#33 - Genomics 11/09/07

Transcription

1 BCB 444/544 Required Reading (before lecture) Lecture 33 Mon Nov 5 - Lecture 31 Phylogenetics Parsimony and ML Chp 11 - pp Genomics Wed Nov 7 - Lecture 32 Machine Learning Fri Nov 9 - Lecture 33 #33_Nov09 Functional and Comparative Genomics Chp 17 and Chp Assignments & Announcements Seminars this Week Fri Nov 9 - HW#6 (will be posted this weekend) BCB List of URLs for Seminars related to Bioinformatics: HW#6 - More fun with Machine Learning!! Due: Fri Nov 16 (or sometime before Mon Nov 26) Nov 7 Wed - BBMB Seminar 4:10 in 1414 MBB Sharon Roth Dent 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 3 4 Chp 11 Phylogenetic Tree Construction Methods and Programs Machine Learning SECTION IV MOLECULAR PHYLOGENETICS Xiong: Chp 11 Phylogenetic Tree Construction Methods and Programs Distance-Based Methods Character-Based Methods Phylogenetic Tree Evaluation Phylogenetic Programs What is learning? What is machine learning? Learning algorithms Machine learning applied to bioinformatics and computational biology Some slides adapted from Dr. Vasant Honavar and Dr. Byron Olson 5 6 BCB 444/544 Fall 07 Dobbs 1

2 Examples of Machine Learning Algorithms An Application: Predicting RNA Binding Sites in Proteins Naïve Bayes (NB) Bayes Theorem Neural network (NN) or Artificial Neural Net (ANN) Perceptrons Support Vector Machine (SVM) Kernel functions Problem: Given an amino acid sequence, classify each residue as RNA binding or non-rna binding Input to the classifier is a string of amino acid identities Output from the classifier is a class label, either binding or not Lab - WEKA: Decision Trees (DT), NB, SVM 7 8 Bayes Theorem Applied to RNA Binding Site Prediction Naïve Bayes for Binary Classification binding) aa seq binding) P ( binding aa seq) = aa seq) c = 1) X = x c = 1) c = 1 X = x) = X = x) c = 1 X = x) Assign c = 1 if "! c = 0 X = x) Otherwise, assign c = 0 c = 0) X = x c = 0) c = 0 X = x) = X = x) 9 10 Example: Is ARG 6 RNA-binding or not? Predicted vs Actual RNA Binding for Ribosomal protein L15 (PDB ID 1JJ2:K) Predicted Actual ARG 6 T S K K K R Q R G S R p(x 1 = T c = 1) p(x 2 = S c = 1) p(x 1 = T c = 0) p(x 2 = S c = 0) θ BCB 444/544 Fall 07 Dobbs 2

3 Artificial Neural Networks (ANNs or NNs) Biological Neurons Sum Input Signals & Generate Output Signal 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 Simple Neuron = Perceptron The Perceptron Perceptron is Simplest ANN = feed-forward NN = linear classifier X1 X2 XN w1 w2 wn N S = i= 1! X i W i T # 1 "! 0 S > T S < T Input X Weights W Summation S Threshold T Output F Perceptron combines input vectors X 1 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 Training set must contain both classes of data (i.e.. with 1 and 0 output) Perceptron Sums Inputs by Computing Dot Product S = X W Training a Perceptron Input is a vector X; Weight is are another vector W Perceptron Summation S computes the dot product, S = X W Find the weights W that minimize the error function E: 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 #( ) 2 E = F(X i W ) " t(x i ) i=1 P: number of training examples X i : training vectors F(W X i ): output of perceptron t(x i ) : target value for X i F ( X )! X 1 = 1 + e 0 1/2 0 1 Use steepest descent: - compute gradient: - update weight vector: - iterate & ' E ' E ' E ' E # ( E = $,,,...,! % ' w1 ' w2 ' w3 ' w N " Wnew = Wold " #! E (ε: learning rate) BCB 444/544 Fall 07 Dobbs 3

4 Artificial Neural Network (ANN) Support Vector Machines - SVMs 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, by minimizing an error function: P #( ) 2 E = PX i ) " t(x i ) i=1 Image from SVM Finds Maximum-Margin Hyperplane (i.e., hyperplane that provides maximum separation between two classes of instances in dataset) Kernel Trick Image from Kernel Function 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 Fall 07 Dobbs 4

5 Genomics - for excellent overview lectures, see these posted by NHGRI & Pevsner: 1- Genomic sequencing Mapping and Sequencing CTGA2005Lecture1.pdf Eric Green, NHGRI 2- Human genome project The Human Genome _ch17.pdf Jonathan Pevsner, Kennedy Krieger Institute 3- SNPs Studying Genetic Variation II: Computational Techniques Jim Mullikin, NHGRI TGA2005Lecture13.pdf 4- Comparative Genomics Comparative Sequence Analysis Elliott Margulies, NHGRI CTGA2005Lecture8.pdf 1- Genomic sequencing Many thanks to: Eric Green, NHGRI for the following slides extracted from his lecture on: Mapping and Sequencing CTGA2005Lecture1.pdf Genomic Sequencing - Brief Review Comparison of Sequenced Genome Sizes Comparison of Genetic & Physical Maps STSs: Provide common markers for "linking" genetic & physical maps BCB 444/544 Fall 07 Dobbs 5

6 With complete genomes (now), why bother to generate physical maps? Genomic sequencing requires assembly of sequences obtained from cloned DNA Human Genome Sequencing NIH: "Hierarchical" BAC-by-BAC 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? "Hierarchical" Subcloning Strategy Celera: Whole-Genome "Shotgun" Sequencing BCB 444/544 Fall 07 Dobbs 6

7 "Shotgun" Sequencing Stategy Either Strategy: Sequence "Finishing" = Hardest part!! Advances in DNA Sequencing Technology Sequencing Method #1: Gilbert-Maxim "Chemical Degradation" Sequencing Method #2: Sanger "Di-deoxy Chain Termination" 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) BCB 444/544 Fall 07 Dobbs 7

8 #33 - Genomics 1st Eukaryotic Genome Sequence: S. cerevisiae Recent technologies? Pyro- & 454 Sequencing 43 1st Animal Genome Sequence: C. elegans BCB 444/544 Fall 07 Dobbs 44 Timetable for Human Genome Sequencing: Faster than expected! 45 1st Draft Human Genome: Complete" in Public Sequencing - International Consortium

9 "Finishing" the Human Genome - continues After "Complete" Human Genome Sequence What next? Interpreting the Human Genome Sequence! Comparative Genomics: now with complete genomic sequences Comparing Genomes: Functional Elements ENCODE Project BCB 444/544 Fall 07 Dobbs 9

10 ENCODE - Web Sites ENCODE - Results? June Eric Green's Genomic Sequencing Challenges (2005 List) 57 BCB 444/544 Fall 07 Dobbs 10