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Storing information in DNA molecules is of great interest because of itsadvantages in longevity, high storage density, and low maintenance cost. A keystep in the DNA storage pipeline is to efficiently cluster the retrieved DNAsequences according to their similarities. Levenshtein distance is the mostsuitable metric on the similarity between two DNA sequences, but it is inferiorin terms of computational complexity and less compatible with mature clusteringalgorithms. In this work, we propose a novel deep squared Euclidean embeddingfor DNA sequences using Siamese neural network, squared Euclidean embedding,and chi-squared regression. The Levenshtein distance is approximated by thesquared Euclidean distance between the embedding vectors, which is fastcalculated and clustering algorithm friendly. The proposed approach is analyzedtheoretically and experimentally. The results show that the proposed embeddingis efficient and robust.

Deep Squared Euclidean Approximation to the Levenshtein Distance for DNA Storage