{"doi":"10.1093/bioinformatics/btad709","title":"Interpretable prediction models for widespread m6A RNA modification across cell lines and tissues","abstract":"MOTIVATION: RNA N6-methyladenosine (m6A) in Homo sapiens plays vital roles in a variety of biological functions. Precise identification of m6A modifications is thus essential to elucidation of their biological functions and underlying molecular-level mechanisms. Currently available high-throughput single-nucleotide-resolution m6A modification data considerably accelerated the identification of RNA modification sites through the development of data-driven computational methods. Nevertheless, existing methods have limitations in terms of the coverage of single-nucleotide-resolution cell lines and have poor capability in model interpretations, thereby having limited applicability. RESULTS: In this study, we present CLSM6A, comprising a set of deep learning-based models designed for predicting single-nucleotide-resolution m6A RNA modification sites across eight different cell lines and three tissues. Extensive benchmarking experiments are conducted on well-curated datasets and accordingly, CLSM6A achieves superior performance than current state-of-the-art methods. Furthermore, CLSM6A is capable of interpreting the prediction decision-making process by excavating critical motifs activated by filters and pinpointing highly concerned positions in both forward and backward propagations. CLSM6A exhibits better portability on similar cross-cell line/tissue datasets, reveals a strong association between highly activated motifs and high-impact motifs, and demonstrates complementary attributes of different interpretation strategies. AVAILABILITY AND IMPLEMENTATION: The webserver is available at http://csbio.njust.edu.cn/bioinf/clsm6a. The datasets and code are available at https://github.com/zhangying-njust/CLSM6A/.","journal":"Bioinformatics","year":2023,"id":328170,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":24,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9371,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1049532,"name":"Zhikang Wang","orcid":"0000-0001-9587-1965","position":1,"is_corresponding":false},{"id":1049533,"name":"Yiwen Zhang","orcid":"0000-0001-9570-4147","position":2,"is_corresponding":false},{"id":579387,"name":"Shanshan Li","orcid":"0000-0002-9021-8470","position":3,"is_corresponding":false},{"id":22518,"name":"Yuming Guo","orcid":"0000-0002-1766-6592","position":4,"is_corresponding":false},{"id":258542,"name":"Jiangning Song","orcid":"0000-0001-8031-9086","position":5,"is_corresponding":false},{"id":626617,"name":"Dong‐Jun Yu","orcid":"0000-0002-6786-8053","position":6,"is_corresponding":false},{"id":648023,"name":"Ying Zhang","orcid":"0000-0003-1792-0121","position":0,"is_corresponding":true}],"reference_count":51,"raw_metadata":null,"created_at":"2026-07-19T01:08:52.196069Z","pmid":"37995291","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":[]}