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DNA Copy number variation (CNV) has recently gained considerable interest asa source of genetic variation that likely influences phenotypic differences.Many statistical and computational methods have been proposed and applied todetect CNVs based on data that generated by genome analysis platforms. However,most algorithms are computationally intensive with complexity at least$O(n^2)$, where $n$ is the number of probes in the experiments. Moreover, thetheoretical properties of those existing methods are not well understood. Afaster and better characterized algorithm is desirable for the ultra highthroughput data. In this study, we propose the Screening and Ranking algorithm(SaRa) which can detect CNVs fast and accurately with complexity down to O(n).In addition, we characterize theoretical properties and present numericalanalysis for our algorithm.

The screening and ranking algorithm to detect DNA copy number variations