NAD: Noise-augmented direct sequencing of target nucleic acids by augmenting with noise and selective sampling
Year: 2025
Type: article
Abstract: Next-generation sequencing necessitates a minimum quantity and concentration of DNA/RNA samples, typically achieved through amplification using the PCR technique. However, this amplification step introduces several drawbacks to biological insights, including PCR bias and the loss of epigenetic information. The advent of long-read sequencing technologies facilitates direct sequencing, with the primary constraint being the limited amount of DNA/RNA present in biological samples. Here, we present a novel method called Noise-Augmented Direct (NAD) sequencing that enables the direct sequencing of target DNA even when it falls below the minimum quantity and concentration required for long-read sequencing by augmenting with noise DNA and adaptive sampling. Adaptive sampling is an emerging technology of nanopore sequencing, allowing the enhanced sequencing of target DNA by selectively depleting noise DNA. In this study, we use the DNA standard of the Lambda phage genome as the noise DNA to augment samples containing low amounts of bacterial genomes (1 ng to 300 ng). The results with cost-effective flow cells indicate that NAD sequencing successfully detects the target DNA with an input quantity as low as 1 ng, and the bacterial genome of Salmonella enterica can be assembled to 30% completion at an accuracy of 98% with an input quantity of 3 ng. With high throughput flow cells, the bacterial genome of Pseudonomas aeruginas was assembled to near completion (99.9%) at an accuracy of 99.97% with an input quantity of 300 ng. This proof-of-concept study demonstrates the potential of NAD sequencing in enhancing the robustness of long-read sequencing with small input DNA/RNA samples with noise augmentation and adaptive sampling.
Source: F1000Research
Author: Hyunjin Shim
Institution: California State University, Fresno
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $1,350
OpenAlex ID: W4409338697