Array-based Genome Comparison of Arabidopsis Ecotypes using Hidden Markov Models
نویسندگان
چکیده
Abstract: Arabidopsis thaliana is an important model organism in plant biology with a broad geographic distribution including ecotypes from Africa, America, Asia, and Europe. The natural variation of different ecotypes is expected to be reflected to a substantial degree in their genome sequences. Array comparative genomic hybridization (Array-CGH) can be used to quantify the natural variation of different ecotypes at the DNA level. Besides, such Array-CGH data provides the basics to establish a genome-wide map of DNA copy number variation for different ecotypes. Here, we present a new approach based on Hidden Markov Models (HMMs) to predict copy number variations in Array-CGH experiments. Using this approach, an improved genome-wide characterization of DNA segments with decreased or increased copy numbers is obtained in comparison to the routinely used segMNT algorithm. The software and the data set used in this case study can be downloaded from http://dig.ipk-gatersleben.de/HMMs/ACGH/ACGH.html.
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Parsimonious Higher-Order Hidden Markov Models for Improved Array-CGH Analysis with Applications to Arabidopsis thaliana
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