STAT115 Introduction to Computational Biology and Bioinformatics Spring 2012

1. STAT115Introduction to Computational Biology and BioinformaticsSpring 2012 Jun Liu & Xiaole Shirley Liu

2. Outline • Course information • Computational biology problems revolve around the Central Dogma of Molecular Biology • Course structure (syllabus) • Q&A STAT115

3. 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 STAT115

4. 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 STAT115

5. STAT115 Recommended Texts STAT115

6. STAT115 Recommended Texts STAT115

7. 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 STAT115

8. Genome and gene

9. Nucleic acid and proteins

10. The information in a gene is encoded by its DNA sequence RPS6 (ribosomal protein S6) gene 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

11. The structure of a protein is encoded by its amino acids sequence RPS6 (ribosomal protein S6) protein 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

12. Nucleotide codes

13. The Four Nucleosides of DNA DNA is built from nucleotides A nucleoside is a sugar, here deoxyribose, plus a base dA = deoxyadenosine, etc. dA dG dC dT PURINES PYRIMIDINES

14. Structure of DNA: Double helix

15. Base Pairing

16. The monomeric units of nucleic acids are called nucleotides. A nucleotide is a phospate, a sugar, and a purine or a pyramidine base.

17. Protein are built from amino acids Amino acid codes

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

20. The diversity of protein structure

21. Anfinsen 1961 ribonuclease re-naturing experiments: Sequence determines structure

22. DNA replication DNA Transcription RNA Translation Protein Folded with function Physiology Central Dogma of Molecular Biology STAT115

23. 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 STAT115

24. 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 STAT115

25. Competing Sequencing Strategies • Clone-by-clone and whole-genome shotgun STAT115

26. Retail DNA Test • TIME's Best Inventions (2008) “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

27. 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

28. 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 STAT115

29. 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 STAT115

30. ALL vs AML • Golub et al, Science 1999. STAT115

31. ALL vs AML STAT115

32. Central Dogma of Molecular Biology • DNA  RNA  Protein • Protein sequence motifs • Protein structure prediction • Mass spectrometry proteomics • Protein interaction networks STAT115

33. 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 STAT115

34. Is Tamiflu for you? • Tamiflu binds to R41Q much stronger • Molecular simulations • Decreased sialidase activity  severe side effect • Li et al, Cell Res, 2007 STAT115

35. Study of HIV drug resistance 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. STAT115

36. 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?

37. 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

38. Interactions • What 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

39. Zhang et al. (2010, PNAS)

40. Human genome sequencing • Human genome project: 13 years (1990-2003), $3 billions, 6 countries, thousands of researchers and technicians • 2011: 4genomes in 8 days, costing $3000 each. • In 2-3 years, each genome for 1-2 days, hundreds $, huge data • Bioinformatics: turn data to knowledge

41. Gene expression microarrays • In the 90s, gene chip, $2000/sample • 2011: chips for multiple copies of 1000genes, $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

42. Statistics?

43. 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

44. 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

45. 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 … Obama

46. 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

47. 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 STAT115

48. 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 STAT115

49. 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