Success in obtaining high soybean productivities depends on the use of high. quality seed. Many problems that damage the seed physiological quality can be related to the seed coat traits. Several studies state the existence of soybean seeds with a semi-permeable coat, which are more resistant to water penetration and less susceptible to mechanical damage, climatic adversities and moisture deterioration. The inclusion of the traits for semi-permeable coat in modern cultivars could minimize problems related to seed quality. In this context, molecular biology techniques in association with bioinformatics are an important alternative to identify genes involved in soybean seed coat and physiological quality. The objective of this study was to describe and to evaluate a strategy using bioinformatics tools, to integrate in silico information with in vitro experiments of molecular markers against data available in genomic databases, and provide information as to whether these markers could be associated with different soybean seed coat traits. Twenty four microssatelite primer sets previously tested and which amplified polymorphic fragments between soybean genotypes CD-202 (yellow seed coat, permeable and susceptible to deterioration) and TP (black seed coat, semi-permeable and resistant to deterioration) were used. The results indicated as promising the use of these molecular markers for studies related to soybean seed coat and quality. The strategy of molecular markers mining from in silico integration of yet anonymous molecular marker sequences and database containing gene sequences with functional descriptions, seems to be promising, as it allowed to predict the functions of these genes and to verify their association with metabolic and biochemical pathways involved in characteristics which are interesting to in vitro analyzes.
Bioinformatics; Molecular Markers; Glycine max
