BCB 444/544
Lecture 33
Genomics
#33_Nov09
BCB 444/544 F07 ISU Dobbs#33 - Genomics
11/09/07
1
Required Reading
(before lecture)
√ Mon Nov 5 - Lecture 31
Phylogenetics – Parsimony and ML
• Chp 11 - pp 142 – 169
√ Wed Nov 7 - Lecture 32
Machine Learning
Fri Nov 9 - Lecture 33
Functional and Comparative Genomics
• Chp 17 and Chp 18
BCB 444/544 F07 ISU Dobbs#33 - Genomics
11/09/07
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Assignments & Announcements
Fri Nov 9 - HW#6
(will be posted this weekend)
HW#6 - More fun with Machine Learning!!
Due: Fri Nov 16
(or sometime before Mon Nov 26)
BCB 444/544 F07 ISU Dobbs#33 - Genomics
11/09/07
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Seminars this Week
BCB List of URLs for Seminars related to Bioinformatics:
http://www.bcb.iastate.edu/seminars/index.html
• 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
BCB 444/544 F07 ISU Dobbs#33 - Genomics
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Chp 11 – Phylogenetic Tree Construction Methods
and Programs
SECTION IV MOLECULAR PHYLOGENETICS
Xiong: Chp 11 Phylogenetic Tree Construction Methods
and Programs
•
•
•
•
Distance-Based Methods
Character-Based Methods
Phylogenetic Tree Evaluation
Phylogenetic Programs
BCB 444/544 F07 ISU Dobbs#33 - Genomics
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Machine Learning
• 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
BCB 444/544 F07 ISU Dobbs#33 - Genomics
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Examples of Machine Learning Algorithms
• Naïve Bayes (NB)
• Bayes Theorem
• Neural network (NN) or Artificial Neural Net (ANN)
• Perceptrons
• Support Vector Machine (SVM)
• Kernel functions
Lab - WEKA: Decision Trees (DT), NB, SVM
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An Application:
Predicting RNA Binding Sites in Proteins
• 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
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Bayes Theorem Applied to
RNA Binding Site Prediction
P(binding ) P(aa seq | binding )
P(binding | aa seq ) 
P(aa seq )
P(c  1) P( X  x | c  1)
P (c  1 | X  x ) 
P( X  x)
P(c  0) P( X  x | c  0)
P (c  0 | X  x ) 
P( X  x)
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Naïve Bayes for Binary Classification
Assign c = 1 if
P (c  1 | X  x )

P (c  0 | X  x )
Otherwise, assign c = 0
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Example: Is ARG 6 RNA-binding or not?
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
≥ θ
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Predicted vs Actual RNA Binding for
Ribosomal protein L15 (PDB ID 1JJ2:K)
Predicted
BCB 444/544 F07 ISU Dobbs#33 - Genomics
Actual
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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
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Biological Neurons “Sum” Input Signals
& Generate Output Signal
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
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Simple Neuron = “Perceptron”
Perceptron is “Simplest ANN” = feed-forward NN
= linear classifier
Image from Christos Stergiou and Dimitrios Siganos
http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html
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The Perceptron
X1
X2
w1
w2
N
1 S  T

0 S  T
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.
Training set must contain both classes of data (i.e.. with “1” and “0” output).
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Perceptron “Sums” Inputs by Computing
Dot Product S = XW
• 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:
1
1
F(X ) 
1  e X
1/2
0
0
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Training a Perceptron
Find the weights W that minimize the error function E:
P
E   F(X W )  t(X )
i
i
i1
Use steepest descent:
- compute gradient:
- update weight vector:
- iterate
2
P: number of training examples
Xi: training vectors
F(WXi): output of perceptron
t(Xi) : target value for Xi
 E E E
E
E  
,
,
,...,
wN
 w1 w2 w3



Wnew  Wold  E
BCB 444/544 F07 ISU Dobbs#33 - Genomics
(: learning rate)
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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
i1
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Support Vector Machines - SVMs
Image from http://en.wikipedia.org/wiki/Support_vector_machine
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SVM Finds Maximum-Margin Hyperplane
(i.e., hyperplane that provides maximum separation
between two classes of instances in dataset)
Image from http://en.wikipedia.org/wiki/Support_vector_machine
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Kernel “Trick”
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Kernel Function
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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
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Genomics -
for excellent overview lectures,
see these posted by NHGRI & Pevsner:
1- Genomic sequencing
Mapping and Sequencing
Eric Green, NHGRI
CTGA2005Lecture1.pdf
2- Human genome project
The Human Genome 2005-10-19_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
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1- Genomic sequencing
Many thanks to:
Eric Green, NHGRI
for the following slides extracted from his lecture on:
Mapping and Sequencing
CTGA2005Lecture1.pdf
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Genomic Sequencing - Brief Review
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E Green 2005
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Comparison of Sequenced Genome Sizes
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E Green 2005
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Comparison of Genetic & Physical Maps
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E Green 2005
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STSs: Provide common markers for "linking"
genetic & physical maps
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E Green 2005
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With complete genomes (now), why bother to
generate physical maps?
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E Green 2005
BCB 444/544 F07 ISU Dobbs#33 - Genomics
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Genomic sequencing requires assembly of
sequences obtained from cloned DNA
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E Green 2005
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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
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NIH: "Hierarchical" BAC-by-BAC Sequencing
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E Green 2005
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"Hierarchical" Subcloning Strategy
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E Green 2005
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Celera: Whole-Genome "Shotgun" Sequencing
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E Green 2005
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"Shotgun" Sequencing Stategy
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E Green 2005
BCB 444/544 F07 ISU Dobbs#33 - Genomics
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Either Strategy:
Sequence "Finishing" = Hardest part !!
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Advances in DNA Sequencing Technology
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Sequencing Method #1: Gilbert-Maxim
"Chemical Degradation"
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E Green 2005
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Sequencing Method #2: Sanger
"Di-deoxy Chain Termination"
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E Green 2005
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Automated Sequencing for Genome Projects:
Sanger method - with improvements
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Another “recent” improvement: rapid & high resolution
separation of fragments in capillaries instead of gels
(E Yeung,Ames Lab, ISU)
E Green 2005
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Recent technologies?
Pyro- & 454 Sequencing
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1st Eukaryotic Genome Sequence:
S. cerevisiae
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E Green 2005
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1st Animal Genome Sequence:
C. elegans
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Timetable for Human Genome Sequencing:
Faster than expected!
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BCB 444/544 F07 ISU Dobbs#33 - Genomics
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1st Draft Human Genome:
”Complete" in 2001
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Public Sequencing - International Consortium
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"Finishing" the Human Genome - continues…
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After "Complete" Human Genome Sequence
What next?
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Interpreting the Human Genome Sequence!
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Comparative Genomics:
now with complete genomic sequences
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Comparing Genomes: Functional Elements
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ENCODE Project
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ENCODE - Web Sites
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ENCODE - Results? June 2007
http://www.nature.com/nature/journal/v447/n7146/full/nature05874.html
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Eric Green's Genomic Sequencing Challenges
(2005 List)
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