{"title":"Cross-Model Uncertainty-Aware Minimal Editing of Cis-Regulatory Elements for Cell-Type-Selective Design.","authors":"Angran Xia, Changwei Wang, Yemao Xia","doi":"10.3390/genes17091062","DOIUrl":null,"url":null,"abstract":"<p><p><b>Background/Objectives:</b> Cis-regulatory elements (CREs) help mediate cell-type-selective gene expression, yet sequence-to-activity models are rarely converted into compact, experimentally tractable variant panels that retain natural regulatory scaffolds. We developed SafeEdit-CRE to select minimal edits that preserve cross-model specificity while reducing predictive uncertainty, yielding an auditable CRE variant library for reporter-screening prioritization. <b>Methods:</b> SafeEdit-CRE combines the published Malinois sequence-activity predictor with an architecture-distinct reviewer ensemble, validation-calibrated uncertainty, sequence-domain controls, and constrained beam search. Natural 200 nt CREs were edited under fixed substitution budgets of 1, 5, 10, or 20 nucleotides and evaluated by a separately trained multi-kernel ensemble kept entirely separate from candidate generation. <b>Results:</b> Across 600 held-out CRE parents in K562, HepG2, and SK-N-SH cells, SafeEdit-CRE achieved a mean cross-model specificity-margin gain of 0.877 versus 0.822 for greedy editing (paired difference 0.055; 95% CI 0.040-0.071). Relative to a compute-matched primary-model beam, SafeEdit-CRE yielded a similar cross-model specificity-margin gain (difference -0.004; 95% CI -0.011-0.004) with lower ensemble uncertainty (0.314 vs. 0.335). In a locked 90-parent benchmark, 60.6% of designs passed all nine prespecified sequence and model-agreement checks, compared with 38.8% for greedy and 13.1% for random substitution (paired difference 21.8 percentage points; 95% CI 18.3-25.3). <b>Conclusions:</b> SafeEdit-CRE reduced 3240 designs to 74 Tier A computational candidates prioritized for reporter screening across three cell types and four edit budgets. By separating search from cross-model audit and enforcing explicit edit budgets and sequence-domain safeguards, the framework provides a reproducible and auditable approach to computational CRE prioritization, regulatory-grammar studies, and synthetic regulatory-element design.</p>","PeriodicalId":12688,"journal":{"name":"Genes","volume":"17 9","pages":""},"PeriodicalIF":3.1000,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13606248/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Genes","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.3390/genes17091062","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 0
Abstract
Background/Objectives: Cis-regulatory elements (CREs) help mediate cell-type-selective gene expression, yet sequence-to-activity models are rarely converted into compact, experimentally tractable variant panels that retain natural regulatory scaffolds. We developed SafeEdit-CRE to select minimal edits that preserve cross-model specificity while reducing predictive uncertainty, yielding an auditable CRE variant library for reporter-screening prioritization. Methods: SafeEdit-CRE combines the published Malinois sequence-activity predictor with an architecture-distinct reviewer ensemble, validation-calibrated uncertainty, sequence-domain controls, and constrained beam search. Natural 200 nt CREs were edited under fixed substitution budgets of 1, 5, 10, or 20 nucleotides and evaluated by a separately trained multi-kernel ensemble kept entirely separate from candidate generation. Results: Across 600 held-out CRE parents in K562, HepG2, and SK-N-SH cells, SafeEdit-CRE achieved a mean cross-model specificity-margin gain of 0.877 versus 0.822 for greedy editing (paired difference 0.055; 95% CI 0.040-0.071). Relative to a compute-matched primary-model beam, SafeEdit-CRE yielded a similar cross-model specificity-margin gain (difference -0.004; 95% CI -0.011-0.004) with lower ensemble uncertainty (0.314 vs. 0.335). In a locked 90-parent benchmark, 60.6% of designs passed all nine prespecified sequence and model-agreement checks, compared with 38.8% for greedy and 13.1% for random substitution (paired difference 21.8 percentage points; 95% CI 18.3-25.3). Conclusions: SafeEdit-CRE reduced 3240 designs to 74 Tier A computational candidates prioritized for reporter screening across three cell types and four edit budgets. By separating search from cross-model audit and enforcing explicit edit budgets and sequence-domain safeguards, the framework provides a reproducible and auditable approach to computational CRE prioritization, regulatory-grammar studies, and synthetic regulatory-element design.
背景/目的:顺式调控元件(cre)有助于介导细胞类型选择性基因表达,但序列-活性模型很少转化为紧凑的、实验上可处理的、保留天然调控支架的变异面板。我们开发了SafeEdit-CRE,以选择最小的编辑,保留跨模型的特异性,同时减少预测的不确定性,从而产生一个可审计的CRE变体库,用于报告筛选优先级。方法:SafeEdit-CRE将已发表的Malinois序列活性预测器与架构不同的审稿人集合、验证校准的不确定性、序列域控制和约束束搜索相结合。天然的200 nt cre在1、5、10或20个核苷酸的固定替代预算下进行编辑,并通过单独训练的多核集成进行评估,该集成与候选生成完全分开。结果:在K562、HepG2和SK-N-SH细胞的600个不含CRE亲本中,SafeEdit-CRE的平均交叉模型特异性边际增益为0.877,而贪婪编辑为0.822(配对差异0.055;95% CI 0.040-0.071)。相对于计算机匹配的主模型光束,SafeEdit-CRE产生了相似的跨模型特异性边际增益(差值-0.004;95% CI -0.011-0.004),集合不确定性更低(0.314比0.335)。在锁定的90个亲本基准中,60.6%的设计通过了所有9个预先指定的序列和模型一致性检查,而贪婪替换为38.8%,随机替换为13.1%(配对差异21.8个百分点;95% CI 18.3-25.3)。结论:SafeEdit-CRE将3240个设计减少到74个A级计算候选设计,优先用于三种细胞类型和四种编辑预算的报告筛选。通过将搜索与跨模型审计分离,并强制执行显式编辑预算和序列域保护,该框架为计算CRE优先级、规则语法研究和综合规则元素设计提供了一种可重复和可审计的方法。
期刊介绍:
Genes (ISSN 2073-4425) is an international, peer-reviewed open access journal which provides an advanced forum for studies related to genes, genetics and genomics. It publishes reviews, research articles, communications and technical notes. There is no restriction on the length of the papers and we encourage scientists to publish their results in as much detail as possible.