Unsupervised identification of redundant domain entries in InterPro database using clustering techniques

InterPro is a widely used database that integrates functional signatures provided by different protein sequence annotation databases with manual curation; in order to present a comprehensive database of functional sequence annotation. However, the integration of the signatures causes inconsistent and/or redundant annotations in some cases. In this study, we proposed an unsupervised method for the automatic detection of inconsistent and redundant entries in the InterPro database. Two clustering methods: Markov Cluster Algorithm (MCL) and hierarchical clustering are employed in order to investigate to what extent these signatures can be detected. Results show that a considerable amount of (~75%) redundant entries can be identified. The future goal is to develop a system that does the identification of redundant and inconsistent signatures with very high performance using machine learning techniques in a supervised fashion. The findings of the study may aid InterPro curators to fix the problematic entries. It may also be used by curators as a road map before the integration of new signatures.


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Citation Formats
A. S. Rifaioğlu and T. Can, “Unsupervised identification of redundant domain entries in InterPro database using clustering techniques,” 2015, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/31766.