发表机构
Zhejiang University; University of Washington（浙江大学; 华盛顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有扰动响应预测仅依赖转录组的问题，提出染色质信息指导的ChromaPert框架，结合分子与DNA先验及配对RNA-ATAC特征，在未见靶标和保留状态中实现最高响应相关性，并显著提升转移预测性能。
AI 中文摘要
预测遗传扰动下的转录响应对于理解基因功能至关重要。现有预测方法主要依赖转录组测量，尽管染色质可及性提供了扰动作用所在细胞环境的补充信息。利用这一信息需要将染色质环境与特定扰动联系起来，并考虑独立采样的对照群体与扰动群体之间的基线差异。我们提出ChromaPert，一种染色质信息指导的框架，用于预测状态依赖性扰动响应。ChromaPert将分子和DNA信息指导的位点先验与配对的对照RNA--ATAC特征相结合，共同表示靶标身份和测量的细胞环境。其染色质引导响应路由器（CGRR）构建一个由对照衍生的表达偏移组成的源库，并通过扰动/位点或对照ATAC相似性检索相关源。这些偏移解释了基线差异，而条件传输流学习剩余的响应。在K562 CAT-ATAC和Perturb-Multiome上的三个未见靶标和保留状态设置中，ChromaPert在评估方法中实现了最高的全基因响应相关性。当将已知扰动转移到保留状态时，与最强基线相比，它使所有基因的相关性提高23.2%，对响应最强烈的二十个基因提高46.2%。正确的RNA--ATAC配对比打乱配对更能改善染色质相关响应斜率的恢复，而跨状态预测保留了对同一扰动的状态特异性响应。
英文摘要
Predicting transcriptional responses to genetic perturbations is central to understanding gene function. Existing predictors primarily rely on transcriptomic measurements, although chromatin accessibility provides complementary information about the cellular context in which perturbations act. Using this information requires linking chromatin context to specific perturbations and accounting for baseline differences between independently sampled control and perturbed populations. We propose ChromaPert, a chromatin-informed framework for predicting state-dependent perturbation responses. ChromaPert combines molecular and DNA-informed locus priors with paired control RNA--ATAC features to jointly represent target identity and measured cellular context. Its Chromatin-Guided Response Router (CGRR) builds a source bank of control-derived expression offsets and retrieves relevant sources using perturbation/locus or control-ATAC similarity. These offsets account for baseline differences, while conditional transport flow learns the remaining response. Across three unseen-target and held-out-state settings on K562 CAT-ATAC and Perturb-Multiome, ChromaPert achieves the highest all-gene response correlation among evaluated methods. When transferring known perturbations to held-out states, it improves correlation by 23.2% across all genes and 46.2% for the twenty most responsive genes over the strongest respective baselines. Correct RNA--ATAC pairing improves recovery of chromatin-associated response slopes over shuffled pairing, while cross-state predictions retain state-specific responses to the same perturbation.
