DNA-Based Data Storage Systems
AssigneeThe Board of Trustees of the University of Illinois
InventorsOlgica Milenkovic, Charles M. Schroeder, Seyedkasra Tabatabaei, Aleksei Aksimentiev, Alvaro Gonzalo Hernandez, Chao Pan, Jingqian Liu, Shubham Chandak, Bach Pham, Min Chen, Spencer A. Shorkey
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Abstract
The present disclosure relates generally to data storage using DNA sequences comprising synthetic nucleotides. In particular, the disclosure provides for a DNA data storage system comprising a covalently linked sequence of nucleotides, wherein the sequence of nucleotides comprises a modification region, wherein the nucleotides comprise synthetic nucleotides.
Claims20
A DNA data storage system comprising a covalently linked sequence of nucleotides, wherein the sequence of nucleotides comprises a modification region, wherein the nucleotides comprise synthetic nucleotides, wherein the synthetic nucleotides are each independently of the formula:
- wherein R is H, or is a heterocycle.
A method of reading a DNA sequence, the method comprising:
- introducing a DNA data storage system into a flow cell of a nanopore sequencing device, wherein the DNA data storage system comprises a modification region comprising synthetic nucleotides;
- receiving information indicative of an electrical signal provided when the modification region passes through a nanopore of the nanopore sequencing device;
- classifying, based on the received information, at least a portion of the modification region according to an expanded molecular alphabet;
- and
- determining, based on the classifying, a nucleotide sequence of the modification region.
A method of training a neural network comprising:
- providing training data to the neural network, wherein the training data comprises labeled data, wherein the labeled data comprises values indicative of electrical signals provided when a modification region of a DNA data storage system passes through a nanopore of a nanopore sequencing device, wherein the labeled data further comprises labels corresponding to an expanded molecular alphabet;
- and
- comparing an output of the neural network to the labels;
- adjusting at least one weight of the neural network based on the comparison.
Dependencies are inferred from the claim text.