  DNA-based storage is an emerging technology that enables digital information
to be archived in DNA molecules. This method enjoys major advantages over
magnetic and optical storage solutions such as exceptional information density,
enhanced data durability, and negligible power consumption to maintain data
integrity. To access the data, an information retrieval process is employed,
where some of the main bottlenecks are the scalability and accuracy, which have
a natural tradeoff between the two. Here we show a modular and holistic
approach that combines Deep Neural Networks (DNN) trained on simulated data,
Tensor-Product (TP) based Error-Correcting Codes (ECC), and a safety margin
mechanism into a single coherent pipeline. We demonstrated our solution on
3.1MB of information using two different sequencing technologies. Our work
improves upon the current leading solutions by up to x3200 increase in speed,
40% improvement in accuracy, and offers a code rate of 1.6 bits per base in a
high noise regime. In a broader sense, our work shows a viable path to
commercial DNA storage solutions hindered by current information retrieval
processes.
