AI Breakthrough Accelerates DNA Data Retrieval by 3,200x, Advancing Biological Data Storage

AI breakthrough claimed to make DNA data retrieval 3,200x faster with better accuracy, but still slower than standard storage

Speed improvement delivers a big step in the right direction. Storing digital content in DNA is an emerging technology that takes advantage of the molecule's density, durability, and low power needs. DNA can last for generations, unlike NAND flash and HDDs that degrade in years or decades, at best. It also offers a data capacity about 100 million times higher than typical data storage systems. However, retrieving data from DNA storage is a complex and rather slow process. Good news, a breakthrough made by Israeli researchers speeds up the activity by 3,200 times, reports TechXplore. A team at Technion - Israel Institute of Technology has created an AI tool that makes retrieving digital information stored in DNA dramatically faster and more accurate. The system, named DNAformer, is 3,200 times faster than the most accurate previous methods and is claimed to deliver excellent results, showing promise for efficient, large-scale data storage using biological material. It is still too slow for the commercial market, but the researchers believe that they are moving in the right direction. This new approach allows for processing 100MB in only 10 minutes, compared to several days using the best current methods. On a test involving 3.1MB, the tool handled several types of content: a still image in color, a short sound recording of Neil Armstrong on the Moon, a text about DNA's storage advantages, and randomly generated data mimicking encrypted or compressed files. To store data, customized DNA molecules are synthesized. Reading the information requires sequencing, but this introduces various errors like deletions or substitutions, and returns unordered and sometimes corrupted data copies. DNAformer handles these issues using algorithms that identify correct patterns from flawed inputs. The model includes tailored correction codes and a safety layer to detect highly noisy sequences. It uses specialized tools to clean up those errors before translating the sequences back into digital form. DNAformer is based on a transformer model trained using synthetic datasets produced by a simulator also built at Technion. Besides the speed improvement, DNAformer also showed up to 40% higher accuracy over previous quick retrieval methods. This performance marks a breakthrough in handling real-world DNA storage data, especially when dealing with incomplete or noisy sequences that challenge traditional correction methods. The researchers plan to adjust DNAformer for specific needs and believe the system can scale for industrial and research applications. It is designed to be flexible and can evolve with future progress in how DNA is written and read, helping meet the growing demand for sustainable and high-capacity storage solutions.

AI supercharges DNA data retrieval, making it 3,200 times faster

Forward-looking: Researchers around the world are embracing DNA-based storage right now. Mixing digital data and biology could bridge the best of both worlds, though a few challenges are still slowing market and industry adoption. Visionary solutions using DNA sequencing have been hailed as the future of the storage world for a few years now. Biology seems to have solved the data encoding problem a few billion years ago, so we could learn a thing or two from nature while we prepare to expand the world's digital realm to 180 zettabytes - amounting to 180 billion terabytes - by the end of 2025. Israeli researchers say they have found a way to significantly improve the data retrieval process, which is one of the biggest issues DNA storage technology is facing right now. A team at Technion - Israel Institute of Technology used a specifically trained AI model to speed up data recovery from DNA strands by 3,200 times. Needless to say, the process is still much slower than "modern" storage technologies available on the market. The AI tech in question is known as DNAformer, and is based on a transformer model trained by Technion researchers on synthetic data. The data simulator that fed DNAformer was also created at Technion. The model can reconstruct accurate DNA sequences from error-prone copies and can boost data integrity even further thanks to a custom error-correcting algorithm designed to work well with DNA. DNAformer is much faster at retrieving data than previously unveiled methods. The AI model can read 100 megabytes 3,200 times faster than the most accurate existing method, and can seemingly do so with no loss of data. Accuracy is improved by "up to" 40 percent as well, which can further decrease the total retrieval process time. The Israeli researchers tested DNAformer's capabilities on a tiny 3.1-megabyte data set, which included a color still image, a 24-second audio clip, a written piece about DNA storage, and some random data. The latter was useful to show how the model can behave when dealing with encrypted or even compressed digital data. The team achieved a "data rate" of 1.6 bits per (DNA) base in a high-noise regime, the official study says, cutting the time needed to read the data back from several days to just 10 minutes. The Technion team said DNAformer will be further developed and tailored to different data storage needs. The technology can easily scale and adapt to various scenarios, with promising prospects for its adaptability. The researchers are already thinking about "market demands" and future improvements in DNA sequencing to improve their AI technology.