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Overview Introduction Biological network data Text mining Gene Ontology Expression data basics Expression, text mining, and GO Modules and complexes Domains and conclusion

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Page 1: Overview Introduction Biological network data Text mining Gene Ontology Expression data basics Expression, text mining, and GO Modules and

Overview

Introduction Biological network data Text mining Gene Ontology Expression data basics

Expression, text mining, and GO

Modules and complexes Domains and conclusion

Page 2: Overview Introduction Biological network data Text mining Gene Ontology Expression data basics Expression, text mining, and GO Modules and

Scenario

Ran a set of expression experiments to study a given disease state.

Need to put the results into a functional context.

Page 3: Overview Introduction Biological network data Text mining Gene Ontology Expression data basics Expression, text mining, and GO Modules and

Atherosclerosis

Most common fatal disease in the U.S., and not well-understood.

Page 4: Overview Introduction Biological network data Text mining Gene Ontology Expression data basics Expression, text mining, and GO Modules and

Microarray analysis

Analyzed 51 artery segments from the hearts from 22 heart transplant patients.

Classified segments by their disease pathology. Will assess the differences between Type I

(moderate) and Type V (severe) atherosclerosis.

Performed microarray analysis of each segment. Agilent expression array with probesets for 13,000

human genes.

Page 5: Overview Introduction Biological network data Text mining Gene Ontology Expression data basics Expression, text mining, and GO Modules and

SAM microarray statistic

For each gene i, contrasted expression in Type I and Type V lesions with SAM (Proc Natl Acad Sci USA 98: 5116-21, 2001). High positive SAM score: gene expressed more highly in

Type V lesions. Large negative SAM score: gene expressed more highly in

Type I lesions.

Page 6: Overview Introduction Biological network data Text mining Gene Ontology Expression data basics Expression, text mining, and GO Modules and

Analysis pipeline

1. Biomarker identification

For formal studies, use machine learning methods

For exploratory work, select several genes with extreme SAM scores.

Page 7: Overview Introduction Biological network data Text mining Gene Ontology Expression data basics Expression, text mining, and GO Modules and

Analysis pipeline, continued

2. Biomarker association Basic question: for this context,

what is common among the biomarker genes?

Approaches Exhaustive reading GO analysis Literature searching

Page 8: Overview Introduction Biological network data Text mining Gene Ontology Expression data basics Expression, text mining, and GO Modules and

Page 9: Overview Introduction Biological network data Text mining Gene Ontology Expression data basics Expression, text mining, and GO Modules and

pros and cons of this approach

Pro: associations are specific to this disease context

Pro: identifies relevant literature

Con: might not find associations on all of your biomarkers

Con: might find associations on other genes

Page 10: Overview Introduction Biological network data Text mining Gene Ontology Expression data basics Expression, text mining, and GO Modules and

Iterative literature searching

Perform an initial searchColor the network by SAM d-scoreIdentify any new “responsive”

genesAdd to biomarker listRepeat

Page 11: Overview Introduction Biological network data Text mining Gene Ontology Expression data basics Expression, text mining, and GO Modules and

Discussion topic

Why not use all genes with extreme SAM scores as biomarkers? Why

iterate?

Page 12: Overview Introduction Biological network data Text mining Gene Ontology Expression data basics Expression, text mining, and GO Modules and

Once you have a good network:

1. Use BiNGO to identify the enriched GO terms

2. Look at the genes corresponding to selected enriched terms

3. Check the literature search sentences for those genes

4. Choose one or two sentences, look at the abstracts.

5. Iterate if desired (or go to lunch)

Page 13: Overview Introduction Biological network data Text mining Gene Ontology Expression data basics Expression, text mining, and GO Modules and

Page 14: Overview Introduction Biological network data Text mining Gene Ontology Expression data basics Expression, text mining, and GO Modules and

Final points

No right or wrong answers, only plausible or novel hypotheses.

You can take any approach you wish.

“If it was easy, everyone would be doing it”.

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