Neda Ghohabi Esfahani

The paper uses in vitro transcribed RNA from genomic DNA to benchmark RNA modification detection by Nanopore direct RNA sequencing models.

tRNAZAP is a deep learning toolkit for analyzing nanopore tRNA sequencing data, achieving high accuracy in segmentation and classification tasks.

This paper develops a computational strategy for analyzing nanopore ionic current signals from synthetic proteins, achieving high accuracy in charge-based classification of amino acids.

The paper introduces tRNAZAP, a deep learning method using nanopore ionic current signals to classify tRNA isodecoders, achieving better alignment results than BWA-MEM in E. coli and S. cerevisiae.

The paper profiles RNA modifications in MS2 bacteriophage using LC-MS/MS and Nanopore DRS, finding {Psi} as the predominant modification.

This paper evaluates updates to Nanopore direct RNA sequencing for detecting RNA modifications, showing improved accuracy in identifying pseudouridine and N6-methyladenosine.