#33 - Genomics 11/09/07 Required Reading BCB 444/544 (before lecture) Lecture 33 √ Mon Nov 5 - Lecture 31 Phylogenetics – Parsimony and ML • Chp 11 - pp 142 – 169 Genomics √ Wed Nov 7 - Lecture 32 Machine Learning Fri Nov 9 - Lecture 33 Functional and Comparative Genomics #33_Nov09 BCB 444/544 F07 ISU Dobbs#33 - Genomics • Chp 17 and Chp 18 11/09/07 1 Assignments & Announcements Fri Nov 9 - HW#6 BCB 444/544 F07 ISU Dobbs#33 - Genomics 11/09/07 2 Seminars this Week BCB List of URLs for Seminars related to Bioinformatics: (will be posted this weekend) http://www.bcb.iastate.edu/seminars/index.html HW#6 - More fun with Machine Learning!! • Nov 7 Wed - BBMB Seminar 4:10 in 1414 MBB • Sharon Roth Dent Due: Fri Nov 16 ( or sometime before Mon Nov 26) MD Anderson Cancer Center • Role of chromatin and chromatin modifying proteins in regulating gene expression • Nov 8 Thurs - BBMB Seminar 4:10 in 1414 MBB • Jianzhi George Zhang U. Michigan • Evolution of new functions for proteins • Nov 9 Fri - BCB Faculty Seminar 2:10 in 102 SciI • Amy Andreotti ISU • T cell signaling: insights from protein NMR spectroscopy BCB 444/544 F07 ISU Dobbs#33 - Genomics 11/09/07 3 Chp 11 – Phylogenetic Tree Construction Methods and Programs 11/09/07 4 11/09/07 6 Machine Learning SECTION IV MOLECULAR PHYLOGENETICS • What is learning? Xiong: Chp 11 Phylogenetic Tree Construction Methods and Programs • • • • BCB 444/544 F07 ISU Dobbs#33 - Genomics • What is machine learning? • Learning algorithms • Machine learning applied to bioinformatics and computational biology Distance-Based Methods Character-Based Methods Phylogenetic Tree Evaluation Phylogenetic Programs • Some slides adapted from Dr. Vasant Honavar and Dr. Byron Olson BCB 444/544 F07 ISU Dobbs#33 - Genomics BCB 444/544 Fall 07 Dobbs 11/09/07 5 BCB 444/544 F07 ISU Dobbs#33 - Genomics 1 #33 - Genomics 11/09/07 An Application: Predicting RNA Binding Sites in Proteins Examples of Machine Learning Algorithms • Problem: Given an amino acid sequence, classify each residue as RNA binding or non-RNA binding • Naïve Bayes (NB) • Bayes Theorem • Input to the classifier is a string of amino acid identities • Neural network (NN) or Artificial Neural Net (ANN) • Perceptrons • Output from the classifier is a class label, either binding or not • Support Vector Machine (SVM) • Kernel functions Lab - WEKA: Decision Trees (DT), NB, SVM BCB 444/544 F07 ISU Dobbs#33 - Genomics 11/09/07 7 Bayes Theorem Applied to RNA Binding Site Prediction P (binding | aa seq) = P (c = 0 | X = x ) = 11/09/07 8 Naïve Bayes for Binary Classification P (binding ) P (aa seq | binding ) P (aa seq) P (c = 1 | X = x ) = BCB 444/544 F07 ISU Dobbs#33 - Genomics Assign c = 1 if P(c = 1) P( X = x | c = 1) P( X = x) P (c = 1 | X = x ) "! P (c = 0 | X = x ) Otherwise, assign c = 0 P(c = 0) P( X = x | c = 0) P( X = x) BCB 444/544 F07 ISU Dobbs#33 - Genomics 11/09/07 9 BCB 444/544 F07 ISU Dobbs#33 - Genomics 11/09/07 10 Predicted vs Actual RNA Binding for Ribosomal protein L15 (PDB ID 1JJ2:K) Example: Is ARG 6 RNA-binding or not? Predicted Actual ARG 6 TSKKKRQRGSR p(X1 = T | c = 1) p(X2 = S | c = 1) … p(X1 = T | c = 0) p(X2 = S | c = 0) … BCB 444/544 F07 ISU Dobbs#33 - Genomics BCB 444/544 Fall 07 Dobbs ≥ θ 11/09/07 11 BCB 444/544 F07 ISU Dobbs#33 - Genomics 11/09/07 12 2 #33 - Genomics 11/09/07 Biological Neurons “Sum” Input Signals & Generate Output Signal Artificial Neural Networks (ANNs or NNs) • Neural networks - classify “input vectors” or “examples” into categories (2 or more) • They are loosely based on biological neurons • Some of most successful methods for predicting secondary structure are based on neural networks: • Neural networks are trained to recognize amino acid patterns corresponding to known secondary structure elements; these patterns are used to predict secondary structure type for aa sequences in proteins of unknown structure Dendrites receive inputs, Axon sends output Image from Christos Stergiou and Dimitrios Siganos http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html BCB 444/544 F07 ISU Dobbs#33 - Genomics 11/09/07 13 BCB 444/544 F07 ISU Dobbs#33 - Genomics Simple Neuron = “Perceptron” 11/09/07 14 The Perceptron Perceptron is “Simplest ANN” = feed-forward NN = linear classifier X1 X2 w1 w2 T N #1 S > T " !0 S < T S = ! X i Wi i =1 XN Input X wN Weights W Summation S Threshold T Output F Perceptron combines input vectors X1…N , compares “sum” S with a threshold T, and generates output class label: either 1 or 0 If weights W and threshold T are not known in advance, the perceptron must be trained. Ideally, perceptron is trained to return correct answer for all training examples, and perform well on test examples it has never seen. Image from Christos Stergiou and Dimitrios Siganos http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html BCB 444/544 F07 ISU Dobbs#33 - Genomics Training set must contain both classes of data (i.e.. with “1” and “0” output). 11/09/07 15 BCB 444/544 F07 ISU Dobbs#33 - Genomics 11/09/07 16 Training a Perceptron Perceptron “Sums” Inputs by Computing Dot Product S = X⋅W Find the weights W that minimize the error function E: • Input is a vector X; Weight is are another vector W • Perceptron Summation S computes the dot product, S = X⋅W • Perceptron Output F is a function of S: it is often discrete (1 or 0), in which case the function is a step function • For continuous output, a sigmoidal function is often used: P E = # ( F(X i •W ) " t(X i )) i=1 2 P: number of training examples Xi: training vectors F(W⋅X i ): output of perceptron t(Xi ) : target value for Xi 1 1 F(X ) = 1 + e! X ! 1/2 Use steepest descent: - compute gradient: 0 - update weight vector: 0 - iterate BCB 444/544 F07 ISU Dobbs#33 - Genomics BCB 444/544 Fall 07 Dobbs 11/09/07 17 & 'E 'E 'E 'E # ! (E = $$ , , ,..., 'wN !" % 'w1 'w2 'w3 Wnew = Wold " #!E BCB 444/544 F07 ISU Dobbs#33 - Genomics (ε: learning rate) 11/09/07 18 3 #33 - Genomics 11/09/07 Support Vector Machines - SVMs Artificial Neural Network (ANN) Artificial neural network • Set of perceptrons interconnected such that outputs of some units become inputs of other units • Many topologies are possible! • Can have multiple layers Neural networks are trained in same way perceptrons are trained, P by minimizing an error function: i i E = # ( PP(X ) " t(X )) 2 Image from http://en.wikipedia.org/wiki/Support_vector_machine i=1 BCB 444/544 F07 ISU Dobbs#33 - Genomics 11/09/07 19 BCB 444/544 F07 ISU Dobbs#33 - Genomics 11/09/07 20 11/09/07 22 ! SVM Finds Maximum-Margin Hyperplane Kernel “Trick” (i.e., hyperplane that provides maximum separation between two classes of instances in dataset) Image from http://en.wikipedia.org/wiki/Support_vector_machine BCB 444/544 F07 ISU Dobbs#33 - Genomics 11/09/07 21 Kernel Function BCB 444/544 F07 ISU Dobbs#33 - Genomics Take Home Messages • Must consider how to set up the learning problem (supervised or unsupervised, generative or discriminative, classification or regression, etc.) • Lots of algorithms out there • No algorithm performs best on all problems BCB 444/544 F07 ISU Dobbs#33 - Genomics BCB 444/544 Fall 07 Dobbs 11/09/07 23 BCB 444/544 F07 ISU Dobbs#33 - Genomics 11/09/07 24 4 #33 - Genomics 11/09/07 Genomics - for excellent overview lectures, see these posted by NHGRI & Pevsner: 1- Genomic sequencing 1- Genomic sequencing Mapping and Sequencing CTGA2005Lecture1.pdf Many thanks to: Eric Green, NHGRI Eric Green, NHGRI 2- Human genome project The Human Genome 2005-10-19_ch17.pdf Jonathan Pevsner, Kennedy Krieger Institute for the following slides extracted from his lecture on: Mapping and Sequencing 3- SNPs Studying Genetic Variation II: Computational Techniques Jim Mullikin, NHGRI TGA2005Lecture13.pdf CTGA2005Lecture1.pdf 4- Comparative Genomics Comparative Sequence Analysis Elliott Margulies, NHGRI CTGA2005Lecture8.pdf BCB 444/544 F07 ISU Dobbs#33 - Genomics 11/09/07 25 Genomic Sequencing - Brief Review E Green 2005 BCB 444/544 F07 ISU Dobbs#33 - Genomics 11/09/07 BCB 444/544 F07 ISU Dobbs#33 - Genomics BCB 444/544 Fall 07 Dobbs 11/09/07 11/09/07 26 Comparison of Sequenced Genome Sizes 27 Comparison of Genetic & Physical Maps E Green 2005 BCB 444/544 F07 ISU Dobbs#33 - Genomics E Green 2005 BCB 444/544 F07 ISU Dobbs#33 - Genomics 11/09/07 28 STSs: Provide common markers for "linking" genetic & physical maps 29 E Green 2005 BCB 444/544 F07 ISU Dobbs#33 - Genomics 11/09/07 30 5 #33 - Genomics 11/09/07 With complete genomes (now), why bother to generate physical maps? E Green 2005 BCB 444/544 F07 ISU Dobbs#33 - Genomics 11/09/07 31 Genomic sequencing requires assembly of sequences obtained from cloned DNA E Green 2005 BCB 444/544 F07 ISU Dobbs#33 - Genomics 11/09/07 32 NIH: "Hierarchical" BAC-by-BAC Sequencing Human Genome Sequencing Two approaches: • Public (government) - International Consortium (6 countries, NIH-funded in US) • "Hierarchical" cloning & BAC-by-BAC sequencing • Map-based assembly • Private (industry) - Celera (Craig Venter) • Whole genome random "shotgun" sequencing • Computational assembly (took advantage of public maps & sequences,too) Guess which human genome Celera sequenced? BCB 444/544 F07 ISU Dobbs#33 - Genomics 11/09/07 33 "Hierarchical" Subcloning Strategy E Green 2005 BCB 444/544 F07 ISU Dobbs#33 - Genomics BCB 444/544 Fall 07 Dobbs E Green 2005 BCB 444/544 F07 ISU Dobbs#33 - Genomics 11/09/07 34 Celera: Whole-Genome "Shotgun" Sequencing 11/09/07 35 E Green 2005 BCB 444/544 F07 ISU Dobbs#33 - Genomics 11/09/07 36 6 #33 - Genomics 11/09/07 Either Strategy: Sequence "Finishing" = Hardest part !! "Shotgun" Sequencing Stategy E Green 2005 BCB 444/544 F07 ISU Dobbs#33 - Genomics 11/09/07 37 Advances in DNA Sequencing Technology E Green 2005 BCB 444/544 F07 ISU Dobbs#33 - Genomics 11/09/07 E Green 2005 BCB 444/544 F07 ISU Dobbs#33 - Genomics 11/09/07 38 Sequencing Method #1: Gilbert-Maxim "Chemical Degradation" 39 Sequencing Method #2: Sanger "Di-deoxy Chain Termination" E Green 2005 BCB 444/544 F07 ISU Dobbs#33 - Genomics 11/09/07 40 Automated Sequencing for Genome Projects: Sanger method - with improvements Another “recent” improvement: rapid & high resolution separation of fragments in capillaries instead of gels (E Yeung,Ames Lab, ISU) E Green 2005 BCB 444/544 F07 ISU Dobbs#33 - Genomics BCB 444/544 Fall 07 Dobbs 11/09/07 41 E Green 2005 BCB 444/544 F07 ISU Dobbs#33 - Genomics 11/09/07 42 7 #33 - Genomics 11/09/07 1st Eukaryotic Genome Sequence: S. cerevisiae Recent technologies? Pyro- & 454 Sequencing BCB 444/544 F07 ISU Dobbs#33 - Genomics 11/09/07 43 1st Animal Genome Sequence: C. elegans E Green 2005 BCB 444/544 F07 ISU Dobbs#33 - Genomics BCB 444/544 F07 ISU Dobbs#33 - Genomics BCB 444/544 Fall 07 Dobbs BCB 444/544 F07 ISU Dobbs#33 - Genomics 11/09/07 44 Timetable for Human Genome Sequencing: Faster than expected! 11/09/07 45 1st Draft Human Genome: ”Complete" in 2001 E Green 2005 E Green 2005 E Green 2005 BCB 444/544 F07 ISU Dobbs#33 - Genomics 11/09/07 46 Public Sequencing - International Consortium 11/09/07 47 E Green 2005 BCB 444/544 F07 ISU Dobbs#33 - Genomics 11/09/07 48 8 #33 - Genomics 11/09/07 "Finishing" the Human Genome - continues… E Green 2005 BCB 444/544 F07 ISU Dobbs#33 - Genomics 11/09/07 49 BCB 444/544 F07 ISU Dobbs#33 - Genomics 11/09/07 E Green 2005 51 E Green 2005 BCB 444/544 F07 ISU Dobbs#33 - Genomics BCB 444/544 Fall 07 Dobbs 11/09/07 11/09/07 50 BCB 444/544 F07 ISU Dobbs#33 - Genomics 11/09/07 52 11/09/07 54 ENCODE Project Comparing Genomes: Functional Elements E Green 2005 BCB 444/544 F07 ISU Dobbs#33 - Genomics Comparative Genomics: now with complete genomic sequences Interpreting the Human Genome Sequence! E Green 2005 After "Complete" Human Genome Sequence What next? 53 E Green 2005 BCB 444/544 F07 ISU Dobbs#33 - Genomics 9 #33 - Genomics 11/09/07 ENCODE - Web Sites E Green 2005 BCB 444/544 F07 ISU Dobbs#33 - Genomics ENCODE - Results? June 2007 11/09/07 55 http://www.nature.com/nature/journal/v447/n7146/full/nature05874.html BCB 444/544 F07 ISU Dobbs#33 - Genomics 11/09/07 56 Eric Green's Genomic Sequencing Challenges (2005 List) E Green 2005 BCB 444/544 F07 ISU Dobbs#33 - Genomics BCB 444/544 Fall 07 Dobbs 11/09/07 57 10