Introduction The modern era of molecular biology began with the discovery of the double helical structure of DNA. Today, sequencing nucleic acids, the determination of genetic information at the most fundamental level, is a major tool of biological research [79]. This revolution in biology has created a huge amount of data at great speed by directly reading DNA sequences. The growth rate of data volume is exponential. For instance, the volume of DNA and protein sequence data is currently doubling every 22 months [55]. One important reason for this exceptional growth rate of biological data is the medical use of such information in the design of diagnostics and therapeutics [24, 50]. For example, identification of genetic markers in DNA sequences would provide Supported in part by Hong Kong Research Council. important informations regarding which portions of the DNA are significant, and would allow the researchers to find many disease genes of

We present a novel technique for improving a fundamental aspect of iterated dynamic programming procedures on sequences, such as progressive sequence alignment.Instead of relying on the unrealistic assumption that each iteration can be performed accurately without including information from other sequences, our technique employs the combinatorial data structure of weighted sequence graphs to represent an exponential number of optimal and suboptimal sequences.The usual dynamic programming algorithm on linear sequences can be generalized to weighted sequence graphs, and therefore allows to align sequence graphs instead of individual sequences in subsequent stages.Thus, locally suboptimal, but globally correct solutions can for the first time be identified through iterated sequence alignment.We demonstrate the utility of our technique by applying it to the benchmark alignment problem of Sank o# et al. (1976).Although a recent e#ort could improve on the original solution from 1976 slightly, our technique leads to even more significant improvements. Keywords: Computational Molecular Biology, algorithms, multiple alignment, evolutionary tree, phylogenetic tree, Tree alignment problem, dynamic programming 1 Introducti Aligning two or more sequences is one of the most frequently performed task in the analysis of biomolecular sequences.Most methods for sequence alignment build on the fact that alignments correspond to source-to-sink paths in the acyclic edi t graph [MM88, Gus97].In this graph, short paths are assumed to correspond to relevant alignments.Current methods mostly compute a single shortest path through the edit graph.Although linear-time algorithms for computing a shortest path in a directed acyclic graph are simple, in practical Corresponding author. E-mail: b...

Motivation We want to provide biologists with a fast and sensitive scanning tool for searching local alignments of a protein query sequence against databases of protein multiple alignments such as ProDom. Reversely, we want to provide a tool for locally aligning a protein multiple alignment query against a protein database such as SWISSPROT.

Background: The superfamily Pterioidea is a morphologically and ecologically diverse lineage of epifaunal marine bivalves distributed throughout the tropical and subtropical continental shelf regions. This group includes commercially important pearl culture species and model organisms used for medical studies of biomineralization. Recent morphological treatment of selected pterioideans and molecular phylogenetic analyses of higher-level relationships in Bivalvia have challenged the traditional view that pterioidean families are monophyletic. This issue is examined here in light of molecular data sets composed of DNA sequences for nuclear and mitochondrial loci, and a published character data set of anatomical and shell morphological characters. Results: The present study is the first comprehensive species-level analysis of the Pterioidea to produce a wellresolved, robust phylogenetic hypothesis for nearly all extant taxa. The data were analyzed for potential biases due to taxon and character sampling, and idiosyncracies of different molecular evolutionary processes. The congruence and contribution of different partitions were quantified, and the sensitivity of clade stability to alignment parameters was explored. Conclusions: Four primary conclusions were reached: (1) the results strongly supported the monophyly of the Pterioidea; (2) none of the previously defined families (except for the monotypic Pulvinitidae) were monophyletic;