Related Experiment Video
Updated: Jun 23, 2026
06:17
Analysis of Multidimensional Microscopy Data Using Cell-ACDC
Published on: November 7, 2025
Overlay tool for aCGHViewer: an analysis module built for aCGHViewer used to perform comparisons of data derived from
Ken C Lo1, Ganesh Shankar, Yaron Turpaz
1Department of Cancer Genetics, Roswell Park Cancer Institute, Buffalo, NY 14263, USA.
Cancer Informatics
|May 21, 2009
Summary
The Overlay Tool integrates diverse microarray data to correlate gene expression with genomic changes like copy number abnormalities (CNAs) and loss of heterozygosity (LOH). This facilitates identifying cancer-related genes affected by genomic alterations.
Area of Science:
- Genomics
- Bioinformatics
- Cancer Research
Background:
- High-throughput microarray data from various platforms present integration challenges.
- Understanding the interplay between gene expression and genomic alterations (CNAs, LOH) is crucial in cancer research.
Purpose of the Study:
- To develop a computational tool, the Overlay Tool, for integrating and analyzing multi-platform microarray data.
- To identify genes with altered expression due to genomic changes in cancer.
Main Methods:
- Developed the Overlay Tool to computationally combine high-throughput datasets (e.g., aCGH and gene expression).
- Implemented an overlay analysis to correlate gene expression changes with copy number abnormalities (CNAs) and loss of heterozygosity (LOH).
- Utilized a gene-centric approach for data visualization and interrogation, compatible with various microarray platforms.
Main Results:
- The Overlay Tool successfully integrates data from multiple microarray platforms without remapping.
- Identified correlations between gene expression profiles and genomic alterations (gains, losses, amplifications).
- Incorporated loss of heterozygosity (LOH) probability data into the analysis.
Conclusions:
- The Overlay Tool provides a unified platform for analyzing complex genomic and transcriptomic data.
- Facilitates the identification of key genes affected by cancer-associated genomic instability.
- Enables efficient investigation of genes of interest through visualization tools and public database integration.
Related Concept Videos
01:11Genome-wide Association Studies-GWAS
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
GWAS does not require the identification of the target gene involved in...
02:26Comparing Copy Number Variations and SNPs
Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...