Back

OligoArchive-DSM: Columnar Design for Error-Tolerant Database Archival using Synthetic DNA

Marinelli, E.; Yan, Y.; Magnone, V.; Dumargne, M.-C.; Barbry, P.; Heinis, T.; Appuswamy, R.

2022-10-06 bioengineering
10.1101/2022.10.06.511077 bioRxiv
Show abstract

The surge in demand for cost-effective, durable long-term archival media, coupled with density limitations of contemporary magnetic media, has resulted in synthetic DNA emerging as a promising new alternative. Today, the limiting factor for DNA-based data archival is the cost of writing (synthesis) and reading (sequencing) DNA. Newer techniques that reduce the cost often do so at the expense of reliability, as they introduce complex, technology-specific error patterns. In order to deal with such errors, it is important to design efficient pipelines that can carefully use redundancy to mask errors without amplifying overall cost. In this paper, we present OligoArchive-DSM (OA-DSM), an end-to-end DNA archival pipeline that can provide error-tolerant data storage at low read/write costs. Central to OA-DSM is a database-inspired columnar encoding technique that makes it possible to improve efficiency by enabling integrated decoding and consensus calling during data restoration.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
Bioinformatics
◐ 1204 papers in training set
Top 2%
15.3%
Similar papers in this journal
2
PLOS ONE
● 5266 papers in training set
Top 15%
12.0%
Similar papers in this journal
3
Scientific Reports
○ 3612 papers in training set
Top 6%
8.0%
Similar papers in this journal
4
Nature Communications
○ 5641 papers in training set
Top 25%
6.3%
Similar papers in this journal
5
GigaScience
◐ 212 papers in training set
Top 0.5%
5.5%
Similar papers in this journal
6
iScience
○ 1154 papers in training set
Top 3%
4.9%
50% of probability mass above
7
PLOS Computational Biology
● 1863 papers in training set
Top 7%
4.9%
8
Genome Biology
○ 637 papers in training set
Top 3%
3.6%
9
Genome Research
● 468 papers in training set
Top 2%
3.3%
10
Journal of Open Source Software
● 25 papers in training set
Top 0.1%
2.8%
11
IEEE Access
● 35 papers in training set
Top 0.6%
1.7%
12
Nature Computational Science
○ 55 papers in training set
Top 0.6%
1.7%
13
Nature Methods
○ 385 papers in training set
Top 4%
1.7%
14
Methods
○ 34 papers in training set
Top 0.3%
1.5%
15
Cell Systems
○ 201 papers in training set
Top 3%
1.5%
16
Gigabyte
● 62 papers in training set
Top 0.8%
1.1%
17
Nature Biotechnology
○ 172 papers in training set
Top 3%
1.1%
18
PeerJ
◐ 308 papers in training set
Top 8%
1.1%
19
Nucleic Acids Research
◐ 1281 papers in training set
Top 11%
1.1%
20
BMC Genomics
○ 406 papers in training set
Top 7%
0.9%
21
Communications Biology
○ 993 papers in training set
Top 27%
0.9%
22
Journal of Neural Engineering
○ 221 papers in training set
Top 2%
0.9%
23
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
● 12 papers in training set
Top 0.2%
0.6%
24
IEEE Transactions on Biomedical Engineering
● 40 papers in training set
Top 1%
0.6%
25
PNAS Nexus
◐ 159 papers in training set
Top 4%
0.6%
26
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 8%
0.6%
27
Plant Phenomics
○ 18 papers in training set
Top 0.3%
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.