Nanopore and Nanochannel Transport Studies

This paper introduces a method to measure DNA replication dynamics on single molecules using nanopore sequencing and applies it to yeast to discover new replication origins.

This study improves the detection of m6A in bacterial DNA using nanopore sequencing with a binary classifier, highlighting the importance of read quality and comparing results to PacBio methylation calls.

This paper develops a Cas12a-based enrichment strategy and algorithm for nanopore sequencing to quantify repeat expansions and their methylation status.

The paper uses Langevin molecular dynamics to study protein translocation through an α-hemolysin pore under periodic external forces, finding that time-dependent forces enhance translocation efficiency.

The paper uses nanopore sequencing and a neural network to identify BrdU-labelled DNA tracts in yeast during replication, showing they align with early-firing origins.

Chiron is a deep learning model that directly translates nanopore raw signals into DNA sequences without segmentation, achieving high accuracy and speed with minimal training data.

The paper demonstrates a minimalist method for protein identification using isoelectric point and nanopore measurements, achieving over 90% identification rates across multiple proteomes.

Base-by-base tests of raw nanopore current, native versus amplified DNA, detect 4mC, 5mC and 6mA without training data, recovering seven methylases' motifs (AUC 0.62–0.97).