Plots and Data for Contact Map Analysis

Git repository with Data and Analysis for the publication Influence of Contact Map Topology on RNA Structure Prediction ¹.

Requirements

To execute all the scripts you need:

• pyDCA ²
• self written Helper library see here
• for effective sequence number calculation sequeff, see here
• Testset from Pucci et al.³
• to include all dependencies we highly recommend using pixi

Structure

FinalConfig

Simulation data w/o any restraints

Columns results.csv:

• RNA: ID of the representative from https://www.rcsb.org/
• Clustering File: Clustered file, where all represents a REMC with 10 Replicas and single only one Replica, postfix A for an energy threshold of $0.01$ and B for $0.005$, i.e. only the $1%$ or $0.05%$ frames with the lowest energy are considered for clustering (for more details see xxx)
• Threshold: Distance threshold for the clustering in $\mathring{A}$
• Cluster: Cluster number
• Configuration: $1$ for Standard Config
• size: Number of residues

contact_distribution

Comparison of different contact map topologies.

false_contacts

Influence of false positives on the structure prediction

applications

Application of the different scores to an additional validation set (included in the folder). Validation set was created with NucleoSeeker ³. All parameters can be found in the original publication .

Plots

Additional figures for the publication.

Footnotes

1. Faber, C., Upadhyay, U., Taubert, O. and Schug, A. (2025) Influence of contact map topology on RNA structure prediction. Nucleic Acids Research, 53(22), gkaf1370. ↩

2. Zerihun,M.B., Pucci,F., Peter,E.K. and Schug,A. (2020) pydca v1.0: a comprehensive software for direct coupling analysis of RNA and protein sequences. Bioinformatics, 36, 2264–2265. ↩

3. Pucci,F., Zerihun,M.B., Peter,E.K. and Schug,A. (2020) Evaluating DCA-based method performances for RNA contact prediction by a well-curated data set. RNA, 26(7), 794-802. ↩ ↩²