title: pyProCT: Automated Cluster Analysis for Structural Bioinformatics.
abstract: Cluster analysis is becoming a relevant tool in structural bioinformatics. It allows analyzing large conformational ensembles in order to extract features or diminish redundancy, or just as a first step for other methods. Unfortunately, the successfulness of this analysis strongly depends on the data set traits, the chosen algorithm, and its parameters, which can lead to poor or even erroneous results not easily detected. In order to overcome this problem, we have developed pyProCT, a Python open source cluster analysis toolkit specially designed to be used with ensembles of biomolecule conformations. pyProCT implements an automated protocol to choose the clustering algorithm and parameters that produce the best results for a particular data set. It offers different levels of customization according to users' expertise. Moreover, pyProCT has been designed as a collection of interchangeable libraries, making it easier to reuse it as part of other programs.
authors.affiliation: Joint BSC-CRG-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, Jordi Girona 29, 08034 Barcelona, Spain.
authors.forename: Víctor A
authors.initials: VA
authors.lastname: Gil
authors.affiliation: Joint BSC-CRG-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, Jordi Girona 29, 08034 Barcelona, Spain.|Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, E-08010 Barcelona, Spain.
authors.forename: Víctor
authors.initials: V
authors.lastname: Guallar