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Multiple Break-Points Detection in Array CGH Data via the Cross-Entropy Method

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Multiple Break-Points Detection in Array CGH Data via the Cross-Entropy Method


Array comparative genome hybridization (aCGH) is a widely used methodology to detect copy variety variations of a genome in high resolution. Knowing the amount of break-points and their corresponding locations in genomic sequences serves totally different biological desires. Primarily, it helps to identify disease-inflicting genes that have functional importance in characterizing genome wide diseases. For human autosomes the traditional copy range is 2, whereas at the sites of oncogenes it increases (gain of DNA) and at the tumour suppressor genes it decreases (loss of DNA). The majority of this detection strategies are deterministic in their set-up and use dynamic programming or different smoothing techniques to obtain the estimates of copy variety variations. These approaches limit the search space of the matter due to totally different assumptions considered within the ways and don't represent the true nature of the uncertainty associated with the unknown break-points in genomic sequences. We tend to propose the Cross-Entropy technique, that is a model-based mostly stochastic optimization technique as an actual search technique, to estimate each the amount and locations of the break-points in aCGH knowledge. We tend to model the continual scale log-ratio information obtained by the aCGH technique as a multiple break-point drawback. The proposed methodology is compared with well established publicly obtainable strategies using both artificially generated information and real information. Results show that the proposed procedure is an effective way of estimating variety and especially the locations of break-points with high level of precision. Availability: The ways described in this article are implemented within the new R package breakpoint and it is available from the Comprehensive R Archive Network at

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Multiple Break-Points Detection in Array CGH Data via the Cross-Entropy Method - 4.7 out of 5 based on 68 votes

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