Beyond the Alphabet: Deep Signal Embedding for Enhanced DNA Clustering
Journal:
arXiv
Published Date:
Oct 8, 2024
Abstract
The emerging field of DNA storage employs strands of DNA bases (A/T/C/G) as a
storage medium for digital information to enable massive density and
durability. The DNA storage pipeline includes: (1) encoding the raw data into
sequences of DNA bases; (2) synthesizing the sequences as DNA \textit{strands}
that are stored over time as an unordered set; (3) sequencing the DNA strands
to generate DNA \textit{reads}; and (4) deducing the original data. The DNA
synthesis and sequencing stages each generate several independent error-prone
duplicates of each strand which are then utilized in the final stage to
reconstruct the best estimate for the original strand. Specifically, the reads
are first \textit{clustered} into groups likely originating from the same
strand (based on their similarity to each other), and then each group
approximates the strand that led to the reads of that group. This work improves
the DNA clustering stage by embedding it as part of the DNA sequencing.
Traditional DNA storage solutions begin after the DNA sequencing process
generates discrete DNA reads (A/T/C/G), yet we identify that there is untapped
potential in using the raw signals generated by the Nanopore DNA sequencing
machine before they are discretized into bases, a process known as
\textit{basecalling}, which is done using a deep neural network. We propose a
deep neural network that clusters these signals directly, demonstrating
superior accuracy, and reduced computation times compared to current approaches
that cluster after basecalling.
