Humans derive more than half their dietary protein from cereal grain sources with maize comprising nearly 40% of the total cereal grain tonnage. Despite the market prevalence of this crop and the improvements we have seen in yields since hybrid production of maize started in the early 20th century, maize remains a rather poor protein source, providing only one-fifth the amount of lysine – the most limiting amino acid in maize – required for optimal human nutrition. The effect of this lower protein quality is moderated by the consumption of other high-protein sources, but in regions like sub-Saharan Africa where maize constitutes over 20% of the daily energy intake (DEI) and alternative protein sources are not readily available, there is a high risk of protein malnutrition, especially in countries where the DEI exceeds 50% like Lesotho, Zambia, and Malawi. Following the discovery of opaque2 (o2) – a chalky endosperm mutant with high levels of lysine and tryptophan relative to common maize – and modifier genes that mitigated the negative effects associated with o2, the International Maize and Wheat Improvement Center (CIMMYT) began the development of quality protein maize (QPM) in the 1960s to help combat undernutrition in developing countries. Maintenance of the preferential amino acid profile in QPM, however, requires that the endosperm be homozygous for o2. Genetic purity is essential to QPM production as foreign pollen from neighboring fields that lacks the o2 allele can contaminate a QPM field, resulting in heterozygous seeds lacking the preferential amino acid profile. Thus, we are pursuing avenues for the incorporation of a gametophytic cross-incompatibility (GCI) system into future QPM lines. Gametophyte factor1-Strong (Ga1-S), currently the most well-understood of these systems in maize, has been used by the popcorn industry since its discovery in the early 1900s. To deepen our understanding of these systems and their genetic and biochemical underpinnings, we have developed a suite of bioinformatics tools that capitalizes on the sequence read archive (SRA) and facilitates ancillary analyses of data, which will allow us to incorporate archived sequence data alongside newly generated data to expand our ability to call genotypes across diverse samples.