Oligo replication advantage driven by GC content and Gibbs free energy

Advances in Genotyping Detection of Fragmented Nucleic Acids

Single nucleotide variant (SNV) detection is pivotal in various fields, including disease diagnosis, viral screening, genetically modified organism (GMO) identification, and genotyping. However, detecting SNVs presents significant challenges due to the fragmentation of nucleic acids caused by cellular apoptosis, molecular shearing, and physical degradation processes such as heating. Fragmented nucleic acids often exhibit variable lengths and inconsistent breakpoints, complicating the accurate detection of SNVs. This article delves into the underlying causes of nucleic acid fragmentation and synthesizes the strengths and limitations of next-generation sequencing technology, high-resolution melting curves, molecular probes, and CRISPR-based approaches for SNV detection in fragmented nucleic acids. By providing a detailed comparative analysis, it seeks to offer valuable insights for researchers working to overcome the challenges of SNV detection in fragmented samples, ultimately advancing the accurate and efficient detection of single nucleotide variants across diverse applications.

2,6-diaminopurine (Z)-containing toehold probes improve genotyping sensitivity

2,6-diaminopurine (Z), a naturally occurring noncanonical nucleotide base found in bacteriophages, enhances DNA hybridization by forming three hydrogen bonds with thymine (T). These distinct biochemical characteristics make it particularly valuable in applications that rely on the thermodynamics of DNA hybridization. However, the practical use of Z-containing oligos is limited by their high production cost and the challenges associated with their synthesis. Here, we developed an efficient and cost-effective approach to synthesize Z-containing oligos of high quality based on an isothermal strand displacement reaction. These newly synthesized Z-oligos are then employed as toehold-blockers in an isothermal genotyping assay designed to detect rare single nucleotide variations (SNV). When compared with their counterparts containing the standard adenine (A) base, the Z-containing blockers significantly enhance the accuracy of identifying SNV. Overall, our innovative methodology in the synthesis of Z-containing oligos, which can also be used to incorporate other unconventional and unnatural bases into oligonucleotides, is anticipated to be adopted for diverse applications, including genotyping, biosensing, and gene therapy.

A digital twin for DNA data storage based on comprehensive quantification of errors and biases

Archiving data in synthetic DNA offers unprecedented storage density and longevity. Handling and storage introduce errors and biases into DNA-based storage systems, necessitating the use of Error Correction Coding (ECC) which comes at the cost of added redundancy. However, insufficient data on these errors and biases, as well as a lack of modeling tools, limit data-driven ECC development and experimental design. In this study, we present a comprehensive characterisation of the error sources and biases present in the most common DNA data storage workflows, including commercial DNA synthesis, PCR, decay by accelerated aging, and sequencing-by-synthesis. Using the data from 40 sequencing experiments, we build a digital twin of the DNA data storage process, capable of simulating state-of-the-art workflows and reproducing their experimental results. We showcase the digital twin's ability to replace experiments and rationalize the design of redundancy in two case studies, highlighting opportunities for tangible cost savings and data-driven ECC development.

Sustainable DNA Data Storage on Cellulose Paper

DNA is a promising material for high density and long-term archival data storage. In addition to algorithms for encoding digital information into DNA sequences, the DNA writing (chemical synthesis) and reading (DNA sequencing), the preservation of DNA mixtures with high sequence diversity is another critical issue for sustainable, long-term, and large-scale DNA data storage. Here, this work demonstrates a method for low-cost, convenient and sustainable DNA data storage on cellulose paper. A DNA pool comprising thousands of sequences, in which archival data are encoded, is conveniently stored on a cellulose paper with a calculated density as high as 15 TB per mm3 through electrostatic adsorption. This work demonstrates that these digitally encoded DNA pools can be stable for years on the cellulose paper after drying even when directly exposed to air. Furthermore, the reversible electrostatic adsorption enables repeated loading/retrieval of DNA on/off cellulose paper. Therefore, this sustainable DNA preservation on cellulose paper through the convenient electrostatic adsorption exhibits a great advantage in terms of storage capacity and cost that is crucial for practical systems to achieve large-scale and long-time data storage.

Adaptive Savitzky–Golay Filters for Analysis of Copy Number Variation Peaks from Whole-Exome Sequencing Data

Copy number variation (CNV) is a form of structural variation in the human genome that provides medical insight into complex human diseases; while whole-genome sequencing is becoming more affordable, whole-exome sequencing (WES) remains an important tool in clinical diagnostics. Because of its discontinuous nature and unique characteristics of sparse target-enrichment-based WES data, the analysis and detection of CNV peaks remain difficult tasks. The Savitzky–Golay (SG) smoothing is well known as a fast and efficient smoothing method. However, no study has documented the use of this technique for CNV peak detection. It is well known that the effectiveness of the classical SG filter depends on the proper selection of the window length and polynomial degree, which should correspond with the scale of the peak because, in the case of peaks with a high rate of change, the effectiveness of the filter could be restricted. Based on the Savitzky–Golay algorithm, this paper introduces a novel adaptive method to smooth irregular peak distributions. The proposed method ensures high-precision noise reduction by dynamically modifying the results of the prior smoothing to automatically adjust parameters. Our method offers an additional feature extraction technique based on density and Euclidean distance. In comparison to classical Savitzky–Golay filtering and other peer filtering methods, the performance evaluation demonstrates that adaptive Savitzky–Golay filtering performs better. According to experimental results, our method effectively detects CNV peaks across all genomic segments for both short and long tags, with minimal peak height fidelity values (i.e., low estimation bias). As a result, we clearly demonstrate how well the adaptive Savitzky–Golay filtering method works and how its use in the detection of CNV peaks can complement the existing techniques used in CNV peak analysis.