To gain insight into the transcriptome of the well-used plant model system Physcomitrella patens, several EST sequencing projects have been undertaken. We have clustered, assembled, and annotated all publicly available EST and CDS sequences in order to represent the transcriptome of this non-seed plant. Here, we present our fully annotated knowledge resource for the Physcomitrella patens transcriptome, integrating annotation from the production process of the clustered sequences and from a high-quality annotation pipeline developed during this study. Each transcript is represented as an entity containing full annotations and GO term associations. The whole production, filtering, clustering, and annotation process is being modelled and results in seven datasets, representing the annotated Physcomitrella transcriptome from different perspectives. We were able to annotate 63.4 % of the 26 123 virtual transcripts. The transcript archetype, as covered by our clustered data, is compared to a compilation based on all available Physcomitrella full length CDS. The distribution of the gene ontology annotations (GOA) for the virtual transcriptome of Physcomitrella patens demonstrates consistency in the ratios of the core molecular functions among the plant GOA. However, the metabolism subcategory is over-represented in bryophytes as compared to seed plants. This observation can be taken as an indicator for the wealth of alternative metabolic pathways in moss in comparison to spermatophytes. All resources presented in this study have been made available to the scientific community through a suite of user-friendly web interfaces via www.cosmoss.org and form the basis for assembly and annotation of the moss genome, which will be sequenced in 2005.

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Abstract. Automata over infinite words provide a powerful framework to solve various decision problems. However, the mechanized reasoning with restricted classes of automata over infinite words is often simpler and more efficient. For instance, weak deterministic Büchi automata (wdbas) can be handled algorithmically almost as efficient as deterministic automata over finite words. In this paper, we show how and when the standard powerset construction for automata over finite words can be used to determinize automata over infinite words. An instance is the class of automata that accept wdba-recognizable languages. Furthermore, we present applications of this new determinization construction. Namely, we apply it to improve the automata-based approach for the mixed firstorder linear arithmetic over the reals and the integers, and we utilize it to accelerate finite state model checking. We report on experimental results for these two applications.

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Automata are a useful tool in infinite-state model checking, since they can represent infinite sets of integers and reals. However, analogous to the use of bdds to represent finite sets, the sizes of the automata are an obstacle in the automata-based set representation. In this paper, we generalize the notion of "don't cares" for bdds to word languages as a means to reduce the automata sizes. We show that the minimal weak deterministic Büchi automaton (wdba) with respect to a given don't care set, under certain restrictions, is uniquely determined and can be efficiently constructed. We apply don't cares to improve the efficiency of a decision procedure for the first-order logic over the mixed linear arithmetic over the integers and the reals based on wdbas.