Pattern Recognition and Interpretation in Sequence Data

随着近年来测序技术的进步，可用的生物序列数据量大大增加。从这些数据中提取知识已经落后，等待新的自动化方法从序列中提取意义的发展。该项目旨在开发快速灵活的算法，用于发现DNA和蛋白质序列数据中的模式，并找到具有相似模式的序列家族。这些模式与蛋白质家族的三维结构特征及其功能特征的关联有助于理解蛋白质的初级结构和功能之间的关系。

With the recent advances in sequencing technology, the amount of biological sequence data available has increased tremendously. Extraction of knowledge from such data has lagged behind, awaiting the development of new automated methods for extracting meaning from the sequences. This project aims to develop fast and flexible algorithms for discovery of patterns in DNA and protein sequence data and to find families of sequences that share similar patterns. Association of these patterns with features of 3-dimensional structures of protein families and their functional characteristics can contribute towards the understanding of the relationship between primary structure and function of a protein.