Bayesian alignment using hierarchical models, with applications in protein bioinformatics

Peter J. Green (Bristol) & Kanti Mardia (Leeds)
An important problem in shape analysis is to match configurations of points in space filtering out some geometrical transformation. In this paper we introduce hierarchical models for such tasks, in which the points in the configurations are either unlabelled, or have at most a partial labelling constraining the matching, and in which some points may only appear in one of the configurations. We derive procedures for simultaneous inference about the matching and the transformation, using a Bayesian approach. Our model is based on a Poisson process for hidden true point locations; this leads to considerable mathematical simplification and efficiency of implementation. We find a novel use for classic distributions from directional statistics in a conditionally conjugate specification for the case where the geometrical transformation includes an unknown rotation. Throughout, we focus on the case of affine or rigid motion transformations. Under a broad parametric family of loss functions, an optimal Bayesian point estimate of the matching matrix can be constructed, that depends only on a single parameter of the family.

Our methods are illustrated by two applications from bioinformatics. The first problem is of matching protein gels in 2 dimensions, and the second consists of aligning active sites of proteins in 3 dimensions. In the latter case, we also use information related to the grouping of the amino acids. We discuss some open problems and suggest directions for future work.

Keywords: bioinformatics, Markov chain Monte Carlo, matching, Poisson process, protein gels, protein structure, shape analysis, von Mises-Fisher distribution.

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