The basic unit of eukaryotic chromatin is the nucleosome, consisting of about 150 by of DNA wrapped around a protein core made of histone proteins. Nucleosomes position is modulated in vivo to regulate fundamental nuclear processes. To measure nucleosome positions on a genomic scale both theoretical and experimental approaches have been recently reported. We have developed a new method, Multi-Layer Model (MLM), for the analysis of nucleosome position data obtained with microarray-based approach. The MLM is a feature extraction method in which the input data is processed by a classifier to distinguish between several kinds of patterns. We applied our method to simulated-synthetic and experimental nucleosome position data and found that besides a high nucleosome recognition and a strong agreement with standard statistical methods, the MLM can identify distinct classes of nucleosomes, making it an important tool for the genome wide analysis of nucleosome position and function. In conclusion, the MLM allows a better representation of nucleosome position data and a significant reduction in computational time.
Di Gesù, V., Lo Bosco, G., Pinello, L., Yuan, G.C., Corona, D. (2009). A Multi-Layer Method to Study Genome-Scale Positions of Nucleosomes. GENOMICS, 93(2), 140-145 [10.1016/j.ygeno.2008.09.012].
A Multi-Layer Method to Study Genome-Scale Positions of Nucleosomes
DI GESU', Vito;LO BOSCO, Giosue'
;PINELLO, Luca;CORONA, Davide
2009-01-01
Abstract
The basic unit of eukaryotic chromatin is the nucleosome, consisting of about 150 by of DNA wrapped around a protein core made of histone proteins. Nucleosomes position is modulated in vivo to regulate fundamental nuclear processes. To measure nucleosome positions on a genomic scale both theoretical and experimental approaches have been recently reported. We have developed a new method, Multi-Layer Model (MLM), for the analysis of nucleosome position data obtained with microarray-based approach. The MLM is a feature extraction method in which the input data is processed by a classifier to distinguish between several kinds of patterns. We applied our method to simulated-synthetic and experimental nucleosome position data and found that besides a high nucleosome recognition and a strong agreement with standard statistical methods, the MLM can identify distinct classes of nucleosomes, making it an important tool for the genome wide analysis of nucleosome position and function. In conclusion, the MLM allows a better representation of nucleosome position data and a significant reduction in computational time.File | Dimensione | Formato | |
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