  De Bruijn graphs are essential for sequencing data analysis and must be
efficiently constructed and stored for large-scale population studies. They
also need to be dynamic to allow updates such as adding or removing edges and
nodes. Existing dynamic implementations include DynamicBOSS and dynamicDBG. In
2018, a new family of data structures called learned indexes was introduced by
Tim Kraska and Alex Beutel, with a particularly efficient implementation
proposed by Paolo Ferragina and Giorgio Vinciguerra in 2020. This paper
presents a new method for implementing De Bruijn graphs using learned indexes
and compares its performance with current implementations. The new method shows
improved time and memory efficiency for edge and node insertions, particularly
with large datasets (over 110 million k-mers).
