Integer linear programming as a tool for constructing trees from quartet data.

The task of the quartet puzzling problem is to find a best-fitting binary X-tree for a finite n-set from confidence values for the 3n4 binary trees with exactly four leaves from X, its fitness being measured by the sum of the confidence values of all "induced" four-leaves subtrees. We describe a method for finding an exact solution of this problem by integer linear programming. Similar procedures can also be used for finding, e.g. best-fitting "circular" networks. A crucial problem in this context is, of course, how to obtain the input confidence values for the quartet trees. We propose to use inner products of rate-matrix diagonals calculated for pairs of taxa and present the trees resulting from applying our approach to two data sets of up to 36 mitochondrial sequences of mammals including an outgroup.

DIALIGN-T: an improved algorithm for segment-based multiple sequence alignment.

BACKGROUND: We present a complete re-implementation of the segment-based approach to multiple protein alignment that contains a number of improvements compared to the previous version 2.2 of DIALIGN. This previous version is superior to Needleman-Wunsch-based multi-alignment programs on locally related sequence sets. However, it is often outperformed by these methods on data sets with global but weak similarity at the primary-sequence level. RESULTS: In the present paper, we discuss strengths and weaknesses of DIALIGN in view of the underlying objective function. Based on these results, we propose several heuristics to improve the segment-based alignment approach. For pairwise alignment, we implemented a fragment-chaining algorithm that favours chains of low-scoring local alignments over isolated high-scoring fragments. For multiple alignment, we use an improved greedy procedure that is less sensitive to spurious local sequence similarities. To evaluate our method on globally related protein families, we used the well-known database BAliBASE. For benchmarking tests on locally related sequences, we created a new reference database called IRMBASE which consists of simulated conserved motifs implanted into non-related random sequences. CONCLUSION: On BAliBASE, our new program performs significantly better than the previous version of DIALIGN and is comparable to the standard global aligner CLUSTAL W, though it is outperformed by some newly developed programs that focus on global alignment. On the locally related test sets in IRMBASE, our method outperforms all other programs that we evaluated.

Multiple sequence alignment with user-defined constraints at GOBICS.

Most multi-alignment methods are fully automated, i.e. they are based on a fixed set of mathematical rules. For various reasons, such methods may fail to produce biologically meaningful alignments. Herein, we describe a semi-automatic approach to multiple sequence alignment where biological expert knowledge can be used to influence the alignment procedure. The user can specify parts of the sequences that are biologically related to each other; our software program uses these sites as anchor points and creates a multiple alignment respecting these user-defined constraints. By using known functionally, structurally or evolutionarily related positions of the input sequences as anchor points, our method can produce alignments that reflect the true biological relationships among the input sequences more accurately than fully automated procedures can do.