Answering biological questions using large genomic data collections Curtis Huttenhower 10-05-09 Harvard School of Public Health Department of Biostatistics

Answering biological questions using large genomic data collections

Curtis Huttenhower

10-05-09Harvard School of Public HealthDepartment of Biostatistics

2

A Definition ofComputational Functional Genomics

Genomic data Prior knowledge

Data↓

Function

Function↓

Function

Gene↓

Gene

Gene↓

Function

3

MEFIT: A Framework forFunctional Genomics

BRCA1 BRCA2 0.9BRCA1 RAD51 0.8RAD51 TP53 0.85…

Related Gene Pairs

HighCorrelation

LowCorrelation

Fre

quen

cy

MEFIT

4


BRCA1 BRCA2 0.9BRCA1 RAD51 0.8RAD51 TP53 0.85…

BRCA2 SOX2 0.1RAD51 FOXP2 0.2ACTR1 H6PD 0.15…

Related Gene Pairs

Unrelated Gene PairsHigh

CorrelationLow

Correlation

Fre

quen

cy

MEFIT

5


Golub 1999

Butte 2000

Whitfield 2002

Hansen 1998

Functional Relationship

6


Golub 1999

Butte 2000

Whitfield 2002

Hansen 1998

Functional Relationship

Biological Context

Functional areaTissueDisease…

7

Functional Interaction Networks

MEFIT

Global interaction network

Autophagy networkVacuolar transport

network Translation network

Currently have data from30,000 human experimental results,

15,000 expression conditions +15,000 diverse others, analyzed for

200 biological functions and150 diseases

8

Predicting Gene Function

Cell cycle genes

Predicted relationships between genes

HighConfidence

LowConfidence

9

Predicting Gene FunctionPredicted relationships

between genes

HighConfidence

LowConfidence

Cell cycle genes

10

Cell cycle genes

Predicting Gene FunctionPredicted relationships

between genes

HighConfidence

LowConfidence

These edges provide a measure of how likely a gene is to

specifically participate in the process of

interest.

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Functional Associations Between Contexts


HighConfidence

LowConfidence

The average strength of these relationships

indicates how cohesive a process is.

Cell cycle genes

12



HighConfidence

LowConfidence

Cell cycle genes

13


DNA replication genes

The average strength of these relationships indicates how

associated two processes are.


HighConfidence

LowConfidence

Cell cycle genes

14

Functional Associations Between Processes

EdgesAssociations between processes

VeryStrong

ModeratelyStrong

Hydrogen Transport

Electron Transport

Cellular Respiration

Protein ProcessingPeptide

Metabolism

Cell Redox Homeostasis

Aldehyde Metabolism

Energy Reserve

Metabolism

Vacuolar Protein

Catabolism

Negative Regulation of Protein Metabolism

Organelle Fusion

Protein Depolymerization

Organelle Inheritance

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VeryStrong

ModeratelyStrong

BordersData coverage of processes

WellCovered

SparselyCovered

Hydrogen Transport

Electron Transport



Metabolism


Aldehyde Metabolism

Energy Reserve

Metabolism

Vacuolar Protein

Catabolism


Organelle Fusion



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VeryStrong

ModeratelyStrong

NodesCohesiveness of processes

BelowBaseline

Baseline(genomic

background)

VeryCohesive

BordersData coverage of processes

WellCovered

SparselyCovered

Hydrogen Transport

Electron Transport



Metabolism


Aldehyde Metabolism

Energy Reserve

Metabolism

Vacuolar Protein

Catabolism


Organelle Fusion



AHP1DOT5GRX1GRX2…

APE3LAP4PAI3PEP4 …

17

HEFalMp: Predicting human gene function

HEFalMp

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HEFalMp: Predicting humangenetic interactions

HEFalMp

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HEFalMp: Analyzing human genomic data

HEFalMp

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HEFalMp: Understanding human disease

HEFalMp

21

Validating Human Predictions

Autophagy

Luciferase(Negative control)

ATG5(Positive control) LAMP2 RAB11A

NotStarved

Starved(Autophagic)

Predicted novel autophagy proteins

5½ of 7 predictions currently confirmed

With Erin Haley, Hilary Coller

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Comprehensive Validation of Computational Predictions

Genomic data

Computational Predictions of Gene Function

MEFITSPELLHibbs et al 2007

bioPIXIEMyers et al 2005

Genes predicted to function in mitochondrion organization

and biogenesis

Laboratory ExperimentsPetite

frequencyGrowthcurves

Confocal microscopy

New known functions for correctly predicted genes

Retraining

With David Hess, Amy Caudy

Prior knowledge

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Evaluating the Performance of Computational Predictions

106Original GO Annotations

Genes involved in mitochondrion organization and biogenesis

135Under-annotations

82Novel Confirmations,

First Iteration

17Novel Confirmations,

Second Iteration

340 total: >3x previously known genes in ~5 person-months

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Evaluating the Performance of Computational Predictions

106Original GO Annotations

Genes involved in mitochondrion organization and biogenesis

95Under-annotations

40Confirmed

Under-annotations

80Novel Confirmations

First Iteration

17Novel Confirmations

Second Iteration

340 total: >3x previously known genes in ~5 person-months

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.

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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?

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Functional Maps:Focused Data Summarization

ACGGTGAACGTACAGTACAGATTACTAGGACATTAGGCCGTATCCGATACCCGATA

How can a researcher 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

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Thanks!

NIGMShttp://function.princeton.edu/hefalmp

Interested? I’m accepting students and postdocs!

Hilary CollerErin HaleyTsheko Mutungu

Olga TroyanskayaMatt HibbsChad MyersDavid HessEdo AiroldiFlorian Markowetz

Shuji OginoCharlie Fuchs

http://www.huttenhower.org

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Next Steps:Microbial Communities

• Data integration is off to a great start in humans– Complex communities of distinct cell types– Very sparse prior knowledge

• Concentrated in a few specific areas

– Variation across populations– Critical to understand mechanisms of disease

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• What about microbial communities?– Complex communities of distinct species/strains– Very sparse prior knowledge

• Concentrated in a few specific species/strains

– Variation across populations– Critical to understand mechanisms of disease

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PKH1

PKH3

PKH2LPD1

CAR1

W04B5.5

pdk-1

R04B3.2

LLC1.3

T21F4.1

PDPK1

ARG1DLD

ARG2

AGA

~120 available expression datasets

~70 species

PKH1

PKH3

PKH2LPD1

CAR1

W04B5.5

pdk-1

R04B3.2

LLC1.3

T21F4.1

PDPK1

ARG1DLD

ARG2

AGA

Weskamp et al 2004

Flannick et al 2006

Kanehisa et al 2008

Tatusov et al 1997

• Data integration works just as well in microbes as it does in humans• We know an awful lot about some microorganisms and almost nothing about others• Purely sequence-based and purely network-based tools for function transfer both fall short• We need data integration to take advantage of both and mine out useful biology!

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Next Steps:Functional Metagenomics

• Metagenomics: data analysis from environmental samples– Microflora: environment includes us!

• Another data integration problem– Must include datasets from multiple organisms

• Another context-specificity problem– Now “context” can also mean “species”

• What questions can we answer?– How do human microflora interact with diabetes,

obesity, oral health, antibiotics, aging, …– What’s shared within community X?

What’s different? What’s unique?– What’s perturbed in disease state Y?

One organism, or many? Host interactions?– Current methods annotate ~50% of synthetic data,

<5% of environmental data

PKH1

PKH3

PKH2LPD1

CAR1

W04B5.5

pdk-1

R04B3.2

LLC1.3

T21F4.1

PDPK1

ARG1DLD

ARG2

AGA

Documents

Answering biological questions using large genomic data collections Curtis Huttenhower 10-05-09 Harvard School of Public Health Department of Biostatistics