{"doi":"10.1101/2020.09.01.278424","title":"Accurate transcription start sites enable mining for the cis-regulatory determinants of tissue specific gene expression","abstract":"Abstract Across tissues, gene expression is regulated by a combination of determinants, including the binding of transcription factors (TFs), along with other aspects of cellular state. Recent studies emphasize the importance of both genetic and epigenetic states – TF binding sites and binding site chromatin accessibility have emerged as potentially causal determinants of tissue specificity. To investigate the relative contributions of these determinants, we constructed three genome-scale datasets for both root and shoot tissues of the same Arabidopsis thaliana plants: TSS-seq data to identify Transcription Start Sites, OC-seq data to identify regions of Open Chromatin, and RNA-seq data to assess gene expression levels. For genes that are differentially expressed between root and shoot, we constructed a machine learning model predicting tissue of expression from chromatin accessibility and TF binding information upstream of TSS locations. The resulting model was highly accurate (over 90% auROC and auPRC), and our analysis of model contributions (feature weights) strongly suggests that patterns of TF binding sites within ∼500 nt TSS-proximal regions are predominant explainers of tissue of expression in most cases. Thus, in plants, cis-regulatory control of tissue-specific gene expression appears to be primarily determined by TSS-proximal sequences, and rarely by distal enhancer-like accessible chromatin regions. This study highlights the exciting future possibility of a native TF site-based design process for the tissue-specific targeting of plant gene promoters.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2020,"id":127308,"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":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9403,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":575991,"name":"Valerie N. Fraser","orcid":"0000-0003-1422-5289","position":1,"is_corresponding":false},{"id":575992,"name":"Sergei A. Filichkin","orcid":"0000-0002-0105-6431","position":2,"is_corresponding":false},{"id":576615,"name":"Maria G. Ivanchenko","orcid":null,"position":3,"is_corresponding":false},{"id":575993,"name":"Zachary A. Bright","orcid":"0000-0002-3149-7967","position":4,"is_corresponding":false},{"id":575994,"name":"Russell A. Gould","orcid":"0000-0002-1789-0631","position":5,"is_corresponding":false},{"id":575995,"name":"Olivia R. Ozguc","orcid":"0000-0001-5826-5819","position":6,"is_corresponding":false},{"id":575996,"name":"Shawn T. O’Neil","orcid":"0000-0001-6220-7080","position":7,"is_corresponding":false},{"id":575997,"name":"Molly Megraw","orcid":"0000-0001-6793-6151","position":8,"is_corresponding":false},{"id":575990,"name":"Mitra Ansariola","orcid":"0000-0003-0092-1435","position":0,"is_corresponding":true}],"reference_count":63,"raw_metadata":null,"created_at":"2026-07-18T23:15:30.930746Z","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":[]}