Evaluating DCA-based method performances for RNA contact prediction by a well-curated data set
- 1John von Neumann Institute for Computing, Jülich Supercomputing Centre, Forschungszentrum Jülich, 52428 Jülich, Germany
- 2Steinbuch Centre for Computing, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany
- 3Department of Physics, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany
- Corresponding author: al.schug{at}fz-juelich.de
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↵4 These authors contributed equally to this work.
Abstract
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.
Keywords
- direct coupling analysis
- multiple sequence alignment
- RNA contact prediction
- RNA structure prediction
Footnotes
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Article is online at http://www.rnajournal.org/cgi/doi/10.1261/rna.073809.119.
- Received October 28, 2019.
- Accepted March 31, 2020.
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