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DNA has immense potential as an emerging data storage medium. The principleof DNA storage is the conversion and flow of digital information between binarycode stream, quaternary base, and actual DNA fragments. This process willinevitably introduce errors, posing challenges to accurate data recovery.Sequence reconstruction consists of inferring the DNA reference from a clusterof erroneous copies. A common assumption in existing methods is that all thestrands within a cluster are noisy copies originating from the same reference,thereby contributing equally to the reconstruction. However, this is not alwaysvalid considering the existence of contaminated sequences caused, for example,by DNA fragmentation and rearrangement during the DNA storage process.Thispaper proposed a robust multi-read reconstruction model using DNN, which isresilient to contaminated clusters with outlier sequences, as well as to noisyreads with IDS errors. The effectiveness and robustness of the method arevalidated on three next-generation sequencing datasets, where a series ofcomparative experiments are performed by simulating varying contaminationlevels that occurring during the process of DNA storage.

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