An Ant Colony Optimization Algorithm for DNA Copy Number Analysis in Array CGH Data

In this paper, the self organization properties of ant colonies are employed to tackle the problem of DNA copy number analysis in array CGH data, which can reveal chromosomal aberrations in the genomic DNA. These amplifications and deletions may be crucial events in the development and progression of cancer and other diseases. Accurately identifying the recurrent aberration at a particular genome location is important to find the possibly damaged genes. Unfortunately, it is difficult to exactly detect the boundaries of aberration from array CGH data with low signal-to-noise ratio. The presented ant colony optimization algorithm represents the problem as a directed graph such that the objective of the original problem becomes to find the shortest path on the graph under the problem-specific constraints. A number of artificial ants are distributed on the graph and communicate with one another through the pheromone trails which are a form of the long-term memory guiding the future exploration of the graph. The important properties of the proposed method are thoroughly investigated. The performance of the proposed method as compared to those of the state-of-the-art methods is very promising.