Summary: Affinity propagation (AP) clustering has recently gained increasing popularity in bioinformatics. AP clustering has the advantage that it allows for determining typical cluster members, the so-called exemplars. We provide an R implementation of this promising new clustering technique to account for the ubiquity of R in bioinformatics. This article introduces the package and presents an application from structural biology. Availability: The R package apcluster is available via CRAN-The Comprehensive R Archive Network: http://cran.r-project.org/web/packages/apcluster

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Support vector machines (SVMs) are well-established standard methods for classifying biological sequences. Advantages of SVMs [2,8]:• Maximizing the margin between two classes → proven to be a near-optimal learning strategy.• Optimization problem is convex and quadratic → global solution exists and can be found efficiently.• Only depend on very few hyperparameters → easier model selection.• Can be applied to any kind of data; all needed is a meaningful positive semi-definite comparison measure (the so-called kernel) → great advantage for sequences (cannot always be cast into vectorial data)SVMs in a Nutshell. Consider training data {(x i , y i ) | i =1,…,l}, where x i are sequences and y i ∈ {-1,+1} are binary labels. Discriminant function of SVM:x: new data item to be classified; α i : weights determined by SVM training (Lagrange multipliers); k(.,.): kernel function.Sequence Kernels. Wide range available [9], many of which can be expressed as [1] P: set of sequence patterns; N(p,x): number of occurrences/matches of pattern p in sequence x. This formulation includes the well-known spectrum kernel [6], the mismatch kernel [5], and the spatial sample kernel [4]. To correct for varying sequence lengths, it is often useful to normalize the kernel [9]: