DNA and Biological Computing

DDS-E-Sim simulates error-prone DNA sequences for DNA data storage, capturing both random and biased errors with high fidelity across different sequencing technologies.

This paper introduces a DNA data storage scheme using 5-methylcytosine to increase information density, achieving high data recovery rates in experimental validation.

The paper introduces a new soft decoder for nanopore sequencing that improves throughput and read density while maintaining low error rates for DNA and RNA.

The paper introduces RSDISC, a method using CRISPR-Cas12a for random sanitization of DNA-stored metadata, achieving 99.9% efficiency in degrading 28,258 oligonucleotides.

This paper introduces a cost-effective DNA storage system using pre-fabricated DNA movable types that can be reused, reducing storage costs to $122/MB.

The paper introduces DNA StairLoop, a coding scheme for DNA-based data storage that improves error correction and parallel processing capabilities.

This paper demonstrates adaptive sampling in nanopore sequencing for PCR-free random access in DNA data storage, enabling selective data retrieval and re-archiving of DNA aliquots.

DNA-SaM improves DNA data storage by achieving high efficiency, scalability, and safety through advanced encoding and architecture.

This paper introduces a DNA labeling system called Genome Signature that records and corrects mutations in living E. coli, enabling self-authenticating genomes.

The paper introduces a nanopore-based codec system, Composite Hedges Nanopores (CHN), capable of handling high indel and substitution error rates for real-time DNA data readout.

This paper develops a model to determine sequencing coverage for DNA-based data storage, using coupon collector and Markov Chain methods to analyze decoding probability and error correction.

This paper introduces GradHC, a clustering algorithm for DNA storage systems that is more stable and robust than existing methods, producing highly reliable clustering results across various designs and error ranges.

This paper introduces a combinatorial DNA encoding method for data storage, achieving 6.5x higher density and near-zero reconstruction error through shortmer-based alphabets and error correction.

This study introduces a method to improve DNA data storage efficiency by using neural networks and language models to generate constraint-free sequences, achieving 12%-26% better compression and reducing synthesis costs.

This paper presents new algorithms for DNA reconstruction problems, outperforming previous methods on simulated and experimental data.

This study analyzes error patterns in photolithographic synthesis and DNA decay for DNA data storage, proposing a digital twin tool for evaluating error-correction codes.

The paper develops a digital twin to simulate DNA data storage workflows, enabling error analysis and ECC design without experimental trials.

The paper introduces composite motifs and enzymatic techniques to improve DNA storage density and presents an end-to-end experiment achieving 84 bits/cycle logical density.

The paper introduces Autoturbo-DNA, a framework for training autoencoders tailored to the DNA data storage channel, enabling error correction and constraint-based sequence generation.

DNA Storage Designer simulates DNA storage experiments, offering encoding, error simulation, and decoding to guide experiment design and reduce costs.

This paper introduces an improved DNA information storage method that enhances dropout tolerance, reduces encoding/decoding time, and enables secure data recovery.

This paper proposes a modulation-based DNA storage architecture that ensures biological sequence constraints and efficiently corrects sequencing errors, enabling reliable data recovery.

This paper introduces MDRAM, a DNA-based random access memory system that enables efficient and scalable readouts using nanopore sequencing and magnetic bead conjugation.

This paper evaluates tradeoffs between DNA storage density and stability to identify optimal configurations for different storage scenarios.

This paper develops thermoresponsive microcapsules for repeated random access of DNA data, demonstrating improved performance over non-compartmentalized storage.

This paper models and characterizes error sources in DNA data storage by analyzing sequence corruption and data-dependent errors to improve data recovery.

This paper designs an enzymatic repair procedure to reverse DNA damage in DNA data storage, showing its benefit at high storage densities and long durations.

The paper introduces DNA Drive, a biosafe and biocompatible DNA data storage strategy with unlimited storage capacity, encoding historical texts from the French Revolution.

This paper introduces a 2D DNA-based data storage system that encodes information in both sequence and backbone structure, using machine learning for error correction and data reconstruction.

This paper introduces a DNA-based data archiving system called Yin-Yang Codec, which achieves high recovery rates and physical information density in both in vitro and in vivo storage.