Deep Squared Euclidean Approximation to the Levenshtein Distance for DNA Storage
This work addresses the need for faster and more compatible clustering in DNA storage systems, though it is incremental as it builds on existing embedding techniques.
The authors tackled the problem of clustering DNA sequences for storage by approximating the Levenshtein distance with a deep squared Euclidean embedding, resulting in an efficient and robust method that reduces computational complexity.
Storing information in DNA molecules is of great interest because of its advantages in longevity, high storage density, and low maintenance cost. A key step in the DNA storage pipeline is to efficiently cluster the retrieved DNA sequences according to their similarities. Levenshtein distance is the most suitable metric on the similarity between two DNA sequences, but it is inferior in terms of computational complexity and less compatible with mature clustering algorithms. In this work, we propose a novel deep squared Euclidean embedding for DNA sequences using Siamese neural network, squared Euclidean embedding, and chi-squared regression. The Levenshtein distance is approximated by the squared Euclidean distance between the embedding vectors, which is fast calculated and clustering algorithm friendly. The proposed approach is analyzed theoretically and experimentally. The results show that the proposed embedding is efficient and robust.