1. Genomic Data Integration
Curtis Huttenhower
Harvard School of Public Health
Department of Biostatistics

2. A Definition of Integrative Data Mining
Prior knowledge
Genomic data
Gene ↓
Function
Gene ↓
Data ↓
Function
Function ↓

3. Machine Learning for Data Integration
100Ms gene pairs → G1 G2 + G4 G9 … G3 G6 - G7 G8 G5 ?
0.9
0.7
0.1
0.2
0.8
0.5
0.05
0.6
← 1Ks datasets
P(G2-G5|Data) = 0.85
High
Correlation
Low
Frequency
High
Correlation
Low
Coloc.
Not coloc.
Frequency + =
Similar
Dissim.
Frequency
High
Similarity
Low

4. Machine Learning for Data Integration
Jansen 2003
Troyanskaya 2003
Functional Relationship
Golub 1999
Butte 2000
Whitfield 2002
Hansen 1998

5. Alternative Data Integration Frameworks
Lee 2004
Lanckriet 2004
Aerts 2006

6. Functional Networks Global interaction network Metabolism network
function.princeton.edu/hefalmp
string-db.org
funcnet.eu
homes.esat.kuleuven.be/~bioiuser/endeavour
Metabolism network
Conserved network
Kidney network

7. Biological Networks: Clusters, Hubs, Bottlenecks, and Flow

8. Biological Networks: Network Motifs
Bi-fan
WGD and evolvability
Feedback
memory
Positive auto-regulation
delay
Negative auto-regulation
speed + stability
Coherent feed-forward
filter
mavisto.ipk-gatersleben.de
theinf1.informatik.uni-jena.de/~wernicke/motifs
Incoherent feed-forward
pulse
Milo 2002 Alon 2007

9. Predicting Gene Function
Predicted relationships between genes
High
Confidence
Low
Cell cycle genes

10. Predicting Gene Function
Predicted relationships between genes
High
Confidence
Low
Cell cycle genes

11. Predicting Gene Function
Huttenhower 2009
Predicted relationships between genes
High
Confidence
Low
These edges provide a measure of how likely a gene is to specifically participate in the process of interest.
Cell cycle genes
Pena-Castillo 2008
Rodrigues 2007

12. Comprehensive Validation of Computational Predictions
Hess, 2009
Hibbs, 2009
Genomic data
Prior knowledge
Computational Predictions of Gene Function
SPELL
Hibbs et al 2007
bioPIXIE
Myers et al 2005
MEFIT
Retraining
Genes predicted to function in mitochondrion organization and biogenesis
New known functions for correctly predicted genes
Could go (-)
Laboratory Experiments
Petite
frequency
Growth
curves
Confocal microscopy

13. Evaluating the Performance of Computational Predictions
Huttenhower, 2009
Genes involved in mitochondrion organization and biogenesis
106
Original GO Annotations
135
Under-annotations 82
Novel Confirmations,
First Iteration 17
Novel Confirmations,
Second Iteration
340 total: >3x previously known genes in ~5 person-months
Could go (-)

14. Evaluating the Performance of Computational Predictions
Huttenhower, 2009
Genes involved in mitochondrion organization and biogenesis
Computational predictions from large collections of genomic data can be accurate despite incomplete or misleading gold standards, and they continue to improve as additional data are incorporated.
106
Original GO Annotations 95
Under-annotations 40
Confirmed
Under-annotations 80
Novel Confirmations
First Iteration 17
Novel Confirmations
Second Iteration
340 total: >3x previously known genes in ~5 person-months
Could go (-)

15. Functional Mapping: Mining Integrated Networks
Predicted relationships between genes
High
Confidence
Low
The strength of these relationships indicates how cohesive a process is.
Chemotaxis

16. Functional Mapping: Mining Integrated Networks
Predicted relationships between genes
High
Confidence
Low
Chemotaxis

17. Functional Mapping: Mining Integrated Networks
Predicted relationships between genes
High
Confidence
Low
The strength of these relationships indicates how associated two processes are.
Chemotaxis
Flagellar assembly

18. Functional Mapping: Associations Between Gene Sets
Hydrogen Transport
Electron Transport
Cellular Respiration
Protein Processing
Peptide Metabolism
Cell Redox Homeostasis
Aldehyde Metabolism
Energy Reserve Metabolism
Vacuolar Protein Catabolism
Negative Regulation of Protein Metabolism
Organelle Fusion
Protein Depolymerization
Organelle Inheritance
Edges
Associations between processes
Moderately
Strong
Very
Strong

19. Functional Mapping: Associations Between Gene Sets
Hydrogen Transport
Electron Transport
Cellular Respiration
Protein Processing
Peptide Metabolism
Cell Redox Homeostasis
Aldehyde Metabolism
Energy Reserve Metabolism
Vacuolar Protein Catabolism
Negative Regulation of Protein Metabolism
Organelle Fusion
Protein Depolymerization
Organelle Inheritance
Edges
Associations between processes
Moderately
Strong
Very
Strong
Borders
Data coverage of processes
Sparsely
Covered
Well
Covered

20. Functional Mapping: Associations Between Gene Sets
Hydrogen Transport
Electron Transport
Cellular Respiration
Protein Processing
Peptide Metabolism
Cell Redox Homeostasis
Aldehyde Metabolism
Energy Reserve Metabolism
Vacuolar Protein Catabolism
Negative Regulation of Protein Metabolism
Organelle Fusion
Protein Depolymerization
Organelle Inheritance
Edges
Associations between processes
Moderately
Strong
Very
Strong
Nodes
Cohesiveness of processes
Below
Baseline
Baseline
(genomic
background)
Very
Cohesive
Borders
Data coverage of processes
Sparsely
Covered
Well
Covered

21. Functional Maps: Focused Data Summarization
ACGGTGAACGTACAGTACAGATTACTAGGACATTAGGCCGTATCCGATACCCGATA
Data integration summarizes an impossibly huge amount of experimental data into an impossibly huge number of predictions; what next?

22. Functional Maps: Focused Data Summarization
ACGGTGAACGTACAGTACAGATTACTAGGACATTAGGCCGTATCCGATACCCGATA
How can a biologist take advantage of all this data to study his/her favorite gene/pathway/disease without losing information?
Functional mapping
Very large collections of genomic data
Specific predicted molecular interactions
Pathway, process, or disease associations
Underlying experimental results and functional activities in data

23. Efficient Computation For Biological Discovery
Massive datasets and genomes require efficient algorithms and implementations.
Sleipnir C++ library for computational functional genomics
Data types for biological entities
Microarray data, interaction data, genes and gene sets, functional catalogs, etc. etc.
Network communication, parallelization
Efficient machine learning algorithms
Generative (Bayesian) and discriminative (SVM)
And it’s fully documented!
huttenhower.sph.harvard.edu/sleipnir
It’s also speedy: microbial data integration computation takes <3hrs.

24. Thanks! Curtis Huttenhower Harvard School of Public Health
Department of Biostatistics

26. Meta-Analysis for Data Integration
Evangelou 2007 + =

27. Meta-Analysis for Data Integration
Evangelou 2007
Simple regression:
All datasets are equally accurate
Random effects:
Variation within and among datasets and interactions