STAT115Introduction to Computational

Biology and BioinformaticsSpring 2012

Jun Liu

&

Xiaole Shirley Liu

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Outline

• Course information

• Computational biology problems revolve around the Central Dogma of Molecular Biology

• Course structure (syllabus)

• Q&A

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STAT115 Lectures• Instructor:

– Jun Liu: 617-495-1600, jliu@stat.harvard.edu

– Xiaole Shirley Liu: 617-632-2472, xsliu@jimmy.harvard.edu

• Lecture: Tuesdays and Thursdays 11:30-1– NWB, B-108 (Cambridge); Kresge 213 (Boston)

– Selected lecture notes available online after lecture

• Office hours– J Liu: Tu 1-3 PM, SC 715

– XS Liu: Thu 2-4 PM, CLSB (3 Blackfan Circle) 11022, Boston

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STAT115 Labs and Web

• Teaching Fellows: – Alejandro Zarat: aquiroz@hsph.harvard.edu– Daniel Fernandes: dfernan@gmail.com – Lab in Science Center FL 418D, Harvard Yard,

W 6-8 pm (google map link in the course syllabus).

• Course website: www.stat115.com

• Lecture notes (also in the course website): http://CompBio.pbwiki.com

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STAT115 Recommended Texts

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STAT115 Recommended Texts

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STAT115 Grading

• Homework: 80 pts– 6 HW, 14*5+10=80 pts each

– Problems to be solved by hand, running some software online to obtain results, and some coding (python and R)

– 6 total late days, <= 3 days for a single HW

• Quiz at selected lectures 2*10=20 pts– 10 highest normalized scores, 2 pts each

– All short answers, true/false, multiple choice

Genome and gene

Entity Definition Molecular Mechanisms

Genome Unit of information transmission

DNA replication

Gene Unit of information expression

Transcription to RNA Translation to protein

Nucleic acid and proteins

Macromolecule Backbone Repeating unit Length Role

Nucleic acid

DNA

Phosphodiester bonds

Deoxyribonucleotides (A, C, G, T)

103-108 Genome

RNA

Phosphodiester bonds

Ribonucleotides (A, C, G, U)

103-105 103-104 102-103

Genome Messenger Gene product

Protein Peptide bonds Amino acids (A, C, D, E, F, G, H, I, K, L, M, N, P, Q, R, S, T, V, W, Y)

102-103 Gene product

1 cctcttttcc gtggcgcctc ggaggcgttc agctgcttca agatgaagct gaacatctcc 61 ttcccagcca ctggctgcca gaaactcatt gaagtggacg atgaacgcaa acttcgtact 121 ttctatgaga agcgtatggc cacagaagtt gctgctgacg ctctgggtga agaatggaag 181 ggttatgtgg tccgaatcag tggtgggaac gacaaacaag gtttccccat gaagcagggt 241 gtcttgaccc atggccgtgt ccgcctgcta ctgagtaagg ggcattcctg ttacagacca 301 aggagaactg gagaaagaaa gagaaaatca gttcgtggtt gcattgtgga tgcaaatctg 361 agcgttctca acttggttat tgtaaaaaaa ggagagaagg atattcctgg actgactgat 421 actacagtgc ctcgccgcct gggccccaaa agagctagca gaatccgcaa acttttcaat 481 ctctctaaag aagatgatgt ccgccagtat gttgtaagaa agcccttaaa taaagaaggt 541 aagaaaccta ggaccaaagc acccaagatt cagcgtcttg ttactccacg tgtcctgcag 601 cacaaacggc ggcgtattgc tctgaagaag cagcgtacca agaaaaataa agaagaggct 661 gcagaatatg ctaaactttt ggccaagaga atgaaggagg ctaaggagaa gcgccaggaa 721 caaattgcga agagacgcag actttcctct ctgcgagctt ctacttctaa gtctgaatcc 781 agtcagaaat aagatttttt gagtaacaaa taaataagat cagactctg

RPS6 (ribosomal protein S6) gene

The information in a gene is encoded by its DNA sequence

1 mklnisfpat gcqklievdd erklrtfyek rmatevaada lgeewkgyvv risggndkqg 61 fpmkqgvlth grvrlllskg hscyrprrtg erkrksvrgc ivdanlsvln lvivkkgekd 121 ipgltdttvp rrlgpkrasr irklfnlske ddvrqyvvrk plnkegkkpr tkapkiqrlv 181 tprvlqhkrr rialkkqrtk knkeeaaeya kllakrmkea kekrqeqiak rrrlsslras 241 tsksessqk

RPS6 (ribosomal protein S6) protein sequence:

The structure of a protein is encoded by its amino acids sequence

Nucleotide codes

A Adenine W Weak (A or T)

G Guanine S Strong (G or C)

C Cytosine M Amino (A or C)

T Thymine K Keto (G or T)

U Uracil B Not A (G or C or T)

R Purine (A or G) H Not G (A or C or T)

Y Pyrimidine (C or T) D Not C (A or G or T)

N Any nucleotide V Not T (A or G or C)

The Four Nucleosides of DNA

dA dG dC dT

A nucleoside is a sugar, here deoxyribose, plus a base

dA = deoxyadenosine, etc.

PYRIMIDINESPURINES

DNA is built from nucleotides

Structure of DNA:Double helix

Base Pairing

A nucleotide is a phospate, a sugar, and a purine or a pyramidine base.

The monomeric units of nucleic acids are called nucleotides.

Amino acid codes

Ala Arg Asn Asp Cys Gln Glu Gly His Ile Leu Lys Met Phe Pro Ser Thr Trp Tyr Val Asx Glx Sec Unk

A R N D C Q E G H I L K M F P S T W Y V B Z U X

Alanine Arginine Asparagine Aspartic acid Cysteine Glutamine Glutamic acid Glycine Histidine Isoleucine Leucine Lysine Methionine Phenylalanine Proline Serine Threonine Tryptophan Tyrosine Valine Asn or Asp Gln or Glu Selenocysteine Unknown

Protein are built from amino acids

http://web.mit.edu/esgbio/www/lm/proteins/peptidebond.html

The diversity of protein structure

Anfinsen 1961 ribonuclease re-naturing experiments: Sequence determines structure

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Central Dogma of Molecular Biology

DNA replication

DNA

RNA

Transcription

Physiology

Folded withfunction

Protein

Translation

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Central Dogma of Molecular Biology• DNA RNA Protein

• Genome sequencing, assembly and annotation– Sequence alignment (pairwise & multiple)– Gene prediction

• Genome variation:– Single base difference (SNP) and big copy

number duplication / deletions– Association studies

• Comparative genomics and phylogenies

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Case Study IThe Human Genome Race

• Human Genome Project: 1990-2003– Originally 1990-2005– Boosted by technology improvement

(automation improved throughput and quality with reduced cost)

– Competition from Celera

• Informatics essential for both the public and private sequencing efforts– Sequence assembly and gene prediction– Working draft finished simultaneously spring

2000

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Competing Sequencing Strategies• Clone-by-clone and whole-genome shotgun

Retail DNA Test

• TIME's Best Inventions (2008)

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“Your genome used to be a closed book. Now a simple, affordable (399 USD) test can shed new light on everything from your intelligence to your biggest health risks. Say hello to your dna — if you dare” -- time.com

“Your genome used to be a closed book. Now a simple, affordable (399 USD) test can shed new light on everything from your intelligence to your biggest health risks. Say hello to your dna — if you dare” -- time.com

1000 Genome Project

• Sequencing the genomes of at least a thousand people from around the world to create the most detailed and medically useful picture to date of human genetic variation

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Central Dogma of Molecular Biology

• DNA RNA Protein • RNA structure prediction• Differential gene expression:

– Gene expression microarray and analysis, normalization, clustering, gene ontology and classification

• Transcription regulation– Transcription factor motif finding, epigenetic

regulation, transcription regulatory network

• Post-transcriptional regulation: mi/siRNA

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Case Study IICancer Classifications Using Microarrays

• Microarray contains hundreds to millions of tiny probes

• Simultaneously detect how much each gene is “on”

• Cancer type classification – AML: acute myeloid leukemia

– ALL: acute lymphoblastic leukemia

– Check multiple samples of each type on microarrays

– Find good gene markers

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ALL vs AML

• Golub et al, Science 1999.

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ALL vs AML

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Central Dogma of Molecular Biology

• DNA RNA Protein

• Protein sequence motifs

• Protein structure prediction

• Mass spectrometry proteomics

• Protein interaction networks

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Case Study IIIIs Tamiflu for you?

• Roche’s Oseltamivir (Tamiflu) is the only available orally application drug for avian influenza (bird flu)

• 75 pediatric severe adverse events– Fatalities, neuropsychiatric, and skin

– 69 in Japan

• Inhibit neuraminidase of flu – The structure of its active site is homologous

to human sialidases (HsNEU2)

– An Asian-specific SNP (~10%) changes R41 to Q

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Is Tamiflu for you?

• Tamiflu binds to R41Q much stronger– Molecular simulations

– Decreased sialidase activity severe side effect

– Li et al, Cell Res, 2007

Study of HIV drug resistance

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Protease Inhibitors (PIs) target HIV-1 protease enzyme which is responsible for the posttranslational processing of the viral gag- and gag-pol-encoded poly proteins to yield the structural proteins and enzymes of the virus.

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Data: can we detect drug resistance mutations?

• Protease sequences from treated patients (949 cases)VVTIRIGGQLKEALLDTGAD

IVTIRIGGQLKEALLDTGAD

RVTIRIGGQLREALLDTGAD

• Sequences from untreated patients (4146 controls)LVTIRIGGQLREALLDTGAD

IVTIRIGGQLKEALLDTGAD

LVTIRIGGQLKEALLDTGAD

Which ones contributes to drug resistance?

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Drug resistance mutations

• The IAS-USA Drug Resistance Mutations list in HIV-1 updated in Fall 2006

• For IDV, mutations on the list are 10, 20, 24, 32, 36, 46, 54, 71, 73, 77, 82, 84, 90

• The ones we detect

10, 24, 32, 46, 54, 71, 73, 82, 90

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InteractionsWhat is known:

The occurrence of changes at L10, L24, M46, I54, A71, V82, I84, L90 was highly significantly correlated with phenotypic resistance.

Minor mutations influence drug resistance only in combination with other mutations.

73 + 90, 32+47, 84+90, 46+54+82, 88+90, Our results are consistent with above.The story about the mutation combination {46,54,82}

Conditional independence: 46 – 82 – 54. Single mutation at 54 has no effect V82A mutation is the key – without it others have small effect

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Zhang et al. (2010, PNAS)

Human genome sequencing

• Human genome project: 13 years (1990-2003), $3 billions, 6 countries, thousands of researchers and technicians

• 2011: 4 genomes in 8 days, costing $3000 each.

• In 2-3 years, each genome for 1-2 days, hundreds $, huge data

• Bioinformatics: turn data to knowledge

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Gene expression microarrays

• In the 90s, gene chip, $2000/sample

• 2011: chips for multiple copies of 1000 genes, $5-10/sample

• Using computational approach to infer gene expressions of ~20K genes from the observed expressions of the 1000 genes.

• Used for medical diagnosis, large scale drug target screening

Statistics?

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04/19/2343

Quotes

• True logic of this world is in the calculus of probabilities --- J. C. Maxwell

• What we see is the solution to a computational problem, our brains compute the most likely causes from the photon absorptions within our eyes --- H. Helmholtz

Beauty, Mathematics, Statistics, and Science

• Statistics: the only systematic way (that I know of) to connect mathematics with ordinary life activities

• Focus: studying and quantifying uncertainty; optimally extracting information; prediction

• Models: All models are wrong, but– Even those imperfect ones are very useful!

– Used as a powerful mathematical framework for organizing our thoughts and integrating information

• Mathematicians and physicists take care of the “beauty-only” part, and we take care of the rest

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Recent Success Stories

• Mapping disease genes – genetics and genomics

• Random walk, Markov, page rank and

• Jim Simons making many billions of $$$

• Compressive sensing, sparsity, random matrix and …

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Obama

Two schools of thoughts in statistics

• Bayesian: using probability distribution as a direct measure of uncertainty– Bayes Theorem:

• Frequentist: embedding the observed event in a sequence of “imaginary replications” – like a false positive false negative evaluation

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P B A P AP A B

P B

( | ) ( ) ( | ) ( )( | )

( ) ( | ) ( )

P Data P P Data PP Data

P Data P Data P d

( | ) ( )( | )

( )

P BloodP C HeartD P HeartDP HeartDisease BloodP C

P BloodP C

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Q&A

• Is this course for me?– Upper undergraduate and entry graduate

students interested in computational biology

• Do I have the background?– Biology knowledge is easy to accumulate– Statistics: basic stats tests, probability, some

linear algebra helps– Programming: prior programming helps

although good logic and willingness to learn and work for it are more important

Q&A

• STAT115 or STAT215?– STAT215 if: – You want to work on an exploratory research

problem (either from the professors or on your own)

– You have better coding skills

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All biology is becoming computational, much the same way it has became

molecular … Otherwise “low input, high throughput and no output science”

--- Sydney Brenner

2002 Nobel Prize