{"doi":"10.1093/bib/bbaf360","title":"MambaCpG: an accurate model for single-cell DNA methylation status imputation using mamba","abstract":"<jats:title>Abstract</jats:title>\n               <jats:p>DNA methylation is a key epigenetic modification involved in biological processes and disease development. The accurate analysis of DNA methylation site information is of significant biological importance. Despite advances in single-cell sequencing, data sparsity due to low cytosine-phosphate-guanine (CpG) coverage remains a challenge. To address this, we introduce MambaCpG, a single-cell DNA methylation state imputation model based on the Mamba block. MambaCpG integrates the methylation matrix and DNA sequence context and uses bidirectional Mamba blocks to obtain embeddings, effectively capturing the long-range dependencies between CpG sites within DNA methylation patterns. Experiments on seven datasets of various scales demonstrate that MambaCpG outperforms existing models on large, highly sparse datasets while showing competitive performance on smaller datasets. MambaCpG has lower parameter and memory requirements, making it suitable for practical applications. MambaCpG also reveals ultra-long-range dependencies and provides new insights into DNA methylation patterning.</jats:p>","journal":"Briefings in Bioinformatics","year":2025,"id":633584,"datarank":0.24141568686511508,"base_score":1.6094379124341003,"endowment":1.6094379124341003,"self_citation_contribution":0.24141568686511508,"citation_network_contribution":0.0,"self_endowment_contribution":0.24141568686511508,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":289920,"name":"Ze Li","orcid":"0000-0003-4179-8672","position":1,"is_corresponding":false},{"id":203250,"name":"Qian Mao","orcid":null,"position":2,"is_corresponding":false},{"id":1642844,"name":"Tingwei Chen","orcid":null,"position":3,"is_corresponding":false},{"id":785621,"name":"Yiran Zhang","orcid":"0000-0002-5796-5490","position":4,"is_corresponding":false},{"id":1642845,"name":"Bingle Li","orcid":null,"position":5,"is_corresponding":false},{"id":627452,"name":"Zheng Zhao","orcid":"0000-0001-7068-1321","position":6,"is_corresponding":false},{"id":1642846,"name":"Xiaoya Fan","orcid":null,"position":7,"is_corresponding":false},{"id":320621,"name":"Qi Zhao","orcid":"0000-0001-9713-1864","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"MambaCpG: an accurate model for single-cell DNA methylation status imputation using mamba","abstract":"<jats:title>Abstract</jats:title>\n               <jats:p>DNA methylation is a key epigenetic modification involved in biological processes and disease development. The accurate analysis of DNA methylation site information is of significant biological importance. Despite advances in single-cell sequencing, data sparsity due to low cytosine-phosphate-guanine (CpG) coverage remains a challenge. To address this, we introduce MambaCpG, a single-cell DNA methylation state imputation model based on the Mamba block. MambaCpG integrates the methylation matrix and DNA sequence context and uses bidirectional Mamba blocks to obtain embeddings, effectively capturing the long-range dependencies between CpG sites within DNA methylation patterns. Experiments on seven datasets of various scales demonstrate that MambaCpG outperforms existing models on large, highly sparse datasets while showing competitive performance on smaller datasets. MambaCpG has lower parameter and memory requirements, making it suitable for practical applications. MambaCpG also reveals ultra-long-range dependencies and provides new insights into DNA methylation patterning.</jats:p>","is_dataset_classified":null,"base_score":1.6094379124341003,"endowment":1.6094379124341003,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"40717284","pmcid":"PMC12301186","openalex_id":"https://openalex.org/W4412683136","authors":[],"funders":[{"funder_name":"Fundamental Research Funds for Public Universities in Liaoning","grant_id":"LJ242410140009","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"62002056","title":null},{"funder_name":"National Training Program of Innovation and Entrepreneurship for Undergraduates","grant_id":"S202410145029","title":null}],"total_grants":3,"fwci":1.6295,"citation_percentile":0.83464592,"influential_citations":0,"citation_trend":[{"year":2025,"count":1},{"year":2026,"count":3}],"oa_status":"gold","license":"cc-by-nc","oa_locations":[{"url":"https://academic.oup.com/bib/article-pdf/26/4/bbaf360/63857630/bbaf360.pdf","host_type":"journal"},{"url":"https://academic.oup.com/bib/article-pdf/26/4/bbaf360/63857630/bbaf360.pdf","host_type":"publisher"},{"url":"https://doi.org/10.1093/bib/bbaf360","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/40717284","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/12301186","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC12301186","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC12301186?pdf=render","host_type":"Europe_PMC"}],"fields_of_study":["Epigenetics and DNA Methylation","Single-cell and spatial transcriptomics","Error Correcting Code Techniques"],"mesh_terms":["Algorithms","Humans","Software","Sequence Analysis, DNA","CpG Islands","DNA Methylation","Computational Biology","Epigenesis, Genetic","Single-Cell Analysis"],"keywords":["DNA methylation","CpG site","Epigenetics","Methylation","Computer science","Computational biology","DNA","Illumina Methylation Assay","Imputation (statistics)","DNA sequencing","Context (archaeology)","Biology","Genetics","Gene","Gene expression","Machine learning","Missing data","Single-cell","Deep Learning","Mamba","Mambacpg","Dna Methylation Imputation"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-06T12:19:09.208467Z","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":[]}