{"doi":"10.7554/elife.96738.2","title":"Regulatory genome annotation of 33 insect species","abstract":"Abstract Annotation of newly-sequenced genomes frequently includes genes, but rarely covers important non-coding genomic features such as the cis-regulatory modules—e.g., enhancers and silencers—that regulate gene expression. Here, we begin to remedy this situation by developing a workflow for rapid initial annotation of insect regulatory sequences, and provide a searchable database resource with enhancer predictions for 33 genomes. Using our previously-developed SCRMshaw computational enhancer prediction method, we predict over 2.8 million regulatory sequences along with the tissues where they are expected to be active, in a set of insect species ranging over 360 million years of evolution. Extensive analysis and validation of the data provides several lines of evidence suggesting that we achieve a high true-positive rate for enhancer prediction. One, we show that our predictions target specific loci, rather than random genomic locations. Two, we predict enhancers in orthologous loci across a diverged set of species to a significantly higher degree than random expectation would allow. Three, we demonstrate that our predictions are highly enriched for regions of accessible chromatin. Four, we achieve a validation rate in excess of 70% using in vivo reporter gene assays. As we continue to annotate both new tissues and new species, our regulatory annotation resource will provide a rich source of data for the research community and will have utility for both small-scale (single gene, single species) and large-scale (many genes, many species) studies of gene regulation. In particular, the ability to search for functionally-related regulatory elements in orthologous loci should greatly facilitate studies of enhancer evolution even among distantly related species.","journal":"eLife","year":2024,"id":491848,"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":22.178386323199504,"corpus_rank":9377,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.8099,"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":1293147,"name":"Ellen Tieke","orcid":null,"position":1,"is_corresponding":false},{"id":1044355,"name":"Kevin D. Deem","orcid":"0000-0002-1539-823X","position":2,"is_corresponding":false},{"id":1293148,"name":"Jabale Rahmat","orcid":null,"position":3,"is_corresponding":false},{"id":1149891,"name":"Tiffany Dong","orcid":"0000-0002-0086-9289","position":4,"is_corresponding":false},{"id":1292638,"name":"Xinbo Huang","orcid":"0000-0002-9500-4586","position":5,"is_corresponding":false},{"id":1292639,"name":"Yoshinori Tomoyasu","orcid":"0000-0001-9824-3454","position":6,"is_corresponding":false},{"id":457264,"name":"Marc S. Halfon","orcid":"0000-0002-4149-2705","position":7,"is_corresponding":false},{"id":740790,"name":"Hasiba Asma","orcid":"0000-0002-9304-2685","position":0,"is_corresponding":true}],"reference_count":80,"raw_metadata":null,"created_at":"2026-07-19T02:08:45.247225Z","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":[]}