Over the past years, the ever-growing trend on data storage demand, more specifically for "cold" data (i.e. rarely accessed), has motivated research for alternative systems of data storage. Because of its biochemical characteristics, synthetic DNA molecules are now considered as serious candidates for this new kind of storage. This paper introduces a novel arithmetic coder for DNA data storage, and presents some results on a lossy JPEG 2000 based image compression method adapted for DNA data storage that uses this novel coder.The DNA coding algorithms presented here have been designed to efficiently compress images, encode them into a quaternary code, and finally store them into synthetic DNA molecules. This work also aims at making the compression models better fit the problematic that we encounter when storing data into DNA, namely the fact that the DNA writing, storing and reading methods are error prone processes.The main take away of this work is our arithmetic coder and it's integration into a performant image codec.

Over the past years, the ever-growing trend on data storage demand, more specifically for “cold” data (rarely accessed data), has motivated research for alternative systems of data storage. Because of its biochemical characteristics, synthetic DNA molecules are now considered as serious candidates for this new kind of storage. This paper presents some results on lossy image compression methods based on convolutional autoencoders adapted to DNA data storage, with synthetic DNA-adapted entropic and fixed-length codes. The model architectures presented here have been designed to efficiently compress images, encode them into a quaternary code, and finally store them into synthetic DNA molecules. This work also aims at making the compression models better fit the problematics that we encounter when storing data into DNA, namely the fact that the DNA writing, storing and reading methods are error prone processes. The main take aways of this kind of compressive autoencoder are our latent space quantization and the different DNA adapted entropy coders used to encode the quantized latent space, which are an improvement over the fixed length DNA adapted coders that were previously used.

Digital media explosion has led to an exponential increase of the amount of data generated worldwide and the need for new means of storage able to keep up with the current growth of digital information has become a critical challenge. During the last decade, DNA has been proven to be a potential candidate thanks to its biological properties allowing to store information at high density (215 petabytes in 1 gram) for centuries. In previous works we have presented an end-to-end storage workflow specifically designed for the efficient storage of images onto synthetic DNA and proven its feasibility in a wet-lab experiment in which sequencing was performed using the Illumina machine. In this work we are studying the sequencing using rather the MinION sequencer on the same data after being stored in a sealed capsule for two years. MinION is a very promising sequencer although introducing a much higher error rate in the process of reading. In this paper, we propose a solution to deal with the MinION sequencing noise allowing to recover the original stored data.

The exponential increase of digital data and the limited capacity of current storage devices have made clear the need for exploring new storage solutions. Thanks to its biological properties, DNA has proven to be a potential candidate for this task, allowing the storage of information at a high density for hundreds or even thousands of years. With the release of nanopore sequencing technologies, DNA data storage is one step closer to become a reality. Many works have proposed solutions for the simulation of this sequencing step, aiming to ease the development of algorithms addressing nanopore-sequenced reads. However, these simulators target the sequencing of complete genomes, whose characteristics differ from the ones of synthetic DNA. This work presents a nanopore sequencing simulator targeting synthetic DNA on the context of DNA data storage.

Rapid technological advances and the increasing use of social media has caused a tremendous increase in the generation of digital data, a fact that imposes nowadays a great challenge for the field of digital data storage due to the short-term reliability of conventional storage devices. Hard disks, fiash, tape or even optical storage have a durability of 5 to 20 years while running data centers also require huge amounts of energy. An alternative to hard drives is the use of DNA, which is life's information-storage material, as a means of digital data storage. Recent works have proven that storing digital data into DNA is not only feasible but also very promising as the DNA's biological properties allow the storage of a great amount of information into an extraordinary small volume for centuries or even longer with no loss of information. In this work we present an extended end-to-end storage workflow specially designed for the efficient storage of images onto synthetic DNA. This workflow uses a new encoding algorithm which serves the needs of image compression while also being robust to the biological errors which may corrupt the encoding.

The exponential increase of digital data that is being generated every year along with the capacity and durability limits of conventional storage devices are raising one of the greatest challenges for the field of data storage. The use of DNA for digital data archiving is a very promising alternative as the biological properties of the DNA molecule allow the storage of a huge amount of information into a very limited volume while also promising data longevity for centuries or even longer. In this paper we present a comparative study of our work with the state of the art solutions, and show that our solution is competitive.

The aggressive growth of digital data threatens to exceed the capacity of conventional storage devices. The need for new means to store digital information has brought great interest in novel solutions as it is DNA, whose biological properties allow the storage of information at a high density and preserve it without any information loss for hundreds of years when stored under specific conditions. Despite being a promising solution, DNA storage faces two major obstacles: the large cost of synthesis and the high error rate introduced during sequencing. While most of the works focus on adding redundancy aiming for effective error correction, this work combines noise resistance to minimize the impact of the errors in the decoded data and post-processing to further improve the quality of the decoding.

The exponential growth in the generation of digital information creates a big challenge for data storage given the capacity limitations of conventional storage devices. Recent works have proposed DNA as a means of digital data storage proposing a novel solution for long-term storage. Although having many advantages, DNA storage is a challenging topic due to the error-prone process of DNA sequencing (reading). To deal with this error most existing works focus on the introduction of error-correction methods. However, most of those methods introduce important redundancy without promising full error correction for the widely used sequencing method using the Nanopore sequencer. This work focuses on noise resistance rather than error-correction proposing a new algorithm for optimally assigning VQ indices to DNA codewords while reducing the visual impact of substitution errors that are caused during sequencing.

This paper aims to build an image coding system based on a model of the mammalian retina. The retina is the light-sensitive layer of tissue located on the inner coat of the eye and it is responsible for vision. Inspired by the way the retina handles and compresses visual information and based on previous studies we aim to build and analytically study a retinal-inspired image quantizer, based on the Leaky Integrate-and-Fire (LIF) model, a neural model approximating the behavior of the ganglion cells of the Ganglionic retinal layer that is responsible for visual data compression. In order to have a more concrete view of the encoder's behavior, in our experiments, we make use of the spatiotemporal decomposition layers provided by extensive studies on a previous retinal layer, the Outer Plexiform Layer (OPL). The decomposition layers produced by the OPL, are being encoded using our LIF image encoder and then, they are reconstructed to observe the encoder's efficiency.