Computational Genomics

Irit Gat-Viks & Ron Shamir & Haim Wolfson Fall 2015-16

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What’s in class this week

• Motivation • Administrata • Some very basic biology • Some very basic biotechnology • Examples of our type of computational

problems

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• The information science of biology: organize, store, analyze, visualize biological data

• Responds to the explosion of biological data, and builds on the IT revolution

Bioinformatics

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Paradigm shift in biological research

Classical biology: focus on a single gene or sub-system. Hypothesis driven

Systems biology: measure (or model) the behavior of numerous parts of an entire biological system. Hypothesis generating

Large-scale data; Bioinformatics

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Personalized medicine

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Administration • ~5 home assignments as part of a home exam, to be done independently (50%) • Final exam (50%) • Must pass the Final to pass the course (TAU rules)

• Classes: Tue 12:15-13:30; Thu 14:45-16:00 • TA: Ron Zeira (Thu 16-17).

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Administration (cont.) • Web page of the course: http://www.cs.tau.ac.il/~rshamir/cg/15/

• Includes slides and full lecture scribes of previous years on each of the classes.

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Bibliography

• No single textbook covers the course :-( • See the full bibliography list in the

website (also for basic biology) • Key sources:

– Gusfield: Algorithms for strings, trees and sequences

– Durbin et al.: Biological sequence analysis – Pevzner: Computational molecular biology – Pevzner and Shamir (eds.): Bioinformatics for

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Lecture 1: Introduction

1. Basic biology 2. Basic biotechnology + some computational challenges arising along the way

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Slides prepared mainly by Ron Shamir and Adi Akavia

1. Basic Biology

•Touches on Chapters 1-8 in “The Cell” by Alberts et al.

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The Cell • Basic unit of life. • Carries complete characteristics of the species. • All cells store hereditary information in DNA. • All cells transform DNA to proteins, which are “the robots of the cell” and determine cell’s structure and function. • Two classes: eukaryotes (with nucleus) and prokaryotes (without).

http://regentsprep.org/Regents/biology/units/organization/cell.gif 13 CG © 2015

Nucleotide Chain Double helix

sugar

phosphate

Nucleotides/ Bases: Adenine (A), Guanine (G), Cytosine (C), Thymine (T).

Weak hydrogen bonds between base

pairs

Strong covalent bonds (phophodiester linkage) between sugars

Gregor Mendel laws of inheritance, “gene” 1866

Watson and Crick DNA structure 1953

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kidzsoft/src/rnadnatutor.html -5-10/98s/f2http://www.cs.utexas.edu/users/almstrum/s 15 CG © 2015

DNA (Deoxy-Ribonucleic acid) • Bases:

– Adenine (A) – Guanine (G) – Cytosine (C) – Thymine (T)

• Bonds: – G - C – A - T

• Oriented from 5’ to 3’. • Located in the cell nucleus

Purines

pyrimidines

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DNA and Chromosomes • DNA is packaged (105-fold)

• Chromatin: complex of DNA and proteins that pack it (histones)

• Chromosome: contiguous stretch of DNA

• Diploid: two homologous chromosomes, one from each parent

• Genome: totality of DNA material

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Replication

Replication fork

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Genes • Gene: a segment of DNA that specifies a protein. • The transformation of a gene into a protein is called

expression. • Genes are < 3% of human DNA • The rest - non-coding (used to be called “junk DNA”)

– RNA elements – Regulatory regions – Retrotransposons – Pseudogenes – and more…

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Gene Structure

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Gene Structure

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The Gene Finding Problem Given a DNA sequence, predict the location of genes (open reading frames) exons and introns. •A simple solution: seeking stop codons.

•6 ways of interpreting DNA sequence

• In most cases of eukaryotic DNA, a segment encodes only one gene.

•Difficulty in Eukaryotic DNA: introns & exons

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CG © Ron Shamir 2010 23

Proteins: The Cellular Machines

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Proteins • Build the cell and drive

most of its functions. • Polymers of amino-acids

(20 total), linked by peptide bonds.

• Oriented (from amino to carboxyl group).

• Fold into 3D structure of lowest energy.

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DNA RNA protein

transcription translation

The hard disk

One program

Its output

http://www.ornl.gov/hgmis/publicat/tko/index.htm

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RNA (Ribonucleic acid) • Bases:

– Adenine (A) – Guanine (G) – Cytosine (C) – Uracil (U); replaces T

• Oriented from 5’ (phosphate) to 3’ (sugar). • Single-stranded => flexible backbone =>

secondary structure => catalytic role.

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The RNA Folding Problem Given an RNA sequence, predict its (secondary structure) folding = the one that creates a maximum number of matched pairs

27 http://www.phys.ens.fr/~wiese/highlights/RNA-folding.html

GCCUUAAUGCACAUGGGCAAGCCCACGUAGCUAGUCGCGCGACACCAGUCCCAAAUAUGUUCACCCAACUCGCCUGACCGUCCCGCAGUAGCUAUACUACCGACUCCUACGCGGUUGAAACUAGACUUUUCUAGCGAGCUGUCAUAGGUAUGGUGCACUGUCUUUAAUUUUGUAUUGGGCCAGGCACGAAAGGCUUGGAAGUAAGGCCCCGCUUGACCCGAGAGGUGACAAUAGCGGCCAGGUGUAACGAUACGCGGGUGGCACGUACCCCAAACAAUUAAUCACACUGCCCGGGCUCACAUUAAUCAUGCCAUUCGUUGCCGAUCCGACCCAUAAGGAUGUGUAUGCCUCAUUCCCGGUCGGGGCGGCGACUGUUAACGCAUGAGAACUGAUUAGAUCUCGUGGUAGUGCUUGUCAAAUAGAAUGAGGCCAUUCCACAGACAUAGCGUUUCCCAUGAGCUAGGGGUCCCAUGUCCAGGUCCCCUAAAUAAAAGAGUCUCAC

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Transcription

http://www.iacr.bbsrc.ac.uk/notebook/courses/guide/words/transcriptiongif.htm

Template

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The Genetic Code

• Codon - a triplet of bases, codes a specific amino acid (except the stop codons)

• Stop codons - signal termination of the protein synthesis process

• Different codons may code the same amino acid

http://ntri.tamuk.edu/cell/ribosomes.html 29 CG © 2015

Translation

http://biology.kenyon.edu/courses/biol114/Chap05/Chapter05.html#Protein 30 CG © 2015

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DNA Protein

transcription translation

RNA

Expression and Regulation

Gene

Transcription factors (TFs) : proteins that control transcription by binding to specific DNA sequence motifs.

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Proteins: The Cellular Machines

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The Protein Folding Problem

•Given a sequence of amino acids, predict the 3D structure of the protein. •Motivation: functionality of protein is determined by its 3D structure. •Solution Approaches:

•Homology •Threading •de novo (=from scratch) 34 CG © 2015

The Human Genome: numbers • 23 pairs of chromosomes • ~3,200,000,000 bases • ~21,000 genes • Gene length: 1000-3000 bases,

spanning 30-40K bases

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Model Organisms

• Eukaryotes; increasing complexity • Easy to grow, manipulate.

Budding yeast • 1 cell • 6K genes

Nematode worm • 959 cells • 19K genes

Fruit fly • vertebrate-like • 14K genes

mouse • mammal • 30K genes

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• Lots of common ground with humans: many / most genes are common – but with mutations

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Sequence Alignment problems

Given two sequences, find their best alignment: Match with insertion/deletion of min cost. Same for best match of contiguous subeq. Same for several sequences “Workhorse” of Bioinformatics! Key challenge: huge volume of data (more on this later) 37 CG © 2015

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Introduction II: Basic Biotechnology and computational

challenges

Ron Shamir and Roded Sharan CG, Fall 2014-15

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Restriction Enzymes • Natural role: break foreign DNA

entering the cell. • Ability:

– Breaks the phosphodiester bonds of a DNA upon appearance of a certain cleavage (cut) sequence.

– Different sequence for each enzyme – Hundreds of different enzymes known.

• Digestion = application of restriction enzymes to a sequence. CG © 2015

Cloning vector (plasmids)

Foreign DNA

Recombinant DNA

Introduction into host cell

Use of antibiotics to grow recombinant cells

Cloning

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Denaturation

Annealing

Extension

Cycle 1

Cycle 2

Cycle 3

PCR

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CG © 2015 43 http://www.atdbio.com/content/20/Sequencing-forensic-analysis-and-genetic-analysis

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Gel Electrophoresis

• Use: “race” digested DNA fragments through electrically charged gel

• Goals: – Separate a mixture of DNA fragments – Measure length of DNA fragments

• How does it work: – smaller molecule travel faster than larger ones – same size and shape ⇒ the same movement

speed

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The Double Digest Problem Given 3 sets of distances {Xi} {Yi}

{Zi}, reconstruct cut sites A1<…<An and B1<…<Bm s.t. – {Ai-A i-1}={X}, {Bi-B i-1}={Y} – for C=A U B (ordered), {Ci-C I-1}={Z}

Complexity: NP hard, many

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The Partial Digest Problem •Problem: Given a (multi-) set of distances {|Xi-Xj|} 1 ≤ i ≤ j ≤ n, reconstruct the original series X1,…,Xn

•Complexity: unknown (yet)

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Sequencing • Sequencing: determining the sequence of

bases in a given DNA molecule. • Classical approach: gel electrophoresis • Basic idea: knowing the lengths of all prefixes

ending with letter X gives a partial seq • Creating DNA strands of different lengths :

catalyzing replication in environment with “terminator” A*.

• Repeat separately with C*, G*, T* • Abilities: reconstructs sequences of 500-1000

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• ---A-----A-

• -CC---CC—--

• T---T------

• -----G----G

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The Sequence Assembly Problem

• Given a set of sub- strings, find the shortest (super)string containing all the members of the set.

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Rearrangement

Rearrangement is a change in the order of complete segments along a chromosome.

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Genome Rearrangements

Challenges: •Reconstruct the evolutionary path of rearrangements •Shortest sequence of rearrangements between two permutations 53 CG © 2015

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More problems in sequencing data

Solve all the problems above (alignment, gene finding, rearrangements,…) on really huge datasets Need to handle practical problems of efficiency – time and space Need to overcome large noise (errors) due to data size

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DNA Microarrays

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Hybridization

• DNA double strands form by “gluing” of complementary single strands

• Complementarity rule: A-T, G-C

ACTCCG TGAGGC

| | | | | |

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Gene Expression Arrays • Assumption: transcription level indicates

gene’s importance in a specific condition.

Given the expression profiles of normal vs. disease: - Build an algorithm to predict if a new sample is normal or disease (classifier) - Cluster disease profiles into sub-classes - Cluster genes into functional groups

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Breast cancer treatment

Van’t veer et al., nature’02 59 CG © 2015

FIN

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Classifier construction Pa

tient

s

70-gene signature

No met.

Met.

• Classify to minimize incorrect assignments in the no met. class.

• 17/19 correct predictions on test set. 61 CG © 2015

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Human Variation • DNA of two human

beings is ~99.9% identical

• Phenotype and disease variation is due these 1/1000 mutations

Challenges: •Associate mutations to specific disease •Deal with huge datasets (noise and statistics)

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Challenges in network analysis:

identifying modules

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Complexity summary

• ~21,000 genes in the genome • Hard to identify • Harder to figure their function • Even harder to figure how they work together

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Promise and problems in comparative genomics (2009)

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arabidposis c. elegans h. sapiens s. cerevisiae

genes

arabidopsis 26207 0.19 0.24 0.42 c. elegans 19992 0.26 0.38 0.38 h. sapiens 21673 0.30 0.28 0.43 s. cerevisiae 5884 0.21 0.13 0.19

a(x,y)= fraction of genes in genome y that have strict orthologs in genome x Source: 10/2009 CG © 2015