Development of a normalization algorithm for array comparative genomic hybridization

Genomic instability is one of fundamental factors in tumorigenesis and tumor progression. Many studies have demonstrated that copy-number abnormalities at the DNA level are important in the pathogenesis of cancer. Array Comparative Genomic Hybridization (aCGH), developed based on expression microarray technology, can reveal the chromosomal aberrations in segmental copy at a high-resolution. However, due to the nature of aCGH, many standard expression data processing tools, such as data normalization, often failed to yield satisfactory results. The proposed study demonstrated a novel aCGH normalization procedure, which provides an accurate aCGH data normalization by utilizing the dependency of neighboring probe measurements in aCGH experiments. To facilitate the study, we have developed a Hidden Markov Model (HMM) to simulate a series of aCGH experiment with random DNA copy number alteration. Furthermore, based on this new development, we will establish a user-friendly web system in order to provide convenient aCGH analysis.