1. CS262 Lecture 9, Win07, Batzoglou Gene Recognition

2. CS262 Lecture 9, Win07, Batzoglou Using Comparative Information

3. CS262 Lecture 9, Win07, Batzoglou Using Comparative Information Hox cluster is an example where everything is conserved

4. CS262 Lecture 9, Win07, Batzoglou Patterns of Conservation 30% 1.3% 0.14% 58% 14% 10.2% GenesIntergenic Mutations Gaps Frameshifts Separation 2-fold 10-fold 75-fold 

5. CS262 Lecture 9, Win07, Batzoglou Comparison-based Gene Finders Rosetta, 2000 CEM, 2000 –First methods to apply comparative genomics (human-mouse) to improve gene prediction Twinscan, 2001 –First HMM for comparative gene prediction in two genomes SLAM, 2002 –Generalized pair-HMM for simultaneous alignment and gene prediction in two genomes NSCAN, 2006 –Best method to-date based on a phylo-HMM for multiple genome gene prediction

6. CS262 Lecture 9, Win07, Batzoglou Twinscan 1.Align the two sequences (eg. from human and mouse) 2.Mark each human base as gap ( - ), mismatch ( : ), match ( | ) New “alphabet”: 4 x 3 = 12 letters  = { A-, A:, A|, C-, C:, C|, G-, G:, G|, U-, U:, U| } 3.Run Viterbi using emissions e k (b) where b  { A-, A:, A|, …, T| } Emission distributions e k (b) estimated from real genes from human/mouse e I (x|) < e E (x|): matches favored in exons e I (x-) > e E (x-): gaps (and mismatches) favored in introns Example Human : ACGGCGACGUGCACGU Mouse : ACUGUGACGUGCACUU Alignment : ||:|:|||||||||:|

7. CS262 Lecture 9, Win07, Batzoglou SLAM – Generalized Pair HMM d e Exon GPHMM 1.Choose exon lengths (d,e). 2.Generate alignment of length d+e.

8. CS262 Lecture 9, Win07, Batzoglou NSCAN—Multiple Species Gene Prediction GENSCAN TWINSCAN N-SCAN TargetGGTGAGGTGACCAAGAACGTGTTGACAGTA Conservation|||:||:||:|||||:||||||||...... sequence TargetGGTGAGGTGACCAAGAACGTGTTGACAGTA Conservation|||:||:||:|||||:||||||||...... sequence TargetGGTGAGGTGACCAAGAACGTGTTGACAGTA Informant1GGTCAGC___CCAAGAACGTGTAG...... Informant2GATCAGC___CCAAGAACGTGTAG...... Informant3GGTGAGCTGACCAAGATCGTGTTGACACAA TargetGGTGAGGTGACCAAGAACGTGTTGACAGTA Informant1GGTCAGC___CCAAGAACGTGTAG...... Informant2GATCAGC___CCAAGAACGTGTAG...... Informant3GGTGAGCTGACCAAGATCGTGTTGACACAA... Target sequence: Informant sequences (vector): Joint prediction (use phylo-HMM):

9. CS262 Lecture 9, Win07, Batzoglou NSCAN—Multiple Species Gene Prediction X X C C Y Y Z Z H H M M R R X X C C Y Y Z Z H H M M R R

10. CS262 Lecture 9, Win07, Batzoglou Performance Comparison GENSCAN Generalized HMM Models human sequence TWINSCAN Generalized HMM Models human/mouse alignments N-SCAN Phylo-HMM Models multiple sequence evolution GENSCAN Generalized HMM Models human sequence TWINSCAN Generalized HMM Models human/mouse alignments N-SCAN Phylo-HMM Models multiple sequence evolution NSCAN human/mouse > Human/multiple informants

11. CS262 Lecture 9, Win07, Batzoglou 2-level architecture No Phylo-HMM that models alignments CONTRAST Human tttcttagACTTTAAAGCTGTCAAGCCGTGTTCTAGATAAAATAAGTATTGGACAACTTGTTAGTCTCCTTTCCAACAACCTGAACAAATTTGATGAAgtatgtaccta Macaque tttcttagACTTTAAAGCTGTCAAGCCGTGTTCTAGATAAAATAAGTATTGGACAACTTGTTAGTCTCCTTTCCAACAACCTGAACAAATTTGATGAAgtatgtaccta Mouse ttgcttagACTTTAAAGTTGTCAAGCCGCGTTCTTGATAAAATAAGTATTGGACAACTTGTTAGTCTTCTTTCCAACAACCTGAACAAATTTGATGAAgtatgta-cca Rat ttgcttagACTTTAAAGTTGTCAAGCCGTGTTCTTGATAAAATAAGTATTGGACAACTTATTAGTCTTCTTTCCAACAACCTGAACAAATTTGATGAAgtatgtaccca Rabbit t--attagACTTTAAAGCTGTCAAGCCGTGTTCTAGATAAAATAAGTATTGGGCAACTTATTAGTCTCCTTTCCAACAACCTGAACAAATTTGATGAAgtatgtaccta Dog t-cattagACTTTAAAGCTGTCAAGCCGTGTTCTGGATAAAATAAGTATTGGACAACTTGTTAGTCTCCTTTCCAACAACCTGAACAAATTCGATGAAgtatgtaccta Cow t-cattagACTTTGAAGCTATCAAGCCGTGTTCTGGATAAAATAAGTATTGGACAACTTGTTAGTCTCCTTTCCAACAACCTGAACAAATTTGATGAAgtatgta-cta Armadillo gca--tagACCTTAAAACTGTCAAGCCGTGTTTTAGATAAAATAAGTATTGGACAACTTGTTAGTCTCCTTTCCAACAACCTGAACAAATTTGATGAAgtatgtgccta Elephant gct-ttagACTTTAAAACTGTCCAGCCGTGTTCTTGATAAAATAAGTATTGGACAACTTGTCAGTCTCCTTTCCAACAACCTGAACAAATTTGATGAAgtatgtatcta Tenrec tc-cttagACTTTAAAACTTTCGAGCCGGGTTCTAGATAAAATAAGTATTGGACAACTTGTTAGTCTCCTTTCCAACAACCTGAACAAATTTGATGAAgtatgtatcta Opossum ---tttagACCTTAAAACTGTCAAGCCGTGTTCTAGATAAAATAAGCACTGGACAGCTTATCAGTCTCCTTTCCAACAATCTGAACAAGTTTGATGAAgtatgtagctg Chicken ----ttagACCTTAAAACTGTCAAGCAAAGTTCTAGATAAAATAAGTACTGGACAATTGGTCAGCCTTCTTTCCAACAATCTGAACAAATTCGATGAGgtatgtt--tg SVM X Y abab

12. CS262 Lecture 9, Win07, Batzoglou CONTRAST

13. CS262 Lecture 9, Win07, Batzoglou log P(y | x) ~ w T F(x, y) F(x, y) =  i f(y i-1, y i, i, x) f(y i-1, y i, i, x):  1{y i-1 = INTRON, y i = EXON_FRAME_1}  1{y i-1 = EXON_FRAME_1, x human,i-2,…, x human,i+3 = ACCGGT)  1{y i-1 = EXON_FRAME_1, x human,i-1,…, x dog,i+1 = ACC, AGC)  (1-c)1{a<SVM_DONOR(i)<b}  (optional)1{EXON_FRAME_1, EST_EVIDENCE} CONTRAST - Features

14. CS262 Lecture 9, Win07, Batzoglou Accuracy increases as we add informants Diminishing returns after ~5 informants CONTRAST – SVM accuracies SNSP

15. CS262 Lecture 9, Win07, Batzoglou CONTRAST - Decoding Viterbi Decoding: maximize P(y | x) Maximum Expected Boundary Accuracy Decoding: maximize  i,B 1{y i-1, y i is exon boundary B} Accuracy(y i-1, y i, B | x) Accuracy(y i-1, y i, B | x) = P(y i-1, y i is B | x) – (1 – P(y i-1, y i is B | x))

16. CS262 Lecture 9, Win07, Batzoglou CONTRAST - Training Maximum Conditional Likelihood Training: maximize L(w) = P w (y | x) Maximum Expected Boundary Accuracy Training: Expected BoundaryAccuracy (w) =  i Accuracy i Accuracy i =  B 1{(y i-1, y i is exon boundary B} P w (y i-1, y i is B | x) -  B’ ≠ B P(y i-1, y i is exon boundary B’ | x)

17. CS262 Lecture 9, Win07, Batzoglou Performance Comparison De Novo EST-assisted Human Macaque Mouse Rat Rabbit Dog Cow Armadillo Elephant Tenrec Opossum Chicken Human Macaque Mouse Rat Rabbit Dog Cow Armadillo Elephant Tenrec Opossum Chicken

18. CS262 Lecture 9, Win07, Batzoglou Performance Comparison

19. CS262 Lecture 9, Win07, Batzoglou Gene Regulation and Microarrays

20. CS262 Lecture 9, Win07, Batzoglou Overview A. Gene Expression and Regulation B. Measuring Gene Expression: Microarrays C. Finding Regulatory Motifs

21. CS262 Lecture 9, Win07, Batzoglou Cells respond to environment Cell responds to environment— various external messages

22. CS262 Lecture 9, Win07, Batzoglou Genome is fixed – Cells are dynamic A genome is static  Every cell in our body has a copy of same genome A cell is dynamic  Responds to external conditions  Most cells follow a cell cycle of division Cells differentiate during development Gene expression varies according to:  Cell type  Cell cycle  External conditions  Location slide credits: M. Kellis

23. CS262 Lecture 9, Win07, Batzoglou Where gene regulation takes place Opening of chromatin Transcription Translation Protein stability Protein modifications

24. CS262 Lecture 9, Win07, Batzoglou Transcriptional Regulation Efficient place to regulate: No energy wasted making intermediate products However, slowest response time After a receptor notices a change: 1.Cascade message to nucleus 2.Open chromatin & bind transcription factors 3.Recruit RNA polymerase and transcribe 4.Splice mRNA and send to cytoplasm 5.Translate into protein

25. CS262 Lecture 9, Win07, Batzoglou Transcription Factors Binding to DNA Transcription regulation: Transcription factors bind DNA Binding recognizes DNA substrings: Regulatory motifs

26. CS262 Lecture 9, Win07, Batzoglou Promoter and Enhancers Promoter necessary to start transcription Enhancers can affect transcription from afar

27. CS262 Lecture 9, Win07, Batzoglou Transcription Factor (Protein) DNA Gene Regulation with TFs Regulatory Element Gene RNA polymerase

28. CS262 Lecture 9, Win07, Batzoglou Gene RNA polymerase Transcription Factor (Protein) Regulatory Element DNA Gene Regulation with TFs

29. CS262 Lecture 9, Win07, Batzoglou DNA New protein Gene Regulation with TFs Transcription Factor (Protein) Regulatory Element Gene RNA polymerase

30. TTATATTGAATTTTCAAAAATTCTTACTTTTTTTTTGGATGGACGCAAAGAAGTTTAATAATCATATTACATGGCATTACCACCATATA CATATCCATATCTAATCTTACTTATATGTTGTGGAAATGTAAAGAGCCCCATTATCTTAGCCTAAAAAAACCTTCTCTTTGGAACTTTC AGTAATACGCTTAACTGCTCATTGCTATATTGAAGTACGGATTAGAAGCCGCCGAGCGGGCGACAGCCCTCCGACGGAAGACTCTCCTC CGTGCGTCCTCGTCTTCACCGGTCGCGTTCCTGAAACGCAGATGTGCCTCGCGCCGCACTGCTCCGAACAATAAAGATTCTACAATACT AGCTTTTATGGTTATGAAGAGGAAAAATTGGCAGTAACCTGGCCCCACAAACCTTCAAATTAACGAATCAAATTAACAACCATAGGATG ATAATGCGATTAGTTTTTTAGCCTTATTTCTGGGGTAATTAATCAGCGAAGCGATGATTTTTGATCTATTAACAGATATATAAATGGAA AAGCTGCATAACCACTTTAACTAATACTTTCAACATTTTCAGTTTGTATTACTTCTTATTCAAATGTCATAAAAGTATCAACAAAAAAT TGTTAATATACCTCTATACTTTAACGTCAAGGAGAAAAAACTATAATGACTAAATCTCATTCAGAAGAAGTGATTGTACCTGAGTTCAA TTCTAGCGCAAAGGAATTACCAAGACCATTGGCCGAAAAGTGCCCGAGCATAATTAAGAAATTTATAAGCGCTTATGATGCTAAACCGG ATTTTGTTGCTAGATCGCCTGGTAGAGTCAATCTAATTGGTGAACATATTGATTATTGTGACTTCTCGGTTTTACCTTTAGCTATTGAT TTTGATATGCTTTGCGCCGTCAAAGTTTTGAACGATGAGATTTCAAGTCTTAAAGCTATATCAGAGGGCTAAGCATGTGTATTCTGAAT CTTTAAGAGTCTTGAAGGCTGTGAAATTAATGACTACAGCGAGCTTTACTGCCGACGAAGACTTTTTCAAGCAATTTGGTGCCTTGATG AACGAGTCTCAAGCTTCTTGCGATAAACTTTACGAATGTTCTTGTCCAGAGATTGACAAAATTTGTTCCATTGCTTTGTCAAATGGATC ATATGGTTCCCGTTTGACCGGAGCTGGCTGGGGTGGTTGTACTGTTCACTTGGTTCCAGGGGGCCCAAATGGCAACATAGAAAAGGTAA AAGAAGCCCTTGCCAATGAGTTCTACAAGGTCAAGTACCCTAAGATCACTGATGCTGAGCTAGAAAATGCTATCATCGTCTCTAAACCA GCATTGGGCAGCTGTCTATATGAATTAGTCAAGTATACTTCTTTTTTTTACTTTGTTCAGAACAACTTCTCATTTTTTTCTACTCATAA CTTTAGCATCACAAAATACGCAATAATAACGAGTAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGA TAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTT GGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAG...TTGCGAA GTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAA TGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGA TACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGT TCTTGGCAAGTTGCCAACTGACGAGATGCAGTTTCCTACGCATAATAAGAATAGGAGGGAATATCAAGCCAGACAATCTATCATTACAT TTAAGCGGCTCTTCAAAAAGATTGAACTCTCGCCAACTTATGGAATCTTCCAATGAGACCTTTGCGCCAAATAATGTGGATTTGGAAAA AGAGTATAAGTCATCTCAGAGTAATATAACTACCGAAGTTTATGAGGCATCGAGCTTTGAAGAAAAAGTAAGCTCAGAAAAACCTCAAT ACAGCTCATTCTGGAAGAAAATCTATTATGAATATGTGGTCGTTGACAAATCAATCTTGGGTGTTTCTATTCTGGATTCATTTATGTAC AACCAGGACTTGAAGCCCGTCGAAAAAGAAAGGCGGGTTTGGTCCTGGTACAATTATTGTTACTTCTGGCTTGCTGAATGTTTCAATAT CAACACTTGGCAAATTGCAGCTACAGGTCTACAACTGGGTCTAAATTGGTGGCAGTGTTGGATAACAATTTGGATTGGGTACGGTTTCG TTGGTGCTTTTGTTGTTTTGGCCTCTAGAGTTGGATCTGCTTATCATTTGTCATTCCCTATATCATCTAGAGCATCATTCGGTATTTTC TTCTCTTTATGGCCCGTTATTAACAGAGTCGTCATGGCCATCGTTTGGTATAGTGTCCAAGCTTATATTGCGGCAACTCCCGTATCATT AATGCTGAAATCTATCTTTGGAAAAGATTTACAATGATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGT TCTTGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATG TTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATA CCTATTCTTGACATGATATGACTACCATTTTGTTATTGTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATG TTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTA AGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGA TTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATA GTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATG CTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACT TAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGAT TGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAAT

31. TTATATTGAATTTTCAAAAATTCTTACTTTTTTTTTGGATGGACGCAAAGAAGTTTAATAATCATATTACATGGCATTACCACCATATA CATATCCATATCTAATCTTACTTATATGTTGTGGAAATGTAAAGAGCCCCATTATCTTAGCCTAAAAAAACCTTCTCTTTGGAACTTTC AGTAATACGCTTAACTGCTCATTGCTATATTGAAGTACGGATTAGAAGCCGCCGAGCGGGCGACAGCCCTCCGACGGAAGACTCTCCTC CGTGCGTCCTCGTCTTCACCGGTCGCGTTCCTGAAACGCAGATGTGCCTCGCGCCGCACTGCTCCGAACAATAAAGATTCTACAATACT AGCTTTTATGGTTATGAAGAGGAAAAATTGGCAGTAACCTGGCCCCACAAACCTTCAAATTAACGAATCAAATTAACAACCATAGGATG ATAATGCGATTAGTTTTTTAGCCTTATTTCTGGGGTAATTAATCAGCGAAGCGATGATTTTTGATCTATTAACAGATATATAAATGGAA AAGCTGCATAACCACTTTAACTAATACTTTCAACATTTTCAGTTTGTATTACTTCTTATTCAAATGTCATAAAAGTATCAACAAAAAAT TGTTAATATACCTCTATACTTTAACGTCAAGGAGAAAAAACTATAATGACTAAATCTCATTCAGAAGAAGTGATTGTACCTGAGTTCAA TTCTAGCGCAAAGGAATTACCAAGACCATTGGCCGAAAAGTGCCCGAGCATAATTAAGAAATTTATAAGCGCTTATGATGCTAAACCGG ATTTTGTTGCTAGATCGCCTGGTAGAGTCAATCTAATTGGTGAACATATTGATTATTGTGACTTCTCGGTTTTACCTTTAGCTATTGAT TTTGATATGCTTTGCGCCGTCAAAGTTTTGAACGATGAGATTTCAAGTCTTAAAGCTATATCAGAGGGCTAAGCATGTGTATTCTGAAT CTTTAAGAGTCTTGAAGGCTGTGAAATTAATGACTACAGCGAGCTTTACTGCCGACGAAGACTTTTTCAAGCAATTTGGTGCCTTGATG AACGAGTCTCAAGCTTCTTGCGATAAACTTTACGAATGTTCTTGTCCAGAGATTGACAAAATTTGTTCCATTGCTTTGTCAAATGGATC ATATGGTTCCCGTTTGACCGGAGCTGGCTGGGGTGGTTGTACTGTTCACTTGGTTCCAGGGGGCCCAAATGGCAACATAGAAAAGGTAA AAGAAGCCCTTGCCAATGAGTTCTACAAGGTCAAGTACCCTAAGATCACTGATGCTGAGCTAGAAAATGCTATCATCGTCTCTAAACCA GCATTGGGCAGCTGTCTATATGAATTAGTCAAGTATACTTCTTTTTTTTACTTTGTTCAGAACAACTTCTCATTTTTTTCTACTCATAA CTTTAGCATCACAAAATACGCAATAATAACGAGTAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGA TAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTT GGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAG...TTGCGAA GTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAA TGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGA TACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGT TCTTGGCAAGTTGCCAACTGACGAGATGCAGTTTCCTACGCATAATAAGAATAGGAGGGAATATCAAGCCAGACAATCTATCATTACAT TTAAGCGGCTCTTCAAAAAGATTGAACTCTCGCCAACTTATGGAATCTTCCAATGAGACCTTTGCGCCAAATAATGTGGATTTGGAAAA AGAGTATAAGTCATCTCAGAGTAATATAACTACCGAAGTTTATGAGGCATCGAGCTTTGAAGAAAAAGTAAGCTCAGAAAAACCTCAAT ACAGCTCATTCTGGAAGAAAATCTATTATGAATATGTGGTCGTTGACAAATCAATCTTGGGTGTTTCTATTCTGGATTCATTTATGTAC AACCAGGACTTGAAGCCCGTCGAAAAAGAAAGGCGGGTTTGGTCCTGGTACAATTATTGTTACTTCTGGCTTGCTGAATGTTTCAATAT CAACACTTGGCAAATTGCAGCTACAGGTCTACAACTGGGTCTAAATTGGTGGCAGTGTTGGATAACAATTTGGATTGGGTACGGTTTCG TTGGTGCTTTTGTTGTTTTGGCCTCTAGAGTTGGATCTGCTTATCATTTGTCATTCCCTATATCATCTAGAGCATCATTCGGTATTTTC TTCTCTTTATGGCCCGTTATTAACAGAGTCGTCATGGCCATCGTTTGGTATAGTGTCCAAGCTTATATTGCGGCAACTCCCGTATCATT AATGCTGAAATCTATCTTTGGAAAAGATTTACAATGATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGT TCTTGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATG TTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATA CCTATTCTTGACATGATATGACTACCATTTTGTTATTGTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATG TTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTA AGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGA TTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATA GTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATG CTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACT TAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGAT TGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATTT Promoter motifs 3’ UTR motifsExons Introns

32. CS262 Lecture 9, Win07, Batzoglou Example: A Human heat shock protein TATA box: positioning transcription start TATA, CCAAT: constitutive transcription GRE: glucocorticoid response MRE:metal response HSE:heat shock element TATASP1 CCAAT AP2 HSE AP2CCAAT SP1 promoter of heat shock hsp70 0 --158 GENE

33. CS262 Lecture 9, Win07, Batzoglou DNA Microarrays Measuring gene transcription in a high- throughput fashion

34. CS262 Lecture 9, Win07, Batzoglou What is a microarray

35. CS262 Lecture 9, Win07, Batzoglou What is a microarray A 2D array of DNA sequences from thousands of genes Each spot has many copies of same gene Measure number of hybridizations per spot Result: Thousands of “experiments” – one per gene – in one go Perform many microarrays for different conditions:  Time during cell cycle  Temperature  Nutrient level

36. CS262 Lecture 9, Win07, Batzoglou Goal of Microarray Experiments Measure level of gene expression across many different conditions:  Expression Matrix M: {genes}  {conditions}: M ij = |gene i | in condition j Group genes into coregulated sets  Observe cells under different conditions  Find genes with similar expression profiles Potentially regulated by same TF slide credits: M. Kellis

37. CS262 Lecture 9, Win07, Batzoglou Clustering vs. Classification Clustering  Idea: Groups of genes that share similar function have similar expression patterns Hierarchical clustering k-means Bayesian approaches Projection techniques Principal Component Analysis Independent Component Analysis Classification  Idea: A cell can be in one of several states (Diseased vs. Healthy, Cancer X vs. Cancer Y vs. Normal)  Can we train an algorithm to use the gene expression patterns to determine which state a cell is in? Support Vector Machines Decision Trees Neural Networks K-Nearest Neighbors

38. CS262 Lecture 9, Win07, Batzoglou Clustering Algorithms b e d f a c h g abdefghc K-means b e d f a c h g c1 c2 c3 abghcdef Hierarchical slide credits: M. Kellis

39. CS262 Lecture 9, Win07, Batzoglou Hierarchical clustering Bottom-up algorithm:  Initialization: each point in a separate cluster At each step:  Choose the pair of closest clusters  Merge The exact behavior of the algorithm depends on how we define the distance CD(X,Y) between clusters X and Y Avoids the problem of specifying the number of clusters b e d f a c h g slide credits: M. Kellis

40. CS262 Lecture 9, Win07, Batzoglou Results of Clustering Gene Expression CLUSTER is simple and easy to use De facto standard for microarray analysis Time: O(N 2 M) N: #genes M: #conditions

41. CS262 Lecture 9, Win07, Batzoglou K-Means Clustering Algorithm Each cluster X i has a center c i Define the clustering cost criterion COST(X 1,…X k ) = ∑ Xi ∑ x  Xi |x – c i | 2 Algorithm tries to find clusters X 1 …X k and centers c 1 …c k that minimize COST K-means algorithm:  Initialize centers  Repeat: Compute best clusters for given centers → Attach each point to the closest center Compute best centers for given clusters → Choose the centroid of points in cluster  Until the changes in COST are “small” b e d f a c h g c1 c2 c3 slide credits: M. Kellis

42. CS262 Lecture 9, Win07, Batzoglou K-Means Algorithm Randomly Initialize Clusters

43. CS262 Lecture 9, Win07, Batzoglou K-Means Algorithm Assign data points to nearest clusters

44. CS262 Lecture 9, Win07, Batzoglou K-Means Algorithm Recalculate Clusters

45. CS262 Lecture 9, Win07, Batzoglou K-Means Algorithm Recalculate Clusters

46. CS262 Lecture 9, Win07, Batzoglou K-Means Algorithm Repeat

47. CS262 Lecture 9, Win07, Batzoglou K-Means Algorithm Repeat

48. CS262 Lecture 9, Win07, Batzoglou K-Means Algorithm Repeat … until convergence Time: O(KNM) per iteration N: #genes M: #conditions

49. CS262 Lecture 9, Win07, Batzoglou Mixture of Gaussians – Probabilistic K-means Data is modeled as mixture of K Gaussians  N(  1,  2 I), …, N(  K,  2 I)  Prior probabilities  1, …,  K Different  i for every Gaussian i, or even different covariance matrices are possible, but learning becomes harder  P(x) = ∑ i P(x | N(  1,  2 I))   i  Use EM to learn parameters

50. CS262 Lecture 9, Win07, Batzoglou Analysis of Clustering Data Statistical Significance of Clusters  Gene Ontologyhttp://www.geneontology.org/http://www.geneontology.org/  KEGG http://www.genome.jp/kegg/http://www.genome.jp/kegg/ Regulatory motifs responsible for common expression Regulatory Networks Experimental Verification

51. CS262 Lecture 9, Win07, Batzoglou Evaluating clusters – Hypergeometric Distribution +–N genes, p labeled +, (N-p) – +Cluster: k genes, m labeled + +P-value of single cluster containing k genes of which at least r are + +– Prob a random set of k genes has m + and k-m – genes + P-value that at least r genes are + in the cluster slide credits: M. Kellis