Learning cis-regulatory principles of ADAR-based RNA editing from CRISPR-mediated mutagenesis
Problem
It was poorly understood how RNA sequence and structure determine the efficiency and specificity of A-to-I RNA editing, hindering interpretation of known editing events and engineering of desired edits.
Why it matters
Understanding these rules is needed for functional interpretation of editing events and targeted transcriptome engineering.
What the AI did
Machine learning integrated RNA sequence features and computationally predicted structures to model editing levels measured by deep sequencing, identifying cis-regulatory features of RNA editing.
Results
Models explained over 70% of variation in editing levels across three proof-of-concept editing substrates.
Impact
The approach identified cis-regulatory principles of ADAR editing, demonstrated as proof-of-concept on three substrates.
Record
| Venue | Nature Communications |
|---|---|
| Published | 2021 |
| Work type | article |
| Authors | Xin Liu, Tao Sun, Anna Shcherbina, Qin Li, Inga Jarmoskaite, Kalli Kappel, Gokul Ramaswami, Rhiju Das et al. |
| Citations | 25 (OpenAlex, at import time) |
| References | 79 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1038/s41467-021-22489-2 |
| Open access | gold |
| Keywords | RNA editing, ADAR, CRISPR mutagenesis, machine learning, cis-regulation |
Importance rating
real but narrower advance
Basic-science advance in deciphering RNA editing rules; proof-of-concept on three substrates, no stated scale of beneficiaries.