Fig. 2: Benchmarking KARR-seq, RIC-seq and PARIS.

a, Average Pearson correlation between the interaction maps of KARR-seq (K562 and HEK293T cells), RIC-seq (HeLa cells) and PARIS (HEK293T cells). b, MFE for RNA–RNA interactions detected using KARR-seq, PARIS and RIC-seq within TERC and U1 transcripts, respectively. Interactions were grouped into ‘secondary’, ‘tertiary’ and ‘novel’. ‘Secondary’ refers to the interactions that match secondary structure prediction. ‘Tertiary’ refers to spatially proximal RNA regions revealed by the cryo-EM structure that do not correspond to secondary structures. ‘Novel’ refers to interactions that are not supported by secondary structures or cryo-EM structures. c, Circos plots showing the RNA–RNA interaction landscape revealed by KARR-seq, PARIS and RIC-seq. The width of the link between two RNA categories indicates the relative abundance of chimeric reads taken by interactions between these two categories. d, Left, the physical distance map of TERC revealed by the cryo-EM structure of TERC. Right, higher-order structures of TERC detected by KARR-seq, PARIS and RIC-seq under the same sequencing depth. The blue dots denote base-pairing secondary structures acquired from the Rfam annotations (RF00024). e, The ROC–AUC curves for KARR-seq, RIC-seq and PARIS for detecting higher-order structures of TERC, 18S, 28S and U3. The dashed lines denote random classifiers. RIC-seq and PARIS data were acquired from the Gene Expression Omnibus (RIC-seq: GSE127188; PARIS: GSE74353). Cryo-EM structures were acquired from the Protein Data Bank (accession codes: 7QXB for TERC, 6QX9 for U3 and 4V6X for 18S and 28S). KARR-seq was performed in two biological replicates.