Partitioning clustering algorithms for protein sequence data sets

<p>Abstract</p> <p>Background</p> <p>Genome-sequencing projects are currently producing an enormous amount of new sequences and cause the rapid increasing of protein sequence databases. The unsupervised classification of these data into functional groups or families, clustering, has become one of the principal research objectives in structural and functional genomics. Computer programs to automatically and accurately classify sequences into families become a necessity. A significant number of methods have addressed the clustering of protein sequences and most of them can be categorized in three major groups: hierarchical, graph-based and partitioning methods. Among the various sequence clustering methods in literature, hierarchical and graph-based approaches have been widely used. Although partitioning clustering techniques are extremely used in other fields, few applications have been found in the field of protein sequence clustering. It is not fully demonstrated if partitioning methods can be applied to protein sequence data and if these methods can be efficient compared to the published clustering methods.</p> <p>Methods</p> <p>We developed four partitioning clustering approaches using Smith-Waterman local-alignment algorithm to determine pair-wise similarities of sequences. Four different sets of protein sequences were used as evaluation data sets for the proposed methods.</p> <p>Results</p> <p>We show that these methods outperform several other published clustering methods in terms of correctly predicting a classifier and especially in terms of the correctness of the provided prediction. The software is available to academic users from the authors upon request.</p

Mining reference process models and their configurations

Reference process models are templates for common processes run by many corporations. However, the individual needs among organizations on the execution of these processes usually vary. A process model can address these variations through control-flow choices. Thus, it can integrate the different process variants into one model. Through configuration parameters, a configurable reference models enables corporations to derive their individual process variant from such an integrated model. While this simplifies the adaptation process for the reference model user, the construction of a configurable model integrating several process variants is far more complex than the creation of a traditional reference model depicting a single best-practice variant. In this paper we therefore recommend the use of process mining techniques on log files of existing, well-running IT systems to help the reference model provider in creating such integrated process models. Afterwards, the same log files are used to derive suggestions for common configurations that can serve as starting points for individual configurations