{"doi":"10.1101/2024.06.04.596709","title":"Cross-species modeling of plant genomes at single nucleotide resolution using a pre-trained DNA language model","abstract":"Interpreting function and fitness effects in diverse plant genomes requires transferable models. Language models (LMs) pre-trained on large-scale biological sequences can learn evolutionary conservation and offer cross-species prediction better than supervised models through fine-tuning limited labeled data. We introduce PlantCaduceus, a plant DNA LM based on the Caduceus and Mamba architectures, pre-trained on a curated dataset of 16 Angiosperm genomes. Fine-tuning PlantCaduceus on limited labeled Arabidopsis data for four tasks, including predicting translation initiation/termination sites and splice donor and acceptor sites, demonstrated high transferability to 160 million year diverged maize, outperforming the best existing DNA LM by 1.45 to 7.23-fold. PlantCaduceus is competitive to state-of-the-art protein LMs in terms of deleterious mutation identification, and is threefold better than PhyloP. Additionally, PlantCaduceus successfully identifies well-known causal variants in both Arabidopsis and maize. Overall, PlantCaduceus is a versatile DNA LM that can accelerate plant genomics and crop breeding applications.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2024,"id":484092,"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":17,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9484,"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":1206929,"name":"Aaron Gokaslan","orcid":"0000-0002-3575-2961","position":1,"is_corresponding":false},{"id":1206927,"name":"Yair Schiff","orcid":"0000-0003-0748-3706","position":2,"is_corresponding":false},{"id":1022363,"name":"Ana Berthel","orcid":"0000-0001-6849-982X","position":3,"is_corresponding":false},{"id":1325673,"name":"Zong-Yan Liu","orcid":"0000-0002-9039-7843","position":4,"is_corresponding":false},{"id":1325674,"name":"Wei‐Yun Lai","orcid":"0000-0002-5101-8695","position":5,"is_corresponding":false},{"id":1325675,"name":"Zachary Miller","orcid":"0000-0002-5454-4527","position":6,"is_corresponding":false},{"id":816377,"name":"Armin Scheben","orcid":"0000-0002-2230-2013","position":7,"is_corresponding":false},{"id":1325676,"name":"Michelle C. Stitzer","orcid":"0000-0003-4140-3765","position":8,"is_corresponding":false},{"id":1075793,"name":"M. Cinta Romay","orcid":"0000-0001-9309-1586","position":9,"is_corresponding":false},{"id":19346,"name":"Edward S. Buckler","orcid":"0000-0002-3100-371X","position":10,"is_corresponding":false},{"id":1194764,"name":"Volodymyr Kuleshov","orcid":"0000-0002-5150-3308","position":11,"is_corresponding":false},{"id":1325672,"name":"Jingjing Zhai","orcid":"0000-0002-1535-3103","position":0,"is_corresponding":true}],"reference_count":59,"raw_metadata":null,"created_at":"2026-07-19T02:07:38.055693Z","pmid":"38895432","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":[]}