We have a lot of information at our fingertips, so how do we make sense of it all, especially in human health? Conor and Dodi speak to two experts who are making sense of this information overload by creating genetic and genomic databases.  Dr Artem Babaian, a computational biologist and now Assistant Professor leading The Laboratory for RNA-Based Lifeforms at the University of Toronto, explains how he and his team uncovered 100,000 novel viruses in old genetic data that could help us predict future pandemics. Professor Jinchuan Xing, Associate Professor at Rutgers University in the Department of Genetics conducting research on genomic variation, walks us through his study on using genomic data to predict infertility from aneuploid egg production. Let's dive into the data! Show notes Edgar, R.C., Taylor, J., Lin, V. et al. Petabase-scale sequence alignment catalyses viral discovery. Nature 602, 142–147 (2022). https://doi.org/10.1038/s41586-021-04332-2 Sun, S., Miller, M., Wang, Y. et al. Predicting embryonic aneuploidy rate in IVF patients using whole-exome sequencing. Hum Genet 141, 1615–1627 (2022). https://doi.org/10.1007/s00439-022-02450-z Keywords: viruses, genome, patients, prediction, mutations, data, RNA viruses, computational biology, families, human genome, whole exome sequencing, discovery, machine learning, infertility, chromosomes, scientists.