CGHScan: A Tool for Analyzing Genomic Variability
Author Information
Author(s): Bradley D Anderson, Michael C Gilson, Abigail A Scott, Bryan S Biehl, Jeremy D Glasner, Gireesh Rajashekara, Gary A Splitter, Nicole T Perna
Primary Institution: University of Wisconsin, Madison
Hypothesis
Can CGHScan effectively identify variable genomic regions using high-density microarray comparative genomic hybridization data?
Conclusion
CGHScan is an effective tool for analyzing comparative genomic hybridization data from high-density microarrays.
Supporting Evidence
- CGHScan identified eight of nine known deletions in a Brucella ovis strain compared to Brucella melitensis.
- The algorithm is tolerant of high noise in measurements.
- CGHScan successfully identified variable regions across different normalization methods.
Takeaway
CGHScan helps scientists find differences in DNA between two organisms by looking at their genetic material closely, even when the data is noisy.
Methodology
The algorithm uses an iterative random walk approach with multi-layered significance testing to detect variable regions in genomic data.
Limitations
The algorithm may miss very small deletions that are below its resolution.
Statistical Information
P-Value
0.05
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
Want to read the original?
Access the complete publication on the publisher's website