TY  - JOUR
AU  - Upadhyay, Utkarsh
AU  - Pucci, Fabrizio
AU  - Herold, Julian
AU  - Schug, Alexander
TI  - NucleoSeeker—precision filtering of RNA databases to curate high-quality datasets
JO  - NAR: genomics and bioinformatics
VL  - 7
IS  - 1
SN  - 2631-9268
CY  - Oxford
PB  - Oxford University Press
M1  - FZJ-2025-02908
SP  - lqaf021
PY  - 2025
AB  - The structural prediction of biomolecules via computational methods complements the often involved wet-lab experiments. Unlike protein structure prediction, RNA structure prediction remains a significant challenge in bioinformatics, primarily due to the scarcity of annotated RNA structure data and its varying quality. Many methods have used this limited data to train deep learning models but redundancy, data leakage and bad data quality hampers their performance. In this work, we present NucleoSeeker, a tool designed to curate high-quality, tailored datasets from the Protein Data Bank (PDB) database. It is a unified framework that combines multiple tools and streamlines an otherwise complicated process of data curation. It offers multiple filters at structure, sequence, and annotation levels, giving researchers full control over data curation. Further, we present several use cases. In particular, we demonstrate how NucleoSeeker allows the creation of a nonredundant RNA structure dataset to assess AlphaFold3’s performance for RNA structure prediction. This demonstrates NucleoSeeker’s effectiveness in curating valuable nonredundant tailored datasets to both train novel and judge existing methods. NucleoSeeker is very easy to use, highly flexible, and can significantly increase the quality of RNA structure datasets.
LB  - PUB:(DE-HGF)16
C6  - 40104673
UR  - <Go to ISI:>//WOS:001446715300001
DO  - DOI:10.1093/nargab/lqaf021
UR  - https://juser.fz-juelich.de/record/1043532
ER  -