Effects of topography on dispersal of black-billed magpie Pica pica sericea revealed by population genetic analysis

This study was conducted using random amplified polymorphic DNA analysis to examine whether the effects of topographical structure on the dispersal of black-billed magpie Pica pica sericea are reflected in its population genetic structure. The black-billed magpie is typically seen in the lowlands and is remarkably sedentary in Korea and Japan. The unweighted pair-group method of clustering analysis showed two main clusters: five populations in the western region of the Bekdudegan mountains (WRBM) in one cluster and five populations in the eastern region of the Bekdudegan mountains (ERBM), including the Japanese population in the other cluster. The populations in WRBM appeared to be diverged from the populations in ERBM. Analysis of molecular variance revealed that the populations in ERBM were more genetically divergent from each other than were those in WRBM. The high-rising mountains, very rugged topographical features and the sea in ERBM may have resulted in a lower dispersal rate and larger genetic variation among populations in ERBM compared to those in WRBM. These results suggest that the topographical structure may have an influence on the dispersal and population genetic structure of the black-billed magpie. Electronic Publication     

A computational approach for the classification of protein tyrosine kinases

Protein tyrosine kinases (PTKs) play a central role in the modulation of a wide variety of cellular events such as differentiation, proliferation and metabolism, and their unregulated activation can lead to various diseases including cancer and diabetes. PTKs represent a diverse family of proteins including both receptor tyrosine kinases (RTKs) and non-receptor tyrosine kinases (NRTKs). Due to the diversity and important cellular roles of PTKs, accurate classification methods are required to better understand and differentiate different PTKs. In addition, PTKs have become important targets for drugs, providing a further need to develop novel methods to accurately classify this set of important biological molecules. Here, we introduce a novel statistical model for the classification of PTKs that is based on their structural features. The approach allows for both the recognition of PTKs and the classification of RTKs into their subfamilies. This novel approach had an overall accuracy of 98.5% for the identification of PTKs, and 99.3% for the classification of RTKs.