Abstract
Dynamic Programming (DP) based change-point methods have shown very good statistical performance on DNA copy number analysis. However, the quadratic algorithmic complexity of DP has limited their use on high-density arrays or next generation sequencing data. This complexity issue is particularly critical for segmentation and calling of segments, and for the joint segmentation of many different profiles. Our contribution is two-fold. First we provide an at worst linear DP algorithm for segmentation and calling, which allows the use of DP-based segmentation on high-density arrays with a considerably reduced computational cost. For the joint segmentation issue we provide a parallel version of the cghseg package which now allows us to analyze more than 1,000 profiles of length 100,000 within a few hours. Therefore our method and software package are adapted to the next generation of computers (multi-cores) and experiments (very large profiles).
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Rigaill, G., Miele, V., Picard, F. (2014). Fast and Parallel Algorithm for Population-Based Segmentation of Copy-Number Profiles. In: Formenti, E., Tagliaferri, R., Wit, E. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2013. Lecture Notes in Computer Science(), vol 8452. Springer, Cham. https://doi.org/10.1007/978-3-319-09042-9_18
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DOI: https://doi.org/10.1007/978-3-319-09042-9_18
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