1. Two bioinformatics applications of dynamic Bayesian networks
William Stafford Noble
Department of Genome Sciences
Department of Computer Science and Engineering
University of Washington

2. Outline Segmenting genomic data Matching peptides to mass spectra
Background: DNA, chromatin and DNase I
Simple solution
Wavelets
Hierarchical model
Matching peptides to mass spectra
Background: tandem mass spectrometry
Modeling peptide fragmentation

3. The human genome in vivo
Chromatin Fiber
Gene ‘domains’
Nucleus
Trans-factor complex
DnaseI
Hypersensitive Site
Genes
Genomic
DNA
Packaged into Chromatin

4. Measuring chromatin accessibility

6. A simple hidden Markov model
very ^
Open
chromatin
Closed
chromatin
Each state contains a single Gaussian.
The model has six parameters (two transitions, two means, two standard deviations).
The parameters are initialized randomly and trained in an unsupervised fashion via expectation-maximization.
EM is re-started 100 times, and we select the parameters that yield the highest likelihood.
The original data set is then segmented using either Viterbi or posterior decoding.

7. 1.5 megabases

8. A problem, and two solutions
Problem: We are interested in phenomena occurring at multiple scales.
Solution #1: Perform a wavelet smooth prior to HMM analysis.
Solution #2: Build a more complex probability model.

13. Change point model Four-state model:
major DNase hypersensitive site (DHS),
minor DHS,
intermediate sensitivity region, and
insensitive region.
Continuous mixture of Gaussians at each state.
Gamma distribution of lengths within each region.

15. Spanning the gaps
Beginning in State 1 (Insensitive)

16. Spanning the gaps
Beginning in State 4 (Major DHS)

17. Selecting the number of states

18. Improved fit to the data
Insensitive
Intermediate sensitivity
Minor DHS
Major DHS
Each panel is a QQ plot of the difference between the observed residuals and the theoretical Gaussian.

19. Capturing different scales

20. Enrichment of biologically relevant features

21. Future directions Many types of genomic data
Phylogenetic conservation scores
Various histone modifications
Replication timing, etc.
Perform segmentions in multiple dimensions simultaneously.
Assign statistical significance to observed segments.

22. Shotgun proteomics Training PSMs Test PSMs Trained Model Evaluation
Probability
Model
PSM = peptide-spectrum match

23. Peptide sequence influences peak height

24. Bayesian network
We model peptide fragmentation using a Bayesian network.
Nodes represent random variables, and edges represent conditional dependencies.
Each node stores a conditional probability table (CPT) giving Pr(node|parents).
Is b-ion
observed?
b-ion
intensity
1.00
0.00
no b-ion observed
0.75
0.25
b-ion observed
intensity > 50%
intensity < 50%

25. Ion series modeled in a Markov chain
Is b-ion
observed?
Is b-ion
observed?
Is b-ion
observed?
Is b-ion
observed?
Is b-ion
observed?
b-ion
intensity
b-ion
intensity
b-ion
intensity
b-ion
intensity
b-ion
intensity
~ PepHMM (Han et al., 2005).

26. A more realistic model Is b-ion observed? b-ion intensity N-term AA
C-term AA
Is ion
detectable?
Fractional
m/z
Is proton
mobile?

27. Ion series modeled in a Markov chain

28. Vectors of log-odds ratios
Correct peptide-spectrum matches
Incorrect peptide-spectrum matches

29. Binary classifier

30. Model Evaluation: Accuracy
Training PSMs
Test PSMs
Trained
Model
Evaluation
Probability
Model
Model
Redundant TP/FP
Unique TP/FP
Bayes Net
285/300, 95%
137/144, 95.1%
SEQUEST
288/300, 96%
136/144, 94.4%
InsPecT
274/300, 91.3%
131/144, 90.9%

31. An incorrect identification
Bayes net: HQDETQDALNALDLLTNEK
SEQUEST: LRPGAELLEGAHVGNFVEMK
This peptide does not appear in E. coli, the organism from which this protein sample was derived.
Blue = b and y, green = a, red = ammonia loss, magenta = water loss, sienna = +2

32. Co-eluting peptides SEQUEST: AFPEAVLFIHPLDAK
Bayes net: DVFVHFSALQGNQFK
SEQUEST: AFPEAVLFIHPLDAK
Blue = b and y, green = a, red = ammonia loss, magenta = water loss, sienna = +2

33. Future directions
Build a single Bayesian network that includes all ion types.
Produce more descriptive outputs from the Bayesian network for input to the classifier.
Add more biophysical details to the model: chromatography retention time, a better mass-to-charge estimate, etc.
Generate a better (larger, more accurate) gold standard data set.

34. Acknowledgments DNase I hypersensitivity Wavelet analysis: Bob Thurman
John Stamatoyannopoulos
Pete Sabo
Scott Kuehn
many others in the Stam lab
Wavelet analysis: Bob Thurman
Change point model
Charles Lawrence
Heng Lian
William Thompson
Mass spectrometry
Aaron Klammer
Jeff Bilmes
Sheila Reynolds
Michael MacCoss