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Abstract BackgroundThe all-electronic Single Molecule Break Junction (SMBJ) method is an emerging alternative to traditional polymerase chain reaction (PCR) techniques for genetic sequencing and identification. Existing work indicates that the current spectra recorded from SMBJ experimentations contain unique signatures to identify known sequences from a dataset. However, the spectra are typically extremely noisy due to the stochastic and complex interactions between the substrate, sample, environment, and the measuring system, necessitating hundreds or thousands of experimentations to obtain reliable and accurate results. ResultsThis article presents a DNA sequence identification system based on the current spectra of ten short strand sequences, including a pair that differs by a single mismatch. By employing a gradient boosted tree classifier model trained on conductance histograms, we demonstrate that extremely high accuracy, ranging from approximately 96 % for molecules differing by a single mismatch to 99.5 % otherwise, is possible. Further, such accuracy metrics are achievable in near real-time with just twenty or thirty SMBJ measurements instead of hundreds or thousands. We also demonstrate that a tandem classifier architecture, where the first stage is a multiclass classifier and the second stage is a binary classifier, can be employed to boost the single mismatched pair’s identification accuracy to 99.5 %. ConclusionsA monolithic classifier, or more generally, a multistage classifier with model specific parameters that depend on experimental current spectra can be used to successfully identify DNA strands. 

Abstract Deoxyribonucleic acid (DNA) has emerged as a promising building block for next-generation ultra-high density storage devices. Although DNA has high durability and extremely high density in nature, its potential as the basis of storage devices is currently hindered by limitations such as expensive and complex fabrication processes and time-consuming read–write operations. In this article, we propose the use of a DNA crossbar array architecture for an electrically readable read-only memory (DNA-ROM). While information can be ‘written’ error-free to a DNA-ROM array using appropriate sequence encodings its read accuracy can be affected by several factors such as array size, interconnect resistance, and Fermi energy deviations from HOMO levels of DNA strands employed in the crossbar. We study the impact of array size and interconnect resistance on the bit error rate of a DNA-ROM array through extensive Monte Carlo simulations. We have also analyzed the performance of our proposed DNA crossbar array for an image storage application, as a function of array size and interconnect resistance. While we expect that future advances in bioengineering and materials science will address some of the fabrication challenges associated with DNA crossbar arrays, we believe that the comprehensive body of results we present in this paper establishes the technical viability of DNA crossbar arrays as low power, high-density storage devices. Finally, our analysis of array performance vis-à-vis interconnect resistance should provide valuable insights into aspects of the fabrication process such as proper choice of interconnects necessary for ensuring high read accuracies. 

Abstract DNA double helices containing metal‐mediated DNA (mmDNA) base pairs are constructed from Ag⁺and Hg²⁺ions between pyrimidine:pyrimidine pairs with the promise of nanoelectronics. Rational design of mmDNA nanomaterials is impractical without a complete lexical and structural description. Here, the programmability of structural DNA nanotechnology toward its founding mission of self‐assembling a diffraction platform for biomolecular structure determination is explored. The tensegrity triangle is employed to build a comprehensive structural library of mmDNA pairs via X‐ray diffraction and generalized design rules for mmDNA construction are elucidated. Two binding modes are uncovered: N3‐dominant, centrosymmetric pairs and major groove binders driven by 5‐position ring modifications. Energy gap calculations show additional levels in the lowest unoccupied molecular orbitals (LUMO) of mmDNA structures, rendering them attractive molecular electronic candidates. 

Abstract Exploring the structural and electrical properties of DNA origami nanowires is an important endeavor for the advancement of DNA nanotechnology and DNA nanoelectronics. Highly conductive DNA origami nanowires are a desirable target for creating low‐cost self‐assembled nanoelectronic devices and circuits. In this work, the structure‐dependent electrical conductance of DNA origami nanowires is investigated. A silicon nitride (Si₃N₄) on silicon semiconductor chip with gold electrodes was used for collecting electrical conductance measurements of DNA origami nanowires, which are found to be an order of magnitude less electrically resistive on Si₃N₄substrates treated with a monolayer of hexamethyldisilazane (HMDS) (∼10¹³ohms) than on native Si₃N₄substrates without HMDS (∼10¹⁴ohms). Atomic force microscopy (AFM) measurements of the height of DNA origami nanowires on mica and Si₃N₄substrates reveal that DNA origami nanowires are ∼1.6 nm taller on HMDS‐treated substrates than on the untreated ones indicating that the DNA origami nanowires undergo increased structural deformation when deposited onto untreated substrates, causing a decrease in electrical conductivity. This study highlights the importance of understanding and controlling the interface conditions that affect the structure of DNA and thereby affect the electrical conductance of DNA origami nanowires. 

The global COVID-19 pandemic has highlighted the need for rapid, reliable, and efficient detection of biological agents and the necessity of tracking changes in genetic material as new SARS-CoV-2 variants emerge. Here we demonstrate that RNA-based, single-molecule conductance experiments can be used to identify specific variants of SARS-CoV-2. To this end, we i) select target sequences of interest for specific variants, ii) utilize single-molecule break junction measurements to obtain conductance histograms for each sequence and its potential mutations, and iii) employ the XGBoost machine learning classifier to rapidly identify the presence of target molecules in solution with a limited number of conductance traces. This approach allows high-specificity and high-sensitivity detection of RNA target sequences less than 20 base pairs in length by utilizing a complementary DNA probe capable of binding to the specific target. We use this approach to directly detect SARS-CoV-2 variants of concerns B.1.1.7 (Alpha), B.1.351 (Beta), B.1.617.2 (Delta), and B.1.1.529 (Omicron) and further demonstrate that the specific sequence conductance is sensitive to nucleotide mismatches, thus broadening the identification capabilities of the system. Thus, our experimental methodology detects specific SARS-CoV-2 variants, as well as recognizes the emergence of new variants as they arise. 

Polymerase chain reaction (PCR) has long been the mainstay in genetic sequencing and identification. Irrespective of whether short read or long read technologies are adopted, PCR methods are generally time consuming and expensive. Recently, an all-electronic approach, the so-called Single Molecule Break Junction (SMBJ) method, has been proposed as a possible alternative to PCR. In this article, we evaluate the performance of four different classifier models on the current signatures of ten short strand sequences, including a pair that differs by a single mismatch. We find that a gradient boosted tree classifier model achieves impressive accuracies, ranging from approximately 96% for molecules differing by a single mismatch to 99.5% otherwise.