computational analysis of biological networks. olga troyanskaya, … · 2004-03-10 ·...
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Gene function prediction
Computational analysis of biological networks.
Olga Troyanskaya, PhD
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Available Data
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Coexpression - Microarrays
Known DNA sequences
Glass slide
Isolate mRNA
Cells of Interest
Reference sample
0.25
0.73
0.14
0.12
0.01
gene
s 0.25 0.01 0.30 0.70
0.73 0.89 0.92 0.67
0.14 0.15 0.60 0.23
0.12 0.12 0.07 0.02
0.01 0.05 0.14 0.12
experiments
gene
s
Resulting data
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Genetic interactions
• Synthetic lethality• Synthetic interaction
– Choice of phenotypes important
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Physical interactions
• Yeast two hybrid• Co-IP precipitation• FRET (doesn’t have to be a true physical
interaction, but has to be close)• Protein arrays (can also test molecular
function directly)
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Yeast two hybrid
a) DNA-binding and activation domains (circles) are fused to proteins X and Y. The interaction of X and Y leads to reporter gene expression (arrow).
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Co-IP
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FRET
FRET (Fluorescence Resonance Energy Transfer) is aphotophysical effect where energy that is absorbed by one fluorescent molecule (donor) is transferred non-radioactively to a second fluorescent molecule (acceptor).
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Protein arrays
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Zhu et al., Annu. Rev. Biochem. 72:783
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Zhu et al., Annu. Rev. Biochem. 72:783
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Interaction coverage - yeast
Von Mering et al. Nature 417:399
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Methods for function prediction based on one type of data
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The Rosetta Stone method
Top sequence = fused domain that’s homologous to two separate seqs from another species
Eisenberg et al. Nature 405 p. 823
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Method of correlated gene neighbors
Eisenberg et al. Nature 405 p. 823
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Phylogenetic profiles method
Proteins are considered functionally linked if they share phylogenetic profiles (presence and absence in genomes). Proteins do not have the be homologous by sequence.
Eisenberg et al. Nature 405 p. 823
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Annotation assignment based on co-expression clusters
If enrichment for genes of a specific biological process, can claim unknowns are also involved in that process.
Prob of x out of n annotations assigned to the same GO term by chance
P(x or more of n genes being annotated to a particular term)
Num of permutations of x of n genes having the annotation
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Methods for function prediction based on diverse large-scale data
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The central densest region of interaction network (15000 interactions) from yeast. The interactions were collected from large-scale studies and the MIPS and BIND databases. Known molecular complexes can be seen clearly, as well as a large, previously unsuspected nucleolarcomplex.
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Abundance of high-throughput data• Sequence data
– Transcription factor binding sites– Functional domains
• Location data– Colocalization
• Physical association data – Yeast two hybrid– Co-IP
• Genetic relationship data– Synthetic lethality– Synthetic rescue– Synthetic interaction
Data explosion
Information explosion
?
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Overcoming noise with data integration
Gene clustering based on Gene clustering based on Microarray AnalysisMicroarray Analysis: :
Gene groupings based on Gene groupings based on Other Experimental DataOther Experimental Data: :
••TF binding sitesTF binding sites•• Affinity precipitationAffinity precipitation•• Two HybridTwo Hybrid
Gene grouping based on Gene grouping based on diverse highdiverse high--throughput datathroughput data
MetabolismMetabolism Function unknownFunction unknown
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General representation for gene groupings
Gene AGene AGene BGene BGene CGene C
Cluster 1Cluster 1
Gene AGene AGene DGene D
Cluster 2Cluster 2
Gene A Gene B Gene C Gene DGene A 1 1 1 1Gene B 1 1 1Gene C 1 1 1Gene D 1 1
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gene gene i…mi…m
gene
ge
ne i.
.mi..m
1 1 1 1 11
1 11 1
1 1 11 1 11 1 1 1 1
11 1
1 1
gene gene i…mi…m
gene
ge
ne i.
.mi..m
1 1 1 11
1 11
1 11
1 1 1 11
1 11 1
gene gene i…mi…m
gene
ge
ne i.
.mi..m
1 1 11 1
1 11 1
1 1 1 11
1 1 1 11
1 1 11 1
Apply MAGIC algorithm to Apply MAGIC algorithm to each gene each gene ii-- gene gene jj pairpair
gene gene i…mi…m
gene
ge
ne i.
.mi..m
1.0 1.0 0.3 0.8 0.91.0
1.0 0.11.0 0.4
1.0 1.00.3 0.4 1.00.8 0.1 1.0 0.8
1.00.9 1.0
0.8 1.0
MAGIC: Multi-source Association of Genes by Integration of Clusters
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A brief intro to Bayesian networks• Bayes rule• Bayesian networks – graphical probabilistic
models• Inference and learning
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Bayesian networks"The essence of the Bayesian approach is to provide a mathematical rule
explaining how you should change your existing beliefs in the light of new evidence. In other words, it allows scientists to combine new data with their existing knowledge or expertise. The canonical example is to imagine that a precocious newborn observes his first sunset, andwonders whether the sun will rise again or not. He assigns equal prior probabilities to both possible outcomes, and represents this by placing one white and one black marble into a bag. The following day, when the sun rises, the child places another white marble in the bag. The probability that a marble plucked randomly from the bag will be white (ie, the child's degree of belief in future sunrises) has thus gone from a half to two-thirds. After sunrise the next day, the child adds another white marble, and the probability (and thus the degree of belief) goes from two-thirds to three-quarters. And so on. Gradually, the initial belief that the sun is just as likely as not to rise each morning is modified to become a near-certainty that the sun will always rise."
From “The Economist”
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Bayes Rule
P(R=r|e) is probability that random variable R has value r given evidence e
Denominator is just a normalizing constant that ensures the posterior adds up to 1; it can be computed by summing up the numerator over all possible values of R, i.e., P(e) = P(R=0, e) + P(R=1, e) + ... = sum_r P(e | R=r) P(R=r)
or
Murphy, K. “A brief introduction to Bayes rule”
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Example of Bayes rule use
• Suppose a patient tests positive for disease. What is the P she actually has the disease?
• Depends on:– accuracy and sensitivity of the test– the background (prior) probability of the disease
• P(Test=true | Disease=true) = 0.95 (FNR=5%)• P(Test=true | Disease=false) = 0.05 (FPR=5%)• P(Disease=true) = 0.01 (1% of total population)
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Example of Bayes rule use (cont)
• D - Disease (R in the above equation)• T - Test (e in the above equation)
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The “famous” sprinkler Bayes netBayes nets are graphical probabilistic models
Conditional probability tables contain the priors
Murphy, K. “A Brief Introduction to Graphical Models and Bayesian Networks”
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The “famous” sprinkler Bayes netPrior probability that it is cloudy
Conditional probability that it rains when it’s cloudy
Probability that grass is wet when the sprinkler is off and it rains
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InferenceObserve: grass is wetTwo possible causes: either it is raining, or the sprinkler is on. Which
is more likely? Use Bayes' rule to compute the posterior probability of each explanation.
is a normalizing constant (probability (likelihood) of the data).
=> it is more likely that the grass is wet because it is raining: the likelihood ratio is 0.7079/0.4298 = 1.647.
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Bayes net encodes conditional independence relationships
A node is independent of its ancestors given its parents
By the chain rule of probability, the joint probability of all the nodes in the graph above is:P(C, S, R, W) = P(C) * P(S|C) * P(R|C,S) * P(W|C,S,R)
Using conditional independence relationships, rewrite as: P(C, S, R, W) = P(C) * P(S|C) * P(R|C) * P(W|S,R) simplified third term because R is independent of S given its parent Csimplified last term because W is independent of C given its parents S and R
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Conditional independence: “Bayes Ball”
Two (sets of) nodes A and B are conditionally independent (d-separated) given a set C if and only if there is no way for a ball to get from A to B in the graph. Hidden nodes are unshaded; observed nodes are shaded.
1st column: X is a "leaf" with two parents:
if X is hidden, its parents are marginally independent => the ball does not pass through
if X is observed, the parents become dependent, and the ball does pass through, because of the explaining away phenomenon.
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Learning Bayesian networks
• Two learning problems: structure + CPTs
Due either to missing data or to hidden nodes
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Learning: ML estimation
Goal of learning is to find the values of the parameters of each CPD which maximizes the likelihood of the training data. The normalized log-likelihood of the training set D is:
What does this mean intuitively?
Just counting!
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Learning: EM• EM algorithm finds a (locally) optimal Maximum
Likelihood Estimate of the parameters. • Idea behind EM: If we knew the values of all the
nodes, learning (the M step) would be easy (ML).– E step: compute the expected values of all the nodes
using an inference algorithm, then treat these expected values as observed.
– Given the expected counts, maximize the parameters, then recompute the expected counts, etc.
• EM is guaranteed to converge to a local maximum of the likelihood surface.
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Back to gene function predictionwith Bayes nets
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MAGIC Bayesian network
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Gene Ontology
• Three hierarchies: – Molecular function– Biological process– Cellular component
• Curated annotation
Biological process
Cell growth and/or maintenance
Metabolism Cell organization and biogenesis
Nucleobase, nucleoside,
nucleotide and nucleic acid metabolism
Protein metabolism
DNA metabolism
Establishment and/or maintenance
of chromatin architecture
DNA packaging
Nuclear organization and biogenesis
Chromosome organization and
biogenesis
Protein modification
Cell cycle
Mitotic cell cycle
Mitotic cell cycle
Transcription
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Biological process
Cell growth and/or maintenance
Metabolism Cell organization and biogenesis
Nucleobase, nucleoside,
nucleotide and nucleic acid metabolism
Protein metabolism
DNA metabolism
Establishment and/or maintenance
of chromatin architecture
DNA packaging
Nuclear organization and biogenesis
Chromosome organization and
biogenesis
Protein modification
Cell cycle
Mitotic cell cycle
Mitotic cell cycle
Transcription
Evaluation method
pairs of num totalGOterm same toannotated geneB &geneA s.t. pairs numTPproportion =
pairs of num totalGOterm same the toannotatednot geneB&geneA s.t. pairs numFPproportion =
Predicted Predicted Gene PairsGene Pairs
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0
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0 1000 2000 3000 4000 5000FP
TPMAGIC
SOM
K-means
HIER
MAGIC performs better than input methods over a range of FP levels
0
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0 100 200 300FP
TPMAGIC
SOM
K-means
HIER
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MAGIC Non-Expression only
MAGIC Expression only
MAGIC
SOM
K-means
HIER
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UbiquitinUbiquitin--dependent dependent protein catabolismprotein catabolism
10 genes in cluster:10 genes in cluster:9 9 ubiquitinubiquitin--dependent dependent
protein catabolismprotein catabolism1 nucleotide1 nucleotide--excision excision
repairrepair
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Vacuolar Vacuolar acidification acidification clustercluster9 genes in cluster:9 genes in cluster:7 vacuolar acidification7 vacuolar acidification2 phosphate transport2 phosphate transport
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Protein Protein biosynthesis biosynthesis clustercluster•• 49 protein biosynthesis49 protein biosynthesis•• 9 other functions9 other functions•• 10 unknown 10 unknown function function
predictionprediction
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Next Steps• Experiment with automatically learning the
weights (in progress)• MAGIC for specific processes (in progress)• Test best predictions experimentally• Integrate system with the Saccharomyces
Genome Database for public use