Bibliographic Details
| Title: |
Massively parallel homogeneous amplification of chip-scale DNA for DNA information storage (MPHAC-DIS) |
| Authors: |
Zhi Weng, Jiangxue Li, Yi Wu, Xuehao Xiu, Fei Wang, Xiaolei Zuo, Ping Song, Chunhai Fan |
| Source: |
Nature Communications, Vol 16, Iss 1, Pp 1-11 (2025) |
| Publisher Information: |
Nature Portfolio, 2025. |
| Publication Year: |
2025 |
| Collection: |
LCC:Science |
| Subject Terms: |
Science |
| More Details: |
Abstract Chip scale DNA synthesis offers a high-throughput and cost-effective method for large-scale DNA-based information storage. Nevertheless, unbiased information retrieval from low-copy-number sequences remains a barricade that largely arises from the indispensable DNA amplification. Here, we devise a simulation-guided quantitative primer-template hybridization strategy to realize massively parallel homogeneous amplification of chip-scale DNA for DNA information storage (MPHAC-DIS). Using a fixed-energy primer design, we demonstrate the unbiasedness of MPHAC for amplifying 100,000-plex sequences. Simulations reveal that MPHAC achieves a fold-80 value of 1.0 compared to 3.2 with conventional fixed-length primers, lowering costs by up to four orders of magnitude through reduced over-sequencing. The MPHAC-DIS system using 35,406 encoded oligonucleotide allows simultaneous access of multimedia files including text, images, and videos with high decoding accuracy at very low sequencing depths. Specifically, even a ~ 1 × sequencing depth, with the combination of machine learning, results in an acceptable decoding accuracy of ~80%. The programmable and predictable MPHAC-DIS method thus opens new door for DNA-based large-scale data storage with potential industrial applications. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2041-1723 |
| Relation: |
https://doaj.org/toc/2041-1723 |
| DOI: |
10.1038/s41467-025-55986-9 |
| Access URL: |
https://doaj.org/article/876ced0de6e0401db984ff7e39e056f3 |
| Accession Number: |
edsdoj.876ced0de6e0401db984ff7e39e056f3 |
| Database: |
Directory of Open Access Journals |
