A Protein Sequence Clustering Algorithm Based on Statistical Models

• Main Authors: Wen-Chin Chung, 鐘文欽
• Other Authors: Yen-Jen Oyang
• Format: Others
• Language: zh-TW
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/53565740661331487469

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 92 === Protein sequence clustering can group the homologous proteins together based on pair-wise sequence similarities. The conventional single-linkage clustering algorithm has been widely used on this problem because it successfully utilizes the transitivity property to identify remote homologues and provides a dendrogram as clustering result that is useful for protein family analysis. However, due to the twilight zone embedded in the distribution of pair-wise similarities, sometimes the single-linkage algorithm generates clusters with low sensitivity for large families or families with noisy relationships to the members of other protein families. In this thesis, a hybrid hierarchical clustering algorithm is proposed to improve the quality of a dendrogram generated by the single-linkage clustering algorithm. By creating pair clusters, a single protein can exist in distinct hierarchical paths of a dendrogram. Next, the proposed algorithm employs the skewness and kurtosis indices to control the formation of subclusters, in order to generate highly homologous clusters at the bottom level of a dendrogram. Finally, selecting pivots of a subcluster in the following clustering process avoids the chaining effect it might be caused by the single-linkage algorithm. Thus the proposed algorithm can produce clusters with both high sensitivity and specificity at the higher level of a dendrogram. The experimental results in this thesis showed that the hierarchy outputted by the proposed algorithm matches the hierarchy of protein families better than the hierarchy generated by the single-linkage algorithm. In this regard, the generated hierarchy can provide automatic annotations for new protein with higher accuracy than the previous approaches.