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Biomedical subjects

J Stoye

Publications and source records attributed to J Stoye.

9 recordsLinked to original sources
PubMed1998

Multiple sequence alignment with the Divide-and-Conquer method.

An improved algorithm for the simultaneous alignment of multiple protein and nucleic acid sequences, the Divide-and-Conquer Alignment procedure (DCA), is presented. The basic method described in Tönges,et al. (1996) (Tönges, U., Perrey, S.W., Stoye, J., Dress, A.W.M., 1996. A general method for fast multiple sequence alignment. Gene, 172, GC33-GC41) is generalized to align any number of sequences to work arbitrary (e.g. affine linear) gap penalty functions. Also, the practical efficiency of the method is improved so that families of more than 10 sequences can now be aligned simultaneously within a few seconds or minutes. After a brief description of the general method, we assess the time and memory requirements of our implementation of DCA. We present several examples showing that the program is able to deal with real-world alignment problems.

Algorithms↗
PubMed1998

Rose: generating sequence families.

MOTIVATION: We present a new probabilistic model of the evolution of RNA-, DNA-, or protein-like sequences and a software tool, Rose, that implements this model. Guided by an evolutionary tree, a family of related sequences is created from a common ancestor sequence by insertion, deletion and substitution of characters. During this artificial evolutionary process, the 'true' history is logged and the 'correct' multiple sequence alignment is created simultaneously. The model also allows for varying rates of mutation within the sequences, making it possible to establish so-called sequence motifs. RESULTS: The data created by Rose are suitable for the evaluation of methods in multiple sequence alignment computation and the prediction of phylogenetic relationships. It can also be useful when teaching courses in or developing models of sequence evolution and in the study of evolutionary processes. AVAILABILITY: Rose is available on the Bielefeld Bioinformatics WebServer under the following URL: http://bibiserv.TechFak.Uni-Bielefeld.DE/rose/ The source code is available upon request. CONTACT: folker@TechFak.Uni-Bielefeld.DE

Algorithms↗
PubMed1997

Generating benchmarks for multiple sequence alignments and phylogenetic reconstructions.

We present a new probabilistic model of evolution of RNA-, DNA-, or protein-like sequences and a tool rose that implements this model. By insertion, deletion and substitution of characters, a family of sequences is created from a common ancestor. During this artificial evolutionary process, the "true" history is logged and the "correct" multiple sequence alignment is created simultaneously. We also allow for varying rates of mutation within the sequences making it possible to establish so-called sequence motifs. The results are suitable for the evaluation of methods in multiple sequence alignment computation and the prediction of phylogenetic relationships.

Amino Acid Sequence↗
PubMed1996

A general method for fast multiple sequence alignment.

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.

Algorithms↗