SIGMA: A Platform to Visualize and Analyze DNA Copy Number Microarray Data

Raj Chari, PhD StudentBC Cancer Research CentreDepartment of Cancer Genetics and Developmental BiologyAPIII 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 microarraysMeasure genomic content vs. RNA transcript

levelsDynamic range of values are much smallerDiscrete 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

MySQL Database

SERVER

MySQL Database

LOCAL

R: Analysis

Java Application

JDBC

JGR

JDBC

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% 100%50% 50%

-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: