Ribonanza: deep learning of RNA structure through dual crowdsourcing

Author:

He ShujunORCID,Huang RuiORCID,Townley JillORCID,Kretsch Rachael C.ORCID,Karagianes Thomas G.,Cox David B.T.ORCID,Blair HamishORCID,Penzar DmitryORCID,Vyaltsev ValeriyORCID,Aristova ElizavetaORCID,Zinkevich ArseniiORCID,Bakulin ArtemyORCID,Sohn HoyeolORCID,Krstevski DanielORCID,Fukui Takaaki,Tatematsu Fumiya,Uchida YusukeORCID,Jang DonghoonORCID,Lee Jun SeongORCID,Shieh RogerORCID,Ma Tom,Martynov EduardORCID,Shugaev Maxim V.ORCID,Bukhari Habib S.T.,Fujikawa KazukiORCID,Onodera KazukiORCID,Henkel ChristofORCID,Ron ShlomoORCID,Romano JonathanORCID,Nicol John J.,Nye Grace P.ORCID,Wu YuanORCID,Choe ChristianORCID,Reade Walter, ,Das RhijuORCID

Abstract

AbstractPrediction of RNA structure from sequence remains an unsolved problem, and progress has been slowed by a paucity of experimental data. Here, we present Ribonanza, a dataset of chemical mapping measurements on two million diverse RNA sequences collected through Eterna and other crowdsourced initiatives. Ribonanza measurements enabled solicitation, training, and prospective evaluation of diverse deep neural networks through a Kaggle challenge, followed by distillation into a single, self-contained model called RibonanzaNet. When fine tuned on auxiliary datasets, RibonanzaNet achieves state-of-the-art performance in modeling experimental sequence dropout, RNA hydrolytic degradation, and RNA secondary structure, with implications for modeling RNA tertiary structure.

Publisher

Cold Spring Harbor Laboratory

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