TY  - JOUR
AU  - Pucci, Fabrizio
AU  - Zerihun, Mehari B.
AU  - Peter, Emanuel K.
AU  - Schug, Alexander
TI  - Evaluating DCA-based method performances for RNA contact prediction by a well-curated data set
JO  - RNA
VL  - 26
IS  - 7
SN  - 1469-9001
CY  - Stanford, Calif.
PB  - HighWire Press
M1  - FZJ-2020-02633
SP  - 794 - 802
PY  - 2020
AB  - RNA molecules play many pivotal roles in a cell that are still not fully understood. Any detailed understanding of RNA function requires knowledge of its three-dimensional structure, yet experimental RNA structure resolution remains demanding. Recent advances in sequencing provide unprecedented amounts of sequence data that can be statistically analyzed by methods such as direct coupling analysis (DCA) to determine spatial proximity or contacts of specific nucleic acid pairs, which improve the quality of structure prediction. To quantify this structure prediction improvement, we here present a well curated data set of about 70 RNA structures of high resolution and compare different nucleotide–nucleotide contact prediction methods available in the literature. We observe only minor differences between the performances of the different methods. Moreover, we discuss how robust these predictions are for different contact definitions and how strongly they depend on procedures used to curate and align the families of homologous RNA sequences.
LB  - PUB:(DE-HGF)16
C6  - pmid:32276988
UR  - <Go to ISI:>//WOS:000541897400003
DO  - DOI:10.1261/rna.073809.119
UR  - https://juser.fz-juelich.de/record/878102
ER  -