Scaling up DNA digital data storage by efficiently predicting DNA hybridisation using deep learning.

Deoxyribonucleic acid (DNA) has shown great promise in enabling computational applications, most notably in the fields of DNA digital data storage and DNA computing. Information is encoded as DNA strands, which will naturally bind in solution, thus enabling search and pattern-matching capabilities. Being able to control and predict the process of DNA hybridisation is crucial for the ambitious future of Hybrid Molecular-Electronic Computing. Current tools are, however, limited in terms of throughput and applicability to large-scale problems. We present the first comprehensive study of machine learning methods applied to the task of predicting DNA hybridisation. For this purpose, we introduce an in silico-generated hybridisation dataset of over 2.5 million data points, enabling the use of deep learning. Depending on hardware, we achieve a reduction in inference time ranging from one to over two orders of magnitude compared to the state-of-the-art, while retaining high fidelity. We then discuss the integration of our methods in modern, scalable workflows

Roadmap on semiconductor-cell biointerfaces.

This roadmap outlines the role semiconductor-based materials play in understanding the complex biophysical dynamics at multiple length scales, as well as the design and implementation of next-generation electronic, optoelectronic, and mechanical devices for biointerfaces. The roadmap emphasizes the advantages of semiconductor building blocks in interfacing, monitoring, and manipulating the activity of biological components, and discusses the possibility of using active semiconductor-cell interfaces for discovering new signaling processes in the biological world

QMCPACK: Advances in the development, efficiency, and application of auxiliary field and real-space variational and diffusion Quantum Monte Carlo

We review recent advances in the capabilities of the open source ab initio Quantum Monte Carlo (QMC) package QMCPACK and the workflow tool Nexus used for greater efficiency and reproducibility. The auxiliary field QMC (AFQMC) implementation has been greatly expanded to include k-point symmetries, tensor-hypercontraction, and accelerated graphical processing unit (GPU) support. These scaling and memory reductions greatly increase the number of orbitals that can practically be included in AFQMC calculations, increasing accuracy. Advances in real space methods include techniques for accurate computation of band gaps and for systematically improving the nodal surface of ground state wavefunctions. Results of these calculations can be used to validate application of more approximate electronic structure methods including GW and density functional based techniques. To provide an improved foundation for these calculations we utilize a new set of correlation-consistent effective core potentials (pseudopotentials) that are more accurate than previous sets; these can also be applied in quantum-chemical and other many-body applications, not only QMC. These advances increase the efficiency, accuracy, and range of properties that can be studied in both molecules and materials with QMC and QMCPACK

Privacy in the Genomic Era

Genome sequencing technology has advanced at a rapid pace and it is now possible to generate highly-detailed genotypes inexpensively. The collection and analysis of such data has the potential to support various applications, including personalized medical services. While the benefits of the genomics revolution are trumpeted by the biomedical community, the increased availability of such data has major implications for personal privacy; notably because the genome has certain essential features, which include (but are not limited to) (i) an association with traits and certain diseases, (ii) identification capability (e.g., forensics), and (iii) revelation of family relationships. Moreover, direct-to-consumer DNA testing increases the likelihood that genome data will be made available in less regulated environments, such as the Internet and for-profit companies. The problem of genome data privacy thus resides at the crossroads of computer science, medicine, and public policy. While the computer scientists have addressed data privacy for various data types, there has been less attention dedicated to genomic data. Thus, the goal of this paper is to provide a systematization of knowledge for the computer science community. In doing so, we address some of the (sometimes erroneous) beliefs of this field and we report on a survey we conducted about genome data privacy with biomedical specialists. Then, after characterizing the genome privacy problem, we review the state-of-the-art regarding privacy attacks on genomic data and strategies for mitigating such attacks, as well as contextualizing these attacks from the perspective of medicine and public policy. This paper concludes with an enumeration of the challenges for genome data privacy and presents a framework to systematize the analysis of threats and the design of countermeasures as the field moves forward

Assessing Time-Dependent Density Functional Theory as a Computational Tool to Model Chromophores Intercalated into DNA Scaffolds

Understanding first-principles energy transport in organic molecules reveals novel energy harvesting systems, quantum computing, and solar power energy transportation and production. DiSC2(5), also named Cy5, exhibits bright fluorescence signals and has shown promising lossless exciton transport properties, as well as tunable energy absorption and emission when conjoined with DNA. This paper focuses on functional groups of organic molecules, specifically the chromophoric red dye DiSC2(5) and its interaction with H- and J-aggregates and with DNA. Density functional theory (DFT) and time dependent density functional theory (TD-DFT) are applied to investigate the chromophore Cy5 in vertically stacked (H-aggregates) and slipped assembly dyes (J-aggregates) arrangements in order to assess computational tools for the prediction of optimized structural characteristics and hypsochromic, bathochromic exciton behavior