A network of protein-protein interactions in yeast.

A global analysis of 2,709 published interactions between proteins of the yeast Saccharomyces cerevisiae has been performed, enabling the establishment of a single large network of 2,358 interactions among 1,548 proteins. Proteins of known function and cellular location tend to cluster together, with 63% of the interactions occurring between proteins with a common functional assignment and 76% occurring between proteins found in the same subcellular compartment. Possible functions can be assigned to a protein based on the known functions of its interacting partners. This approach correctly predicts a functional category for 72% of the 1,393 characterized proteins with at least one partner of known function, and has been applied to predict functions for 364 previously uncharacterized proteins.

Universal DNA tag systems: a combinatorial design scheme.

Custom-designed DNA arrays offer the possibility of simultaneously monitoring thousands of hybridization reactions. These arrays show great potential for many medical and scientific applications, such as polymorphism analysis and genotyping. Relatively high costs are associated with the need to specifically design and synthesize problem-specific arrays. Recently, an alternative approach was suggested that utilizes fixed, universal arrays. This approach presents an interesting design problem-the arrays should contain as many probes as possible, while minimizing experimental errors caused by cross-hybridization. We use a simple thermodynamic model to cast this design problem in a formal mathematical framework. Employing new combinatorial ideas, we derive an efficient construction for the design problem and prove that our construction is near-optimal.

An exact algorithm to identify motifs in orthologous sequences from multiple species.

The identification of sequence motifs is a fundamental method for suggesting good candidates for biologically functional regions such as promoters, splice sites, binding sites, etc. We investigate the following approach to identifying motifs: given a collection of orthologous sequences from multiple species related by a known phylogenetic tree, search for motifs that are well conserved (according to a parsimony measure) in the species. We present an exact algorithm for solving this problem. We then discuss experimental results on finding promoters of the rbcS gene for a family of 10 plants, on finding promoters of the adh gene for 12 Drosophila species, and on finding promoters of several chloroplast encoded genes.

The deferred path heuristic for the generalized tree alignment problem.

Many multiple alignment methods implicitly or explicitly try to minimize the amount of biological change implied by an alignment. At the level of sequences, biological change is measured along a phylogenetic tree, a structure frequently being predicted only after the multiple alignment instead of together with it. The Generalized Tree Alignment problem addresses both questions simultaneously. It can formally be viewed as a Steiner tree problem in sequence space and our approach merges a path heuristic for the construction of a Steiner tree with a clustering method as usually applied only to distance data. This combination is achieved using sequence graphs, a data structure for efficient representation of similar sequences. Although somewhat slower in practice than an earlier method by Hein (1989) the current approach achieves significantly better results in terms of the underlying scoring function. Furthermore, a variant of the algorithm is introduced that maintains a guaranteed error bound of (2 - 2/n) for n sequences.