Abstract: Glycine max is a photoperiodic short-day plant and the practical consequence of the response is latitude and sowing period limitations to commercial crops. Genetic and physiological studies using the model plants Arabidopsis thaliana and rice (Oryza sativa) have uncovered several genes and genetic pathways controlling the process, however information about the corresponding pathways in legumes is scarce. Data mining prediction methodologies, including multiple sequence alignment, phylogenetic analysis, bioinformatics expression and sequence motif pattern identification, were used to identify soybean genes involved in day length perception and photoperiodic flowering induction. We have investigated approximately 330 000 sequences from open-access databases and have identified all bona fide central oscillator genes and circadian photoreceptors from A. thaliana in soybean sequence databases. We propose a working model for the photoperiodic control of flowering time in G. max, based on the identified key components. These results demonstrate the power of comparative genomics between model systems and crop species to elucidate the several aspects of plant physiology and metabolism.

Key words: circadian clock, cryptochrome, data mining, flowering, photoperiod, phytochrome.