Bioinformatics for Computer Scientists (Part 4 - Applications) Sepp Hochreiter. Institute of Bioinformatics Johannes Kepler University, Linz, Austria

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1 Bioinformatics for Computer Scientists (Part 4 - Applications) Institute of Bioinformatics Johannes Kepler University, Linz, Austria

2 Overview 4.1 Micorarray Technique Preprocessing 4.2 Gene Expression Data Classification 4.3 Prediction of Nucleosome Positions Regression 4.4 Copy Number Variations Preprocessing, Classification 4.5 Other: Single Nucleotide Polymorphisms (SNPs), alternative splicing, systems biology

3 4.1 Microarray Technique mrna concentration (expression level) ~ activity of a gene microarrays measure simultaneously cellular concentrations of thousands of mrna: gene expression profile basis for the functional genome analysis, molecular diagnostics, systems biology

4 4.1 Microarray Technique Affymetrix GeneChip

5 4.1 Microarray Technique Affymetrix Fluidics station Wash / Stain Affymetrix Scanner

6 4.1 Microarray Technique Affymetrix Technique

7 4.1 Microarray Technique 5 mrna reference sequence 3 probe probeset 5 mrna reference sequence TGTGATGGTGGGAATGGGTCAGAAGGACTCCTATGTGGGTGACGAGGCC TTACCCAGTCTTCCTGAGGATACAC perfect match TTACCCAGTCTTGCTGAGGATACAC mismatch 3 Fluorescence intensity image probe probeset Perfect match reporters Mismatch reporters

8 4.1 Microarray Technique Image processing

9 4.1 Microarray Technique Noise originates from: chip fabrication scanning deviations hybridization efficiency background intensity temperature fluctuations pipette errors non-uniform target labelling RNA extraction reverse transcription biological variations (tissue samples vary in their RNA content)

10 4.1 Microarray Technique Our new approach based on factor analysis: FARMS (Factor Analysis for Robust Microarray Summarization) factor z loading matrix observations additive noise λ 1 λ 2 λ 3 λ 4 λ 5 λ 6 λ 7 λ 8 λ 9 x 1 x 2 x 3 x 4 x 5 x 6 x 7 x 8 x 9 ε 1 ε 2 ε 3 ε 4 ε 5 ε 6 ε 7 ε 8 ε 9 z = variation in mrna concentration λ i = sensitivity of log-pm i ε i = measurement noise for log-pm i x i = observed log-pm i

11 4.2 Gene Expression Data Given Tissue samples (Tumor, Blood, ) Therapy outcome, clinical features, tumor type or state Goals: Diagnosis (tumor type) Prognosis of the therapy outcome (alternative therapy, medication) Identification of relevant genes (drug design) Working hypothesis Gene expression profile cell state tumor state prediction Prediction: Support Vector Machine and Feature Selection

12 4.2 Gene Expression Data Linear SVM Error bounds for the future error F 1 ( γ L ) F has upper bound proportional to n L and Expected F has upper bound proportional to n 1 L and γ (L trainings examples, n-dimensional space, margin γ) 2 ( L) Margin: minimal distance of data points to class boundary Goals are Feature selection: minimal dimension n of the input space Maximizing the margin γ Support Vector Machine (quadratic optimization problem)

13 4.2 Gene Expression Data not linear separable class 1 class +1

14 4.2 Gene Expression Data with slack variables slack variable ξi class 1 class +1

15 4.2 Gene Expression Data data: (x i, y i), 1 i L n x R, y {+1, 1} i class 1 class +1 support vectors i optimal search separating hyperplane with w,x maximal + b = 0margin w,x + b = 1 w,x + b = 1 w linear classifier: f (x) = sign( w,x + b) w,b model selection: find best classifier, i.e. optimal w and b margin γ = w 2-1

16 4.2 Gene Expression Data non-linear support vector machine class 1 class +1 support vectors

17 4.2 Gene Expression Data Problem Many genes (several 10,000 features) but few samples (about 100 examples) SVM theory requires more examples (L) then features (n) for a low error bound on future data (determined by L / n ). Solution Feature selection: Selection of relevant genes and, therefore, decreasing the input dimension n Our approach: matrix of measurements is kernel matrix K S. Hochreiter, K. Obermayer Feature Selection and Classifikation on Matrix Data: From Large Margins To Small Covering Numbers Neural Information Processing Systems 15, 2002

18 4.2 Gene Expression Data class -1 class +1 feature objects support vectors hφ(x), ω (z)i = k(z,x) separating hyperplane given Zby feature objects feature selection ω (z) φ(x) X

19 4.2 Gene Expression Data Algorithm 1 min α + α > K > K α + α α +,α 2 y > K α + α + ² 1 > α + + α s.t. 0 α +, α C1 y is vector of labels K is kernel matrix 1 is vector of ones α = α + α w = Zα Fast optimization through a new sequential minimal optimization (SMO) technique: only box constraints! Classifier: f(x) = hw, xi + b = PX α j x, z j + b = j=1 PX α j K (x)j + b j=1

20 4.2 Gene Expression Data Task Brain tumor (medulloblastoma) patients respond differently to the therapy (chemo, radiation) Negative prognosis: alternative therapy or more intensive control Positive prognosis: reduced medication 60 patients and 7129 genes S. L. Pomeroy, P. Tamayo, M. Gaasenbeek, L. M. Sturla, M. Angelo, M. E. McLaughlin, J. Y. H. Kim, L. C. Goumnerova and P. M. Black, C. Lau, J. C. Allen, D. Zagzag, J. M. Olson, T. Curran, C. Wetmore, J. A. Biegel, T. Poggio, S. Mukherjee, R. Rifkin, A. Califano, G. Stolovitzky, D. N. Louis, J. P. Mesirov, E. S. Lander, T. R. Golub Prediction of central nervous system embryonal tumour outcome based on gene expression Nature 415(687): , 2002

21 4.2 Gene Expression Data Classification results Standard New method Method F Error Method Features Error TrkC (1 gene) 1 20 SVM 40 / 45 / 50 5 / 4 / 5 SVM 15 SVM 40 / 45 / 50 5 / 5 / 5 TrkC & SVM 14 P-SVM 40 / 45 / 50 4 / 4 / 5 KNN 8 13 KNN & SVM 12 Standard feature selection with signal-to-noise- and t -statistics NATURE New

22 4.2 Gene Expression Data Task Breast cancer: 70-80% of the patients do not need a treatment because metastasis does not appear Prediction of metastasis: therapy selection Alternative treatment or individual medication (toxicity) 78 patients and 25,000 genes L. J. van't Veer, H, Dai, M. J. van de Vijver, Y. D. He, A. A. M. Hart, M. Mao, H. L. Peterse, K. van der Kooy, M. J. Marton, A. T. Witteveen, G. J. Schreiber, R. M. Kerkhoven, C. Roberts, P. S. Linsley, R. Bernards, S. H. Friend Gene expression profiling predicts clinical outcome of breast cancer Nature 415: , 2002

23 4.2 Gene Expression Data Classification results Standard feature selection New method Method F Error ROC Test Method F Errpr ROC Test weighted voting SVM Standard feature selection with signal-to-noise-statistics % true positives ROC curves standard feature selection new methode (P-SVM) % false positives

24 4.2 Gene Expression Data selected genes active genes at positive outcome active genes at negative outcome selected genes selected genes failure success failure success treatment outcome

25 4.3 Prediction of Nucleosome Positions The problem DNA regions at nucleosomes gene regulation by accessibility The solution SVMs with string kernel LSTM

26 4.3 Prediction of Nucleosome Positions DNA Nucleosomes Chromosome Felsenfeld & Groudine, Nature (2003)

27 4.3 Prediction of Nucleosome Positions Segal et al., 2006 GC AA TT TA GC DNA positions which prefer nucleosomes AA TT TA GC AA TT TA AA TT TA 10 bp frequency of AA/TT/TA and of GC GC AA TT TA GC GC AA TT TA from a Markov model given the likelihood of nucleosome position AA TT TA GC Also used: SVMs

28 4.3 Prediction of Nucleosome Positions Isolation of natural nucleosome DNAs Digest unwrapped DNA Extract protected DNA Clone, sequence, analyze individuals

29 4.3 Prediction of Nucleosome Positions nucleosome signature in living yeast cells Fraction (AA/TT/TA) AA/TT/TA (fraction) Segal et al., Position on (bp) Position on nucleosome (bp) ~10 bp periodicity of AA/TT/TA and for GC (out of phase)

30 4.3 Prediction of Nucleosome Positions 0.4 Fraction AA/TT/TA Wang & Widom, Position in nucleosome (bp) Center alignment Location mixture model alignment

31 4.3 Prediction of Nucleosome Positions 0.36 AA/TT/TA (fraction) chicken + yeast merge AA/TT/TA (fraction) AA/TT/TA (fraction) Segal et al., 2006 Position on nucleosome (bp) Position on nucleosome (bp) chicken (in vivo) yeast (in vivo) Position on nucleosome (bp)

32 4.3 Prediction of Nucleosome Positions Mouse (in vitro) 0.29 AA/TT/TA (fraction) Random DNA (in vitro) AA/TT/TA (fraction) Chicken (in vivo) AA/TT/TA (fraction) AA/TT/TA (fraction) Yeast (in vitro) 0.16 AA/TT/TA (fraction) Yeast (in vivo) Position on nucleosome (bp) Segal et al., 2006

33 4.3 Prediction of Nucleosome Positions nucleosome occupancy adjacent to TATA box -- yeast promoters Average Nucleosome Occupancy Model Permuted Distance from Coding Start (bp) Semi-stable nucleosomes Stable nucleosome Semi-stable nucleosomes Fraction 0.1 TATA Box Segal et al., 2006

34 4.3 Prediction of Nucleosome Positions nucleosome organization near 5 ends of genes Segal et al., 2006 Fondufe-Mittendorf, Segal, & JW

35 4.3 Prediction of Nucleosome Positions 3D superstructures ~10 bp quantized linker DNA lengths nucleosome i+1 nucleosome i Widom, 1992 nucleosome i nucleosome i+1

36 4.3 Prediction of Nucleosome Positions stable nucleosomes are correlated (3D structure) Frequency Frequency distances histogram Stable nucleosomes (model) Stable nucleosomes (permuted) Correlation Correlation autocorrelation Distance between centers of proximal nucleosomes (bp) Center-center distance (bp) (bp) Correlation offset (bp) Segal et al., 2006

37 4.3 Prediction of Nucleosome Positions linker lengths in purified di-nucleosomes biochemically isolated Duration HMM Location mixture model Frequency Linker DNA length (bp) Linker DNA length (bp) Wang & Widom

38 4.3 Prediction of Nucleosome Positions Evolution of the nucleosome positioning code Sandman & Reeve, Curr. Op. Microbiol nucleosomes

39 4.3 Prediction of Nucleosome Positions Pattern: tetranucleotides Lowary & Widom, 1998 Tetranucleotide Actual # Occur. Expected # (actual-expected) std. dev. ctag ± taga ± tcta ± agag ± 8 4.6

40 4.4 Copy Number Variations Copy-number variant (CNV): 1 kb or larger (50 kb) DNA sequences variable copy-number compared to a reference insertions, deletions and duplication 2004 first publications: Sebat et al., Science Iafrate et al., Nature Genetics

41 4.4 Copy Number Variations Chromosome Normal A B C Deletion A C Insertion A B D C Inversion C B A Segmental duplication A B C A B C Copy-number variant A B B B B C

42 4.4 Copy Number Variations Cytogenetics: fluorescence marked images of chromosomes macroscopic DNA variations technique: fluorescence in situ hybridization (FISH) syndromes like Down, Edward, William, Prader-Willi, Angelman but also leukaemia like chronic myeloid, acute promyelocytic, acute lymphoblastic as well as cancers like follicular lymphoma. Chromosomes of a human male

43 4.4 Copy Number Variations

44 4.4 Copy Number Variations CNVs: comparative genome hybridization (CGH) two genomes (sample and reference) are hybridized to an array DNA is amplified through PCR or through cloning hybridized to complementary sequences (probes) Techniques: bacterial artificial chromosome (BAC) arrays (cloning) 50 to 300 kb sequences has low resolution (1 Mb), many false positives representational oligonucleotide microarray analysis (ROMA) low coverage Oligonucleotide SNP and CNV arrays: high resolution, cheap Sequencing: expensive (time, material, human resources)

45 4.4 Copy Number Variations Map of copynumber variations identified by representational oligonucleotide microarray analysis in Sebat et al. 2004

46 4.4 Copy Number Variations

47 4.4 Copy Number Variations

48 4.4 Copy Number Variations Copy number variation associated with cancer and tumor Zhao et al., Cancer Research, 2004 normal CN=2 tumor deletion amplification CN=0 CN=1 CN=2 CN=3 CN=4

49 4.4 Copy Number Variations

50 4.4 Copy Number Variations

51 4.4 Copy Number Variations

52 4.4 Copy Number Variations Gonzalez 2005: AIDS susceptibility in humans related to CCL3L1 copy-number Aitman 2006: glomerulonephritis in humans related to FCGR3 copy-number Frayling 2007: September 2007, earlier studies Scott, 2007 and Zeggini 2007 found 11 genomic regions that alter the risk of type 2 diabetes in the European population Yang 2007 and Fanciulli 2007: autoimmune diseases: systemic lupus erythematosus, microscopic polyangiitis, and Wegener's granulomatosis.

53 4.4 Copy Number Variations In March 2007: Jacobs et al. found that even formalin-fixed, paraffin-embedded (FFPE) tissue samples can reliably be analyzed for copy-number variants and for loss of heterozygosity with Affymetrix 500K SNP arrays. FFPE: quality is not as good as with snap-frozen tissues but sufficient enormous impact: hospitals have banks of stored FFPE samples clinical follow-ups are known studies are very expensive and take a lot of time

54 4.4 Copy Number Variations Approaches to CNV: supervised approach: classification on the HapMap data set: Rabbee et al 2006 clustering: Affymetrix white paper HMM linear model with Mahalanobis distance (RLMM group of Terry Speed)

55 4.5 Single Nucleotide Polymorphsm (SNP) The problem single differences (one base) on DNA has impact on metabolism find relevant SNPs: feature selection Relevance individual medicine diseases more likely (schizophrenia, alcohol dependence)

56 4.5 Single Nucleotide Polymorphism (SNP) Single DNA nucleotides differ at each human Small differences are inherited from both parents (except maternal mitochondrial DNA) Variation in the DNA at the same position in at least 1% of the population: single nucleotide polymorphism (SNP -- pronounced snip) SNPs occur all 100 to 300 base pairs Current research relate diseases to SNPs (schizophrenia or alcohol dependence).

57 4.5 Single Nucleotide Polymorphism (SNP) Mullighan et al., Nature, 2007: childhood acute lymphoblastic leukaemia (ALL) pinned down to PAX5 gene frequent deletions and loss-of-function mutations found by genomic copy number and SNP analysis by Affymetrix SNP arrays 192 ALL samples high density of probes possible to narrow down to PAX5

58 4.5 Alternative Splice Site Recognition The problem recognize alternative splicing The solution SVMs with string kernel (position specific) Rätsch LSTM

59 4.5 Systems Biology The problem modeling of biochemical dynamics simulating the cell detecting dependencies

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