Goldner, G. | bioRxiv Science

bioRxiv2025

Write and read: Harnessing Synthetic DNA Modifications for Nanopore Sequencing.

An exciting feature of nanopore sequencing is its ability to record multi-omic information on the same sequenced DNA molecule. Well-trained models allow the detection of nucleotide-specific molecular signatures through changes in ionic current as DNA molecules translocate through the nanopore. Thus, naturally occurring DNA modifications, such as DNA methylation and hydroxymethylation, may be recorded simultaneously with the genetic sequence. Additional genomic information, such as chromatin state or the locations of bound transcription factors, may also be recorded if their locations are chemically encoded into the DNA. Here, we present a versatile "write-and-read" framework, where chemo-enzymatic DNA labeling with unnatural synthetic tags results in predictable electrical fingerprints in nanopore sequencing. As a proof-of-concept, we explore a DNA glucosylation approach that selectively modifies 5-hydroxymethylcytosine (5hmC) with glucose or glucose-azide adducts. We demonstrate that these modifications generate distinct and reproducible electrical shifts, enabling the direct detection of chemically altered nucleotides. We further demonstrate that enzymatic alkylation, such as the enzymatic transfer of azide residues to the N6 position of adenines, also produces characteristic nanopore signal shifts relative to the native adenine and 6-methyladenine. Beyond direct nucleotide detection, this approach introduces new possibilities for bio-orthogonal DNA labeling, enabling an extended alphabet of sequence-specific detectable moieties. The future use of programmable chemical modifications for simultaneous analysis of multiple omics features on individual molecules opens new avenues for genetic research and discovery.

bioengineering↗

bioRxiv2021

Nanopore callers for epigenetics from limited supervised data

Nanopore sequencing platforms combined with supervised machine learning (ML) have been effective at detecting base modifications in DNA such as 5mC and 6mA. These ML-based nanopore callers have typically been trained on data that span all modifications on all possible DNA k-mer backgrounds--a complete training dataset. However, as nanopore technology is pushed to more and more epigenetic modifications, such complete training data will not be feasible to obtain. Nanopore calling has historically been performed with Hidden Markov Models (HMMs) that cannot make successful calls for k-mer contexts not seen during training because of their independent emission distributions. However, deep neural networks (DNNs), which share parameters across contexts, are increasingly being used as callers, often outperforming their HMM cousins. It stands to reason that a DNN approach should be able to better generalize to unseen k-mer contexts. Indeed, herein we demonstrate that a common DNN approach (DeepSignal) outperforms a common HMM approach (Nanopolish) in the incomplete data setting. Furthermore, we propose a novel hybrid HMM-DNN approach, Amortized-HMM, that outperforms both the pure HMM and DNN approaches on 5mC calling when the training data are incomplete. Such an approach is expected to be useful for calling 5hmC and combinations of cytosine modifications, where complete training data are not likely to be available.

bioinformatics↗