PT  - JOURNAL ARTICLE
AU  - Shubham Chandak
AU  - Joachim Neu
AU  - Kedar Tatwawadi
AU  - Jay Mardia
AU  - Billy Lau
AU  - Matthew Kubit
AU  - Reyna Hulett
AU  - Peter Griffin
AU  - Mary Wootters
AU  - Tsachy Weissman
AU  - Hanlee Ji
TI  - Overcoming high nanopore basecaller error rates for DNA storage via basecaller-decoder integration and convolutional codes
AID  - 10.1101/2019.12.20.871939
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 2019.12.20.871939
4099  - http://biorxiv.org/content/early/2019/12/20/2019.12.20.871939.short
4100  - http://biorxiv.org/content/early/2019/12/20/2019.12.20.871939.full
AB  - As magnetization and semiconductor based storage technologies approach their limits, bio-molecules, such as DNA, have been identified as promising media for future storage systems, due to their high storage density (petabytes/gram) and long-term durability (thousands of years). Furthermore, nanopore DNA sequencing enables high-throughput sequencing using devices as small as a USB thumb drive and thus is ideally suited for DNA storage applications. Due to the high insertion/deletion error rates associated with basecalled nanopore reads, current approaches rely heavily on consensus among multiple reads and thus incur very high reading costs. We propose a novel approach which overcomes the high error rates in basecalled sequences by integrating a Viterbi error correction decoder with the basecaller, enabling the decoder to exploit the soft information available in the deep learning based basecaller pipeline. Using convolutional codes for error correction, we experimentally observed 3x lower reading costs than the state-of-the-art techniques at comparable writing costs.The code, data and Supplementary Material is available at https://github.com/shubhamchandak94/nanopore_dna_storage.