Robust Multi-Read Reconstruction from Contaminated Clusters Using Deep Neural Network for DNA Storage

by   Yun Qin, et al.

DNA has immense potential as an emerging data storage medium. The principle of DNA storage is the conversion and flow of digital information between binary code stream, quaternary base, and actual DNA fragments. This process will inevitably introduce errors, posing challenges to accurate data recovery. Sequence reconstruction consists of inferring the DNA reference from a cluster of erroneous copies. A common assumption in existing methods is that all the strands within a cluster are noisy copies originating from the same reference, thereby contributing equally to the reconstruction. However, this is not always valid considering the existence of contaminated sequences caused, for example, by DNA fragmentation and rearrangement during the DNA storage process.This paper proposed a robust multi-read reconstruction model using DNN, which is resilient to contaminated clusters with outlier sequences, as well as to noisy reads with IDS errors. The effectiveness and robustness of the method are validated on three next-generation sequencing datasets, where a series of comparative experiments are performed by simulating varying contamination levels that occurring during the process of DNA storage.


page 6

page 12

page 13


Single-Read Reconstruction for DNA Data Storage Using Transformers

As the global need for large-scale data storage is rising exponentially,...

Deep DNA Storage: Scalable and Robust DNA Storage via Coding Theory and Deep Learning

The concept of DNA storage was first suggested in 1959 by Richard Feynma...

Reconstruction Codes for DNA Sequences with Uniform Tandem-Duplication Errors

DNA as a data storage medium has several advantages, including far great...

Deep Squared Euclidean Approximation to the Levenshtein Distance for DNA Storage

Storing information in DNA molecules is of great interest because of its...

DNA Steganalysis Using Deep Recurrent Neural Networks

The technique of hiding messages in digital data is called a steganograp...

Inferring taxonomic placement from DNA barcoding allowing discovery of new taxa

In ecology it has become common to apply DNA barcoding to biological sam...

Trace Reconstruction Problems in Computational Biology

The problem of reconstructing a string from its error-prone copies, the ...

Please sign up or login with your details

Forgot password? Click here to reset