DNA is a promising candidate for data storage due to its
high density and long-term stability. However, accessing
specific data, known as random access, is challenging.
This process uses primers, short DNA segments that act
as identifiers. Efficient random access depends on high-
quality primers, constrained by DNA structure. This paper
introduces a method to generate primers that meet strict
biochemical criteria, avoiding sequences that form prob-
lematic shapes. The tool uses computational models to pre-
dict primer binding affinity and specificity, allowing users
to adjust parameters for lab protocols, enhancing data re-
trieval efficiency and optimization.