Design of an incremental clustering package for protein function and family analysis

Chien-Yu Chen, Hsueh‐Fen Juan, Po-Jen Hsiao, Shui-Tein Chen, Hsiang-Wen Tseng, Yen-Jen Oyang
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Abstract

Protein clustering has been widely exploited to facilitate in-depth analysis of protein functions and families. We discuss the design of an incremental protein clustering package that provides comprehensive features for protein function and family analysis. Specifically, the package offers alternative options for carrying out high-quality protein clustering from different aspects. The incremental nature of the clustering algorithm is essential for efficient analysis of those contemporary protein databases whose sizes are growing rapidly. Concerning the quality of clustering results, experimental results from applying the incremental clustering algorithm to protein sequence analysis show that the incremental algorithm is able to identify protein sequence clusters that match protein families more consistently than the single-link algorithm, which is the most widely used hierarchical clustering algorithm for protein sequence analysis. We also address the implementation techniques employed to improve the system performance.
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一个用于蛋白质功能和家族分析的增量聚类包的设计
蛋白质聚类已被广泛用于蛋白质功能和家族的深入分析。我们讨论了一个增量蛋白质聚类包的设计,提供了蛋白质功能和家族分析的综合功能。具体来说,该软件包提供了从不同方面进行高质量蛋白质聚类的替代选项。聚类算法的增量特性对于快速增长的现代蛋白质数据库的有效分析至关重要。在聚类结果的质量方面,将增量聚类算法应用于蛋白质序列分析的实验结果表明,增量聚类算法能够比单链接算法更一致地识别出与蛋白质家族匹配的蛋白质序列聚类,单链接算法是蛋白质序列分析中应用最广泛的分层聚类算法。我们还讨论了用于改进系统性能的实现技术。
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