Distinguishing regulators of biomolecular pathways
This presentation is the property of its rightful owner.
Sponsored Links
1 / 22

Distinguishing Regulators of Biomolecular Pathways PowerPoint PPT Presentation


  • 46 Views
  • Uploaded on
  • Presentation posted in: General

Distinguishing Regulators of Biomolecular Pathways. Mentor: Dr. Xiwei Wu City of Hope. Sean Caonguyen SoCalBSI 8/21/08. Expression Pattern Analysis. Microarray technology is a powerful tool for investigating cellular activity at different levels

Download Presentation

Distinguishing Regulators of Biomolecular Pathways

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Distinguishing regulators of biomolecular pathways

Distinguishing Regulators of Biomolecular Pathways

Mentor:

Dr. Xiwei WuCity of Hope

Sean Caonguyen

SoCalBSI

8/21/08


Expression pattern analysis

Expression Pattern Analysis

  • Microarray technology is a powerful tool for investigating cellular activity at different levels

  • DNA microarrays can be used to identify genetic ‘‘signatures’’ for disease

Pan et al. (2005)

http://www.sciencedaily.com/images/2007/09/070912102212.jpg


A traditional approach to dna microarray analysis

Threshold

A Traditional Approach to DNA Microarray Analysis

  • Individual Gene Analysis

    • Two step process

      • Selects genes from an arbitrarily chosen cut-off

      • From the selected genes, one infers biological meaning of gene expression data

Gene Expression Data

Gene Selected

Biological Interpretation

Jiang Z and Gentlemen R. (2006) and Nam D, et al. (2007)


Emerging approach to dna microarray analysis

Assess gene set directly

Emerging Approach to DNA Microarray Analysis

  • Gene Set Analysis (GSA)

    • Rank all genes based on their phenotype association

    • Calculate a maximal enrichment score for each gene set

    • Rank each gene set score for biological interpretation

Gene Set Database

Gene Expression Data

Biological Interpretation

Jiang Z and Gentlemen R. (2006) and Nam D, et al. (2007)


Biological significance of gene set analyses

Biological Significance of Gene Set Analyses

  • Ability to identify subtle changes in gene expression that are undetectable by traditional approaches

  • No arbitrary threshold

  • Generate results that are easier to interpret


Current problem with gsa

Current Problem with GSA

  • Reduces gene set into a list of names

  • No difference in up-regulation and down-regulation

    • Directionality is lost

A

A

up-regulation

up-regulation

down-regulation

B

B

C

E

E

D

D

F

F

G

P

P

HIGHER

Suggests a lower probability of pathway activation

Suggests that the pathway is activated


Enriched gene set analysis

Assess gene set directly

Enriched Gene Set Analysis

Gene Expression Data

Gene Set Database

Curated Analysis

Biological Interpretation


Useful tools for the pathway analysis program

Useful Tools for the Pathway Analysis Program

  • National Cancer Institutes (NCI) Pathway Interaction Database (http://pid.nci.nih.gov/PID/index.shtml)

    • contains information about molecular interactions and biological processes in signaling pathways

    • focuses on cancer research in human cells

    • searches for biomolecules, processes, or by viewing pathways

    • Data format

      • Graphics: SVG or GIF

      • Texts: XML or BioPax


Segment of the phosphoinositide 3 kinases pi3k signaling pathway

Key to Icons

Segment of the Phosphoinositide 3-Kinases (PI3K) Signaling Pathway

XML Script

non-lipid kinase pathway

of Class IB PI3K


Project objective

Project Objective

  • Create a program to distinguish the activators and inhibitors in each signaling pathway

    • Requires extensive use of XML Parser in Python


Approach to project

Approach to Project

  • Identify all the elements in the pathway

  • Record the pairwise interactions

    • Linking each interaction

  • Determine the role of each molecule

    • Finding each leaf node

    • Using a traceback method

A

B

C

E

D

F

G

P


1 identify the elements in the pathway

1) Identify the Elements in the Pathway

  • Properly assign each ID to reference a “preferred symbol”

  • Locate each interaction ID


2 record the pairwise interactions

2) Record the Pairwise Interactions

  • How to can we store each interaction?

    • Memory efficient

    • Easy extraction of data

A

B

C

E

D

F

G

Sparse Matrix!

P


Sparsing matrix initialization

Sparsing Matrix Initialization

Regulators

A

1

B

C

1

-1

Output

1

E

D

-1

1

F

G

Sparse Matrix

1

P


3 determine the role of each molecule

3) Determine the Role of Each Molecule

Regulators

A

1

B

C

1

-1

Output

1

E

D

-1

1

F

G

1

P

Traceback each leaf node

Identify each leaf node


Locate activated pathways for better biological interpretation

Locate Activated Pathways for Better Biological Interpretation

  • Gene Expression Data

    • Up-regulation of B and D

    • Down-regulation of E

  • Enriched Gene Set Analysis

A

B

B

down-regulation

C

up-regulation

E

E

D

D

F

G

P

Possible activation of Pathway


Results

Results

  • For each pathway menu, one can:

    • find a list of proteins with associated roles for each node

    • look at each protein in an interaction

    • find a list of all interactions in a pathway


Conclusion

Conclusion

  • Successfully parse XML files

  • Pathway analysis program works

  • ~50% of pathways include inhibitors

  • 20% of the pathways contains >=5% of inhibitors

    • Average total molecules = 60


Future directions

Future Directions

  • Improvements to Software

    • Ambiguous roles

    • Proteins in different Complex may have different roles

    • Fine tune the overall role of proteins in each pathway

  • Run program with real expression data set

  • Improve prognoses and drugs for diseases

A

B

C

E

D

F

G

P


References

References

  • Pan KH, Lih Cj, Cohen SN. Effects of threshold choice on biological conclusions reached during analysis of gene expression by DNA microarrays. Proc Natl Acad Sci 2005, 102:8961-5.

  • Subramanian A, Tamayo P, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci 2005, 102:15545-50.

  • Nam D, Kim SY. Gene-set approach for expression pattern analysis. Brief Bioinform 2008, 9:189-97.

  • Dupuy A, Simon RM. Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. J Natl Cancer Inst 2007, 99:147-57.

  • Jiang Z, Gentleman R. Extensions to gene set enrichment. Bioinformatics 2007,23:306-13.

  • Dinu I, Potter JD, et al. Improving gene set analysis of microarray data by SAM-GS. BMC Bioinformatics 2007, 8:242.

  • Liu Q, Dinu I, et al. Comparative evaluation of gene-set analysis methods. BMC Bioinformatics 2007,8:431.


Acknowledgements

Acknowledgements

  • Mentor

    • Xiwei Wu

  • SoCalBSI Faculty and Staff

    • Jamil Momand

    • Sandy Sharp

    • Nancy Warter-Perez

    • Wendie Johnston

  • Funding for SoCalBSI:

    • DOE and NASA

    • LA / Orange County Biotechnology Center

    • NSF, NIH, and Economic & Workforce Development

  • Funding at City of Hope:

    • National Cancer Institute

    • National Institute of Health


  • Login