The surge in demand for cost-effective long-term archival media, coupled with density limitations of contemporary magnetic media, has resulted in synthetic DNA emerging as a promising new alternative. Despite its benefits, storing data in DNA poses several challenges as the technology used for reading/writing data on DNA are highly error prone. Thus, it is important to design pipelines that can efficiently use redundancy to mask errors without amplifying read/write cost. In this work, we present Columnar MOlecular Storage System (CMOSS), a novel, end-to-end DNA storage pipeline that can provide error-tolerant data storage at low read/write costs. CMOSS differs from state-of-the-art (SOTA) on three fronts (i) a motif-based, vertical layout in contrast to nucleotide-based horizontal layout, (ii) integrated consensus calling and decoding enabled by the vertical layout, and (iii) a flexible, block-based data organization for random access over DNA storage in contrast to object-based organization. Using an in-depth evaluation with several simulated and real wet lab experiments, we demonstrate the benefits of CMOSS design.

Given the growing adoption of AI, cloud data lakes are facing the need to support cost-effective "just-in-case" data archival over long time periods to meet regulatory compliance requirements. Unfortunately, current media technologies suffer from fundamental issues that will soon, if not already, make cost-effective data archival infeasible. In this paper, we present a vision for redesigning the archival tier of cloud data lakes based on a novel, obsolescence-free storage medium-synthetic DNA. In doing so, we make two contributions: (i) we highlight the challenges in using DNA for data archival and list several open research problems, (ii) we outline OligoArchive-DSM (OA-DSM)-an end-to-end DNA storage pipeline that we are developing to demonstrate the feasibility of our vision.

This paper deals with unsupervised clustering in high dimensional space. The problem is to estimate both labels and a sparse projection matrix of weights. To address this combinatorial non-convex problem maintaining a strict control on the sparsity of the matrix of weights, we propose an alternating minimization of the Frobenius norm criterion. We provide a new efficient algorithm named k-sparse which alternates k-means with projection-gradient minimization. The projection-gradient step is a method of splitting type, with exact projection on the ℓ1ball to promote sparsity. The convergence of the gradient-projection step is addressed, and a preliminary analysis of the alternating minimization is provided. Experiments on Single Cell RNA sequencing datasets show that our method significantly improves the results of spectral clustering, SIMLR, and Sparcl methods. The complexity of our method is linear with the number of samples (cells), so that the method scales up to large datasets.

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 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.

SUMMARY: Current challenges in microRNA (miRNA) research are to improve the identification of in vivo mRNA targets and clarify the complex interplay existing between a specific miRNA and multiple biological networks. MiRonTop is an online java web tool that integrates DNA microarrays or high-throughput sequencing data to identify the potential implication of miRNAs on a specific biological system. It allows a rapid characterization of the most pertinent mRNA targets according to several existing miRNA target prediction approaches. It also provides useful representations of the enrichment scores according to the position of the target site along the 3'-UTR, where the contribution of the sites located in the vicinity of the stop codon and of the polyA tail can be clearly highlighted. It provides different graphs of miRNA enrichment associated with up- or down-regulated transcripts and different summary tables about selections of mRNA targets and their functional annotations by Gene Ontology. AVAILABILITY: http://www.microarray.fr:8080/miRonTop/index.

UNLABELLED: Mediante is a MIAME-compliant microarray data manager that links together annotations and experimental data. Developed as a J2EE three-tier application, Mediante integrates a management system for production of long oligonucleotide microarrays, an experimental data repository suitable for home made or commercial microarrays, and a user interface dedicated to the management of microarrays projects. Several tools allow quality control of hybridizations and submission of validated data to public repositories. AVAILABILITY: http://www.microarray.fr. SUPPLEMENTARY INFORMATION: http://www.microarray.fr/SP/lebrigand2007/

This paper describes MEAT (Memory of Experiments for the Analysis of Transcriptomes), a project aiming at supporting biologists working on DNA microarrays. We provide methodological and software support to build an experiment memory for this domain. Our approach, based on Semantic Web Technologies, is relying on formalized ontologies and semantic annotations of scientific articles and other knowledge sources. It can probably be extended to other massive analyses of biological events.