Presentation is loading. Please wait.

Presentation is loading. Please wait.

Comparative Genomics & Annotation The Foundation of Comparative Genomics Non-Comparative Annotation Three methodological tasks of CG Annotation: Protein.

Similar presentations


Presentation on theme: "Comparative Genomics & Annotation The Foundation of Comparative Genomics Non-Comparative Annotation Three methodological tasks of CG Annotation: Protein."— Presentation transcript:

1 Comparative Genomics & Annotation The Foundation of Comparative Genomics Non-Comparative Annotation Three methodological tasks of CG Annotation: Protein Gene Finding RNA Structure Prediction Signal Finding Challenges Empirical Investigations: Genes & Signals Functional Stories Positive Selection Open Questions

2 Hidden Markov Models in Bioinformatics Definition Three Key Algorithms Summing over Unknown States Most Probable Unknown States Marginalizing Unknown States Key Bioinformatic Applications Pedigree Analysis Profile HMM Alignment Fast/Slowly Evolving States Statistical Alignment O 1 O 2 O 3 O 4 O 5 O 6 O 7 O 8 O 9 O 10 H1H1 H2H2 H3H3

3 Further Examples Simple Prokaryotic Simple Eukaryotic Gene Finding: Burge and Karlin, 1996 Isochore: Churchill,1989,92 Likelihood Recursions: Likelihood Initialisations: poor rich HMM: L p (C)=L p (G)=0.1, L p (A)=L p (T)=0.4, L r (C)=L r (G)=0.4, L r (A)=L r (T)=0.1

4 Further Examples Secondary Structure Elements: Goldman, 1996 Profile HMM Alignment: Krogh et al.,1994  L   L   LLL HMM for SSEs: Adding Evolution: SSE Prediction:

5 Grammars: Finite Set of Rules for Generating Strings Regular finished – no variables General (also erasing) Context Free Context Sensitive Ordinary letters: & Variables: i.A starting symbol: ii. A set of substitution rules applied to variables in the present string:

6 Simple String Generators Terminals (capital) --- Non-Terminals (small) i. Start with S S --> aT bS T --> aS bT  One sentence – odd # of a’s: S-> aT -> aaS –> aabS -> aabaT -> aaba ii.  S--> aSa bSb aa bb One sentence (even length palindromes): S--> aSa --> abSba --> abaaba

7 Stochastic Grammars The grammars above classify all string as belonging to the language or not. All variables has a finite set of substitution rules. Assigning probabilities to the use of each rule will assign probabilities to the strings in the language. S -> aSa -> abSba -> abaaba i. Start with S. S --> (0.3)aT (0.7)bS T --> (0.2)aS (0.4)bT (0.2)  If there is a 1-1 derivation (creation) of a string, the probability of a string can be obtained as the product probability of the applied rules. S -> aT -> aaS –> aabS -> aabaT -> aaba ii.  S--> (0.3)aSa (0.5)bSb (0.1)aa (0.1)bb *0.3 *0.2 *0.7 *0.3 *0.2 *0.5 *0.1

8 Finding Regulatory Signals in Genomes Regulatory signals know from molecular biology Different Kinds of Signals Promotors Enhancers Splicing Signals The Computational Problem Non-homologous/homologous sequences Known/unknown signal 1 common signal/complex signals/additional information Combinations  -globins in humans

9 Weight Matrices & Sequence Logos Wasserman and Sandelin (2004) ‘Applied Bioinformatics for the Identification of Regulatory Elements” Nature Review Genetics G A C C A A A T A A G G C A 2 G A C C A A A T A A G G C A 3 T G A C T A T A A A A G G A 4 T G A C T A T A A A A G G A 5 T G C C A A A A G T G G T C 6 C A A C T A T C T T G G G C 7 C A A C T A T C T T G G G C 8 C T C C T T A C A T G G G C Set of signal sequences: B R M C W A W H R W G G B M Consensus sequence: A C G T Position Frequency Matrix - PFM A C G T Position Weight Matrix - PWM T T G C A T A A G T A G T C Score for New Sequence Sequence Logo & Information content

10 Motifs in Biological Sequences 1990 Lawrence & Reilly “An Expectation Maximisation (EM) Algorithm for the identification and Characterization of Common Sites in Unaligned Biopolymer Sequences Proteins Cardon and Stormo Expectation Maximisation Algorithm for Identifying Protein-binding sites with variable lengths from Unaligned DNA Fragments L.Mol.Biol Lawrence… Liu “Detecting subtle sequence signals: a Gibbs sampling strategy for multiple alignment” Science 262, (R,l) 1 K A=(a 1,..,a K ) – positions of the windows Priors A has uniform prior  j has Dirichlet(N 0  ) prior –  base frequency in genome. N 0 is pseudocounts  =(  1,A,…,  w,T ) probability of different bases in the window  0 =(  A,..,  T ) – background frequencies of nucleotides.

11 The Gibbs Sampler For i=1,..,d: Draw x i (t+1) from conditional distribution  (.|x [-i] (t) ) and leave remaining components unchanged, i.e. x [-i] (t+1) = x [-i] (t) Both random & systematic scan algorithms leaves the true distribution invariant. The conditional distributions are then: The approximating distribution after t steps of a systematic GS will be: An example: Target Distribution is x2x2 x1x1

12 The Gibbs sampler Objective: Find conserved segment of length k in n unrelated sequences n 2 1 1k 1k 1k Gibbs iteration: Remove one at random - s j Form profile of remaining n-1 (q 1,..q k ) Let p i be the probability with which s j [i..i+k-1] fits profile. Including pseudo-counts. Choose to start replacement at i with probability proportional to p i From : Lawrence, C. et al.(1993) Detecting Subtle Sequence Signals: A Gibbs Sampler approach to Multiple Alignment. Science

13 The Gibbs sampler: example From : Lawrence, C. et al.(1993) Detecting Subtle Sequence Signals: A Gibbs Sampler approach to Multiple Alignment. Science Observed pattern in aligned sequences Pattern Probability Model Upper Points: original sequences Middle: original date minus pattern Lower: Shuffled sequences 3 independent runs a run on sequences without signal

14 Natural Extensions to Basic Model I Multiple Pattern Occurances in the same sequences: Liu, J. `The collapsed Gibbs sampler with applications to a gene regulation problem," Journal of the American Statistical Association width = w length n L akak Prior: any position i has a small probability p to start a binding site: Modified from Liu Composite Patterns: BioOptimizer: the Bayesian Scoring Function Approach to Motif Discovery Bioinformatics

15 Natural Extensions to Basic Model II Correlated in Nucleotide Occurrence in Motif: Modeling within-motif dependence for transcription factor binding site predictions. Bioinformatics, 6, Regulatory Modules: De novo cis-regulatory module elicitation for eukaryotic genomes. Proc Nat’l Acad Sci USA, 102, M1 M2 M3 Stop Start Gene A Gene B Insertion-Deletion BALSA: Bayesian algorithm for local sequence alignment Nucl. Acids Res., K w1w1 w2w2 w3w3 w4w4

16 Combining Signals and other Data Modified from Liu 1.Rank genes by E=log 2 (expression fold change) 2.Find “many” (hundreds) candidate motifs 3.For each motif pattern m, compute the vector S m of matching scores for genes with the pattern 4.Regress E on S m Expresssion and Motif Regression: Integrating Motif Discovery and Expression Analysis Proc.Natl.Acad.Sci Motifs Coding regions ChIP-on-chip kb information on protein/DNA interaction: An Algorithm for Finding Protein-DNA Interaction Sites with Applications to Chromatin Immunoprecipitation Microarray Experiments Nature Biotechnology, 20, Protein binding in neighborhood Coding regions

17 MEME- Multiple EM for Motif Elicitation n 2 1 1k 1k 1k 1k 1k 1k 3 Z i,j = 1 if a motif starts at j’th position in i’th sequence, otherwise 0. i j Motif nucleotide distribution: M[p,q], where p - position, q-nucleotide. Background distribution B[q], is probability that a Z i,j = 1 Find M,B,, Z that maximize Pr (X, Z | M, B, ) Expectation Maximization to find a local maximum Iteration t: Expectation-step: Z (t) = E (Z | X, (M, B, (t)  Maximization-step: Find (M, B, (t+1) that maximizesPr (X, Z (t) | (M, B, (t+1) ) Bailey, T. L. and C. Elkan (1994). "Fitting a mixture model by expectation maximization to discover motifs in biopolymers." Proc Int Conf Intell Syst Mol Biol 2:

18 Phylogenetic Footprinting (homologous detection) Blanchette and Tompa (2003) “FootPrinter: a program designed for phylogenetic footprinting” NAR Term originated in 1988 in Tagle et al. Blanchette et al.: For unaligned sequences related by phylogenetic tree, find all segments of length k with a history costing less than d. Motif loss an option. beginsignalend

19 The Basics of Footprinting I Many aligned sequences related by a known phylogeny: n 1 positions sequences k 1 slow - r s fast - r f HMM: Two un-aligned sequences: A A G C T ATG A-C HMM:

20 Many un-aligned sequences related by a known phylogeny: Conceptually simple, computationally hard Dependent on a single alignment/no measure of uncertainty sequences k 1 Alignment HMM acgtttgaaccgag---- Signal HMM Alignment HMM sequences k 1 acgtttgaaccgag---- Statistical Alignment and Footprinting. sequence s k 1 acgtttgaaccgag---- Solution: Cartesian Product of HMMs

21 “Structure” does not stem from an evolutionary model Structure HMM S F 0.1 S F F FF 0.9 F FSFS 0.1 S SS 0.9 S SFSF 0.1 The equilibrium annotation does not follow a Markov Chain: ? F F S S F Each alignment in from the Alignment HMM is annotated by the Structure HMM: using the HMM at the alignment will give other distributions on the leaves No ideal way of simulating: using the HMM at the root will give other distributions on the leaves Alignment HMM Structure HMM (A,S)(A,S)

22 (Homologous + Non-homologous) detection Wang and Stormo (2003) “Combining phylogenetic data with co-regulated genes to identify regulatory motifs” Bioinformatics gene promotor Unrelated genes - similar expressionRelated genes - similar expression Combine above approaches:Mixed genes - similar expression Combine “profiles”

23 Regulatory Signals in Humans Sourece: Transcriptional Regulatory Elements in the Human GenomeGlenn A. Maston, Sara K. Evans, Michael R. GreenAnnual Review of Genomics and Human Genetics. Volume 7, Sep 2006Glenn A. Maston Sara K. Evans Michael R. GreenAnnual Review of Genomics and Human Genetics Transcription Start Site - TSS Core Promoter - within 100 bp of TSS Proximal Promoter Elements - 1kb TSS Locus Control Region - LCR Insulator Silencer Enhancer Transcription in Eukaryotes is done by RNA Polymerase II DNA-binding proteins in the human genome.

24 Sourece: Transcriptional Regulatory Elements in the Human GenomeGlenn A. Maston, Sara K. Evans, Michael R. GreenAnnual Review of Genomics and Human Genetics. Volume 7, Sep 2006Glenn A. Maston Sara K. Evans Michael R. GreenAnnual Review of Genomics and Human Genetics Core Promoter Elements TATA - box Inhibitor element (Inr) Downstream Promoter Element (DPE) Downstream Core Element (DCE) TFIIB-recognition element (BRE) Motif Ten Element (MTE) Examples of Disease Mutations: Core promoter  -thallasemia TATA-box  -globin Enhancer X-linked deafness 900kb deletion POU3P4 Silencer Asthma 509bp mut TSS TFG-b Activator Prostate Cancer ATBF1 Coactivator Parkinson disease DJ-1 Chromatin Cancer BRG1/BRM

25  -globins Multispecies Conserved Sequences - MCSs Analyzed 238kb in 22 species Found 24 MCSs Programs use GUMBY - VISTA - MULTIPIPMAKER MULTILAGAN - CLUSTALW - DIALIGN TRANSFAC TRES - Experimental Knowledge of the region Hypersensitive sites (DHSs) DNA Methylation Region lies in CG rich, gene rich region close to the telomeres. It is not easy to align CG-islands.

26 Promoters in  -globins Sourece: Hughes et al.(2005) Annotation of cis-regulatory elements by identification, subclassification, and functional assessment of multispecies conserved sequences PNAS : ; vista illus. 5 MCSs Divergence relative to human 1.Promoters MCSs Regulatory MCSs Intronics MCSs Exonic MCSs Unknown - 3

27 Regulatory Protein-DNA Complexes Luscome et al.(2000) An overview of the structure of protein-DNA complexes Genome Biology Moses et al.(2003) “Position specific variation in the rate fo evolution of transcription binding sites” BMC Evolutionary Biology Databases with the 3-D structure of combined DNA -Protein Data bases with known promoters

28 Challenges Open Problems


Download ppt "Comparative Genomics & Annotation The Foundation of Comparative Genomics Non-Comparative Annotation Three methodological tasks of CG Annotation: Protein."

Similar presentations


Ads by Google