Biomedical subjects
Publications and source records attributed to A W Dress.
MOTIVATION: DCA is a new computer program for multiple sequence alignment which utilizes a 'divide-and-conquer' type of heuristic approach. AVAILABILITY: The algorithm is freely available from http://bibiserv.TechFak.Uni-Bielefeld.DE/dca/.
We have developed a fast heuristic algorithm for multiple sequence alignment which provides near-to-optimal results for sufficiently homologous sequences. The algorithm makes use of the standard dynamic programming procedure by applying it to all pairs of sequences. The resulting score matrices for pair-wise alignment give rise to secondary matrices containing the additional charges imposed by forcing the alignment path to run through a particular vertex. Such a constraint corresponds to slicing the sequences at the positions defining that vertex, and aligning the remaining pairs of prefix and suffix sequences separately. From these secondary matrices, one can compute-for any given family of sequences-suitable positions for cutting all of these sequences simultaneously, thus reducing the problem of aligning a family of n sequences of average length l in a Divide and Conquer fashion to aligning two families of n sequences of approximately half that length. In this paper, we explain the method for the case of 3 sequences in detail, and we demonstrate its potential and its limits by discussing its behaviour for several test families. A generalization for aligning more than 3 sequences is lined out, and some actual alignments constructed by our algorithm for various user-defined parameters are presented.
In order to analyze the structure inherent to a matrix of dissimilarities (such as evolutionary distances) we propose to use a new technique called split decomposition. This method accurately dissects the given dissimilarity measure as a sum of elementary "split" metrics plus a (small) residue. The split summands identify related groups which are susceptible to further interpretation when casted against the available biological information. Reanalysis of previously published ribosomal RNA data sets using split decomposition illustrate the potential of this approach.
A new and apparently rather useful and natural concept in cluster analysis is studied: given a similarity measure on a set of objects, a sub-set is regarded as a cluster if any two objects a, b inside this sub-set have greater similarity than any third object outside has to at least one of a, b. These clusters then form a closure system which can be described as a hypergraph without triangles. Conversely, given such a system, one may attach some weight to each cluster and then compose a similarity measure additively, by letting the similarity of a pair be the sum of weights of the clusters containing that particular pair. The original clusters can be reconstructed from the obtained similarity measure. This clustering model is thus located between the general additive clustering model of Shepard and Arabie (1979) and the standard hierarchical model. Potential applications include fitting dendrograms with few additional nonnested clusters and simultaneous representation of some families of multiple dendrograms (in particular, two-dendrogram solutions), as well as assisting the search for phylogenetic relationships by proposing a somewhat larger system of possibly relevant "family groups", from which an appropriate choice (based on additional insight or individual preferences) remains to be made.