{"doi":"10.1093/bioinformatics/btae528","title":"Deep5hmC: predicting genome-wide 5-hydroxymethylcytosine landscape via a multimodal deep learning model","abstract":"MOTIVATION: 5-Hydroxymethylcytosine (5hmC), a crucial epigenetic mark with a significant role in regulating tissue-specific gene expression, is essential for understanding the dynamic functions of the human genome. Despite its importance, predicting 5hmC modification across the genome remains a challenging task, especially when considering the complex interplay between DNA sequences and various epigenetic factors such as histone modifications and chromatin accessibility. RESULTS: Using tissue-specific 5hmC sequencing data, we introduce Deep5hmC, a multimodal deep learning framework that integrates both the DNA sequence and epigenetic features such as histone modification and chromatin accessibility to predict genome-wide 5hmC modification. The multimodal design of Deep5hmC demonstrates remarkable improvement in predicting both qualitative and quantitative 5hmC modification compared to unimodal versions of Deep5hmC and state-of-the-art machine learning methods. This improvement is demonstrated through benchmarking on a comprehensive set of 5hmC sequencing data collected at four developmental stages during forebrain organoid development and across 17 human tissues. Compared to DeepSEA and random forest, Deep5hmC achieves close to 4% and 17% improvement of Area Under the Receiver Operating Characteristic (AUROC) across four forebrain developmental stages, and 6% and 27% across 17 human tissues for predicting binary 5hmC modification sites; and 8% and 22% improvement of Spearman correlation coefficient across four forebrain developmental stages, and 17% and 30% across 17 human tissues for predicting continuous 5hmC modification. Notably, Deep5hmC showcases its practical utility by accurately predicting gene expression and identifying differentially hydroxymethylated regions (DhMRs) in a case-control study of Alzheimer's disease (AD). Deep5hmC significantly improves our understanding of tissue-specific gene regulation and facilitates the development of new biomarkers for complex diseases. AVAILABILITY AND IMPLEMENTATION: Deep5hmC is available via https://github.com/lichen-lab/Deep5hmC.","journal":"Bioinformatics","year":2024,"id":445417,"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":7,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9441,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1261827,"name":"Sai Ritesh Thela","orcid":null,"position":1,"is_corresponding":false},{"id":890305,"name":"Fengdi Zhao","orcid":"0009-0007-8754-7955","position":2,"is_corresponding":false},{"id":305960,"name":"Bing Yao","orcid":"0000-0002-6657-2760","position":3,"is_corresponding":false},{"id":227213,"name":"Zhexing Wen","orcid":"0000-0002-1518-3845","position":4,"is_corresponding":false},{"id":307991,"name":"Peng Jin","orcid":"0000-0001-6137-6659","position":5,"is_corresponding":false},{"id":1261343,"name":"Jinying Zhao","orcid":"0000-0003-4192-5053","position":6,"is_corresponding":false},{"id":33191,"name":"Li Chen","orcid":"0000-0001-9372-5606","position":7,"is_corresponding":false},{"id":1163323,"name":"Xin Ma","orcid":"0000-0001-8209-3068","position":0,"is_corresponding":true}],"reference_count":54,"raw_metadata":null,"created_at":"2026-07-19T02:01:42.089235Z","pmid":"39196755","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":[]}