Back

Secure data storage on DNA hard drives

Chen, K.; Zhu, J.; Boskovic, F. N.; Keyser, U. F.

2019-11-28 biophysics
10.1101/857748 bioRxiv
Show abstract

DNA is emerging as a novel material for digital data storage. The two main challenges are efficient encoding and data security. Here, we develop an approach that allows for writing and erasing data by relying solely on Watson-Crick base pairing of short oligonucleotides to single-stranded DNA overhangs located along a long double-stranded DNA hard drive (DNA-HD). Our enzyme-free system enables fast synthesis-free data writing with predetermined building blocks. The use of DNA base pairing allows for secure encryption on DNA-HDs that requires a physical key and nanopore sensing for decoding. The system is suitable for miniature integration for an end-to-end DNA storage device. Our study opens a novel pathway for rewritable and secure data storage with DNA. One Sentence SummaryStoring digital information on molecules along DNA hard drives for rewritable and secure data storage.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

1
Nature Communications
○ 5641 papers in training set
Top 19%
9.5%
Similar papers in this journal
2
Nature Nanotechnology
○ 32 papers in training set
Top 0.1%
8.7%
Similar papers in this journal
3
Nano Letters
● 71 papers in training set
Top 0.1%
8.7%
Similar papers in this journal
4
ACS Nano
● 113 papers in training set
Top 0.3%
6.6%
Similar papers in this journal
5
Angewandte Chemie International Edition
○ 93 papers in training set
Top 0.3%
5.3%
6
Nucleic Acids Research
◐ 1281 papers in training set
Top 4%
5.3%
Similar papers in this journal
7
Journal of the American Chemical Society
● 217 papers in training set
Top 0.8%
4.7%
8
Science Advances
● 1243 papers in training set
Top 6%
4.7%
50% of probability mass above
9
Advanced Materials
○ 56 papers in training set
Top 0.3%
4.2%
10
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 17%
3.1%
11
Biophysical Journal
○ 631 papers in training set
Top 2%
3.1%
12
Small Methods
○ 29 papers in training set
Top 0.2%
2.4%
13
Small
○ 78 papers in training set
Top 0.7%
2.3%
14
Nature Chemistry
○ 42 papers in training set
Top 0.4%
1.9%
15
ChemBioChem
○ 55 papers in training set
Top 0.6%
1.7%
16
ACS Sensors
● 49 papers in training set
Top 0.4%
1.7%
17
Lab on a Chip
● 96 papers in training set
Top 0.7%
1.5%
18
Biophysical Reports
○ 37 papers in training set
Top 0.3%
1.3%
19
Nature Photonics
○ 10 papers in training set
Top 0.1%
1.1%
20
Cell
○ 431 papers in training set
Top 8%
1.1%
21
Advanced Science
○ 286 papers in training set
Top 7%
1.1%
22
iScience
○ 1154 papers in training set
Top 28%
1.1%
23
Nature Methods
○ 385 papers in training set
Top 6%
1.0%
24
Biosensors and Bioelectronics
○ 57 papers in training set
Top 0.7%
1.0%
25
Science
● 477 papers in training set
Top 8%
1.0%
26
ACS Central Science
● 71 papers in training set
Top 2%
0.8%
27
eLife
● 5828 papers in training set
Top 68%
0.8%
28
Advanced Functional Materials
○ 46 papers in training set
Top 1%
0.6%
29
Angewandte Chemie
○ 15 papers in training set
Top 0.3%
0.6%
30
Communications Biology
○ 993 papers in training set
Top 37%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.