These community annotated models were obtained by querying the six-frame translation of the rice genome with well-characterized proteins or sequence motifs and were subjected to manual curation [ 26- 30].
There is no exact time after which the vectors are ready, so we verified the quality of the representations empirically by querying the model for the most similar words to a given one.
In addition, by querying the model, interesting executions could suggest new experiments to be tested in the lab.
We ran hmmsearch against proteins of the 17 genomes by querying these HMM models in the ls mode (global with respect to query domain and local with respect to hit protein; see details in the manual of HMMER package).
By querying the ModBase database [ 17] we obtained 42 models having more than 40% similarity with known protein structures.
Potential Rosetta Stone candidates were identified by querying the database for genomes with orthologs to two or more of the split models associated with a multifunctional protein.
While model checking using most timed temporal logics is PSPACE-complete or harder [Alur and Henzinger, Inform. and Comput. 104 (1993) 35; Alur et al., Inform. and Comput. 104 (1993) 2], we show that many practical timing queries, where we are interested in the worst-case or best-case timings, can be answered in polynomial time by querying the system using Min-max CTL.
Functional annotation and Gene Ontology were retrieved by querying the annotation browser in agriGO with Affymetrix probesets, by querying the annotation batch function at PopGenie http://130.239.72.5/popgenie1 with the poplar gene model, and by querying the annotation tool at The Arabidopsis Information Resource TAIRR) http://www.arabidopsis.org [ 37, 101].
The first two approaches build protein models by aligning query sequences onto solved template structures.
Saint facilitates the biological annotation of SBML models by using query translation to present an integrated view of data sources and suggested ontological terms.
