanding gene networks

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Topics III (Structural BioInfo Course)

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

experimental measurements (MassSpec)

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Algorithmics

Will introduce algorithmic techniques that are

useful in computational genomics (and elsewhere): Dynamic programing, dynamic programing, dynamic.. Suffix trees and arrays Probabilistic models: PSSM (Position Specific

Scoring Matrices), HMM (Hidden Markov Models) Learning and classification, SVM (Support Vector

Machines) Heuristics for solving hard optimization problems

(Many problems in comp. genomics are NP-hard)

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• Each human cell contains 23 pairs of chromosomes.

• Chromosomes can be distinguished by size and by unique banding patterns.

• Male – XY, Female – XX.• This set is from a male.

DNA – The Basic Genetic Material

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Chromosomes

SKY –Differentially dye-staining chromosomes

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Watson and Crick

… On Feb. 28, 1953, Francis Crick walked into the Eagle pub in Cambridge, England, and, as James Watson later recalled, announced that "we had found the secret of life."

"The structure was too pretty not to be true."

-- JAMES D. WATSON, "The Double Helix"

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1920-1958

(1953)

DNA -the Code for Life

Died from ovarian cancer

http://www.nobel.se/medicine/laureates/1962/index.html

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The Double HelixS

ourc

e: A

lber

ts e

t al

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The Central Dogma of Molecular Biology

Transcription Translation

Replication

AC

UA A G C A

G

AC

UGUAC

DNA mRNAprotein

Phenotype

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Conclusion: DNA strands are complementary (1953).

Watson-Crick Complementarity

HumanSheepTurtleSea urchinWheat

E. coli

DNA source% of each base

Purines/Pyrimidines

Base ratios

PurinesPyrimidines

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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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Mendel and the Discovery of Genes

• Gregor Mendel, 1822-1884, studied inheritance patterns of common pea traits.  

• He concluded that traits were passed through generations in basic units which were later called genes.

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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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What is a Gene ?

DNA contains various recognition sites:• Promoter signals.• Transcription start signals.• Start codon.• Exon, intron boundaries.• Transcription termination signal.

Start codon Terminal codon

Transcribed region Un-codedregion

Un-codedregion

exon

exon exon

intron intronpromotor

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Control of the Human -Globin Gene

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Alternative Splicing 42

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Genes: How Many?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”

So called “Junk” DNA (unknown function - ~90% of the DNA in human’s chromosomes)

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Gene Finding• Only 4% of the human genome encodes for functional genes.

• Genes are found along large non-coding DNA regions.

• Repeats, pseudo-genes, introns, contamination of vectors, are confusing.

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Gene FindingExisting programs for locating genes within genomic sequences utilize a number of statistical signals and employ statistical models such as hidden Markov models (HMMs).

The problem is not solvedyet, esp. for the newly discovered “RNA genes”.

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Diversity of 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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New Roles of RNACellular Regulation

http://www.nature.com/nature/journal/v408/n6808/fig_tab/408037a0_F1.html

http://www.sciencemag.org/content/vol298/issue5602/cover.shtml

COVER: Researchers are discovering that small RNA molecules play a surprising variety of key roles in cells. They can inhibit translation of messenger RNA into protein, cause degradation of other messenger RNAs, and even initiate complete silencing of gene expression from the genome.

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Translation in Eukaryotes

http://www1.imim.es/courses/Lisboa01/slide1.6_translation.html

Animation: http://cbms.st-and.ac.uk/academics/ryan/Teaching/medsci/Medsci6.htm

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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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The Central Paradigm of Bio-informatics

Geneticinformation

Molecular structure

Biochemical function

Symptoms

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Similarity Search in Databanks

Find similar sequencesto a working draft.

As databanks grow, homologies get harder,and quality is reduced.

Alignment Tools: BLAST & FASTA (time saving heuristics-approximations).

>gb|BE588357.1|BE588357 194087 BARC 5BOV Bos taurus cDNA 5'.

Length = 369

Score = 272 bits (137), Expect = 4e-71

Identities = 258/297 (86%), Gaps = 1/297 (0%)

Strand = Plus / Plus

Query: 17 aggatccaacgtcgctccagctgctcttgacgactccacagataccccgaagccatggca 76

|||||||||||||||| | ||| | ||| || ||| | |||| ||||| |||||||||

Sbjct: 1 aggatccaacgtcgctgcggctacccttaaccact-cgcagaccccccgcagccatggcc 59

Query: 77 agcaagggcttgcaggacctgaagcaacaggtggaggggaccgcccaggaagccgtgtca 136

|||||||||||||||||||||||| | || ||||||||| | ||||||||||| ||| ||

Sbjct: 60 agcaagggcttgcaggacctgaagaagcaagtggagggggcggcccaggaagcggtgaca 119

Query: 137 gcggccggagcggcagctcagcaagtggtggaccaggccacagaggcggggcagaaagcc 196

|||||||| | || | ||||||||||||||| ||||||||||| || ||||||||||||

Sbjct: 120 tcggccggaacagcggttcagcaagtggtggatcaggccacagaagcagggcagaaagcc 179

Query: 197 atggaccagctggccaagaccacccaggaaaccatcgacaagactgctaaccaggcctct 256

||||||||| | |||||||| |||||||||||||||||| ||||||||||||||||||||

Sbjct: 180 atggaccaggttgccaagactacccaggaaaccatcgaccagactgctaaccaggcctct 239

Query: 257 gacaccttctctgggattgggaaaaaattcggcctcctgaaatgacagcagggagac 313

|| || ||||| || ||||||||||| | |||||||||||||||||| ||||||||

Sbjct: 240 gagactttctcgggttttgggaaaaaacttggcctcctgaaatgacagaagggagac 296

Pairwise alignment:

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Multiple Sequence Alignment

Multiple alignment: Basis for phylogenetic tree construction. Useful to find protein families and functional domains.

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EvolutionEvolution - a process in which small changes occur within species over time.

These changes are mainly monitored today using molecular sequences (DNA/proteins).

The Tree of Life: A classical, basic science problem, since Darwin’s

1859 “Origin of Species”.

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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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The Tree of Life

Sou

rce:

Alb

erts

et

al

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Phylogeny ReconstructionGoal: Given a set of species, reconstruct the

tree which best explains their evolutionary history.

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Today most phylogenetic trees are based on molecular sequence data (DNA or proteins).

Darwin (Origin of Species, 1859) and his contemporaries based their work on morphological and physiological properties (e.g. cold/warm blood, existance of scales, number of teeth, existance of wings, etc., etc.). Paleontological data is still in use when constructing trees for certain extinct species (e.g. dinosaures, mammoths, moas, unicorns, etc…)

Trees are Based on What ?

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Phylogenetic Trees Based on What?

1. Morphology

2. Single genes

3. Whole genomes

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www.tomchalk.com/evolution.gif

Evolution

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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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DNA Microarrays (Chips)

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A Modern Use of WC Complimentarity

A binds to TC binds to G

AATGCTTAGTCTTACGAATCAG

Perfect match

AATGCGTAGTCTTACGAATCAG

One base mismatch

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Array Based Hybridization Assays (DNA Chips)

Unknown sequence (target)Many copies.

Array of probes

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• Target hybs to WC complimentary probes only• Therefore – the fluorescence pattern is indicative of the

target sequence.

Array Based Hyb Assays

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Microarrays (“DNA Chips”)Leading edge, future technologies (since 1988):

In a single experiment, measure expression level of thousands of genes.

• Find informative genes that may have predictive power for medical diagnosis.

• Potential for personalized medicine, e.g. kits for identifying cancer types and prescribe “personal”

treatment.

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• Each chip has n “pixels” on it. Every pixel contains copies of a probe from a single gene.

• Do m experiments: Cells in each experiment are taken from different conditions: (different phase of cell cycle, different patient, different type of tissue etc.).

• Purpose: Measure mRNA expression levels (Color coded) of all n genes in one experiment.

DNA Chips - Structure

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Gene Expression Matrix

• Rows correspond to genes. (Typically n between 500 and 15,000).

• Columns correspond to experiments. (Typically m between 10 and 200).

• Entryi, j = expression level

of gene i, in experiment j.

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Algorithmic Challange

Analyse the vast amount of data in gene expression matrices.

Discover meaningful biological structures and functions.

And now, time for a break

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Are We Close to Being Done ?

“Now this is not the end.

It is not even the beginning of the end.

But it is perhaps, the end of the beginning”.

Winston Churchill, 1942 (3 years into WW2)

http://www.globecartoon.com/neweconomy/10.html