Sepideh Tavakoli

This paper develops machine learning models to quantitatively measure pseudouridine occupancy in human mRNAs using synthetic controls, revealing site-specific requirements for accurate classification.

The paper generates and characterizes modification-free RNA transcripts from human cell lines for use as controls in nanopore direct RNA sequencing analysis.

This study uses direct RNA sequencing to examine pseudouridylation dynamics in neurons under healthy and unhealthy conditions, finding changes in psi writer expression and site occupancy.

The paper develops a semi-quantitative method to detect pseudouridine modifications in human mRNAs using long-read nanopore sequencing and validates it with synthetic RNA controls.