Statistical inference of chromosomal homology based on gene colinearity and applications to Arabidopsis and rice

Background: The identification of chromosomal homology will shed light on such mysteries of genome evolution as DNA duplication, rearrangement and loss. Several approaches have been developed to detect chromosomal homology based on gene synteny or colinearity. However, the previously reported implementations lack statistical inferences which are essential to reveal actual homologies. Results: In this study, we present a statistical approach to detect homologous chromosomal segments based on gene colinearity. We implement this approach in a software package ColinearScan to detect putative colinear regions using a dynamic programming algorithm. Statistical models are proposed to estimate proper parameter values and evaluate the significance of putative homologous regions. Statistical inference, high computational efficiency and flexibility of input data type are three key features of our approach. Conclusion: We apply ColinearScan to the Arabidopsis and rice genomes to detect duplicated...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Xiyin Wang, Xiaoli Shi, Zhe Li, Qihui Zhu, Lei Kong, Wen Tang, Song Ge, Jingchu Luo
• Keywords: BMCBI 2006, Chromosomal Homology, Genome, Homologous Chromosomal Segments