SIGMA: A Platform to
Visualize and Analyze DNA
Copy Number Microarray
Data
Raj Chari, PhD Student
BC Cancer Research Centre
Department of Cancer Genetics and Developmental Biology
APIII Conference, August 17th, 2006
Overview
DNA microarrays and array comparative
genomic hybridization (array CGH)
 Architecture of SIGMA
 Examples
 Current/Future directions

Studying DNA changes
Methods to study DNA aberrations are
getting better => movement to array-based
 Different from expression microarrays

 Measure
genomic content vs. RNA transcript
levels
 Dynamic range of values are much smaller
 Discrete vs. continuous data (segmentation
algorithms)
Array CGH Technology
Chari et al, Cancer Informatics, 2006, 2, 48-58
Rationale for SIGMA




Many different platforms for array CGH
Software developed tends to be platform-specific
Inefficient data processing pipeline
Need to encapsulate data processing and
support different types of data => System for
Integrative Genomic Microarray Analysis
(SIGMA)
Architecture of SIGMA
LOCAL
MySQL
Database
JDBC
SERVER
MySQL
Database
JDBC
Java
Application
JGR
R:
Analysis
Main interface
Functionalities of SIGMA


Importing data from multiple array CGH platforms
Built-in segmentation algorithms



DNACopy
Edge detection based Segmentation (Poster #105)
Integration with other types of DNA microarray-based
assays



Chromosome Immunoprecipitation on microarray chips (ChIP on
chip) (Poster #116) => Histone acetylation
Methylation Dependent Immunoprecipitation array CGH (MeDIP
array CGH) (Poster #120) => DNA methylation
Gene expression => RNA levels
Example: cancer cell line database
“stripped” down version of SIGMA
 database of pre-processed data
 Poster #104
 Case #1: Examining a single sample for
copy number aberrations
 Case #2: Identifying recurrent alterations
in lung adenocarcinoma

H2087 Lung cancer cell line
A. Whole genome
karyogram
B. Chromosome 8
C. Region on arm
8q
D. Highlight and
find genes
Segment & Curate changes
100% 50%
50% 100%
-1
+1
+1
+1
-1
+1
Individual Profile
Detection of
Alterations
Frequency of alterations
(aligning many profiles)
Summary of 24 Lung Adenocarcinomas
Current / Future Directions



Database of cancer cell lines will soon be
publicly available
Full application to be completed by October
Integration with proteomics
 DNA-RNA-Protein

Multi-dimensional views of the cell will enhance
understanding of pathogenesis => “Systems”
approach
Acknowledgements
Wan Lam lab
 Calum MacAulay
 Funding organizations:
