{"doi":"10.1101/2021.04.26.441443","title":"Secondary Structure Prediction for RNA Sequences Including N <sup>6</sup> -methyladenosine","abstract":"Abstract There is increasing interest in the roles played by covalently modified nucleotides in mRNAs and non-coding RNAs. New high-throughput sequencing technologies localize these modifications to exact nucleotide positions. There has been, however, and inability to account for these modifications in secondary structure prediction because of a lack of software tools for handling modifications and a lack of thermodynamic parameters for modifications. Here, we report that we solved these issues for N 6 -methyladenosine (m 6 A), for the first time allowing secondary structure prediction for a nucleotide alphabet of A, C, G, U, and m 6 A. We revised the RNAstructure software package to work with any user-defined alphabet of nucleotides. We also developed a set of nearest neighbor parameters for helices and loops containing m 6 A, using a set of 45 optical melting experiments. Interestingly, N 6 -methylation decreases the folding stability of structures with adenosines in the middle of a helix, has little effect on the folding stability of adenosines at the ends of helices, and stabilizes the folding stability for structures with unpaired adenosines stacked on the end of a helix. The parameters were tested against an additional two melting experiments, including a consensus sequence for methylation and an m 6 A dangling end. The utility of the new software was tested using predictions of the structure of a molecular switch in the MALAT1 lncRNA, for which a conformation change is triggered by methylation. Additionally, human transcriptome-wide calculations for the effect of N 6 -methylation on the probability of an adenosine being buried in a helix compare favorably with PARS structure mapping data. Now users of RNAstructure are able to develop hypothesis for structure-function relationships for RNAs with m 6 A, including conformational switching triggered by methylation.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2021,"id":220626,"datarank":0.10397207708399181,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.0,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9493,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":820093,"name":"Xiaoju Zhang","orcid":"0000-0001-7040-5490","position":1,"is_corresponding":false},{"id":820094,"name":"Richard M. Watson","orcid":"0000-0002-3815-7236","position":2,"is_corresponding":false},{"id":754464,"name":"Ryszard Kierzek","orcid":"0000-0002-7644-0016","position":3,"is_corresponding":false},{"id":97947,"name":"David H. Mathews","orcid":"0000-0002-2907-6557","position":4,"is_corresponding":false},{"id":754465,"name":"Elżbieta Kierzek","orcid":"0000-0002-5563-003X","position":0,"is_corresponding":true}],"reference_count":116,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T23:53:46.965811Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}