  Motivation: Array Comparative Genomic Hybridization (aCGH) is used to scan
the entire genome for variations in DNA copy number. A central task in the
analysis of aCGH data is the segmentation into groups of probes sharing the
same DNA copy number. Some well known segmentation methods suffer from very
long running times, preventing interactive data analysis. Results: We suggest a
new segmentation method based on wavelet decomposition and thresholding, which
detects significant breakpoints in the data. Our algorithm is over 1,000 times
faster than leading approaches, with similar performance. Another key advantage
of the proposed method is its simplicity and flexibility. Due to its intuitive
structure it can be easily generalized to incorporate several types of side
information. Here we consider two extensions which include side information
indicating the reliability of each measurement, and compensating for a changing
variability in the measurement noise. The resulting algorithm outperforms
existing methods, both in terms of speed and performance, when applied on real
high density CGH data. Availability: Implementation is available under software
tab at: http://www.ee.technion.ac.il/Sites/People/YoninaEldar/ Contact:
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