Getting personal with epigenetics: towards individual-specific epigenomic imputation with machine learning.
Getting personal with epigenetics: towards individual-specific epigenomic imputation with machine learning.
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DOI：
10.1038/s41467-023-40211-2
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发表时间：
2023-08-07
影响因子：
16.6
通讯作者：
Schweikert, Gabriele
中科院分区：
文献类型：
--
作者：
Hawkins-Hooker, Alex;Visona, Giovanni;Narendra, Tanmayee;Rojas-Carulla, Mateo;Schoelkopf, Bernhard;Schweikert, Gabriele
表观遗传修饰是参与基因表达调控的动态机制。与DNA序列不同，表观遗传模式不仅在个体之间存在差异，而且在个体内的不同细胞类型之间也存在差异。环境因素、体细胞突变和衰老导致表观遗传变化，这些变化可能构成疾病的早期标志或致病因素。表观遗传修饰是可逆的，因此有希望成为精准医学的治疗靶点。然而，确定个体细胞类型特异性表观基因组的作图工作受到实验成本和组织可及性的限制。为了解决这些挑战，我们开发了eDICE，这是一种基于注意力的深度学习模型，经过训练，可以通过对观察到的轨迹进行调节来估算缺失的表观基因组轨迹。使用最近发表的一组来自四个个体供体的表观基因组，我们表明，跨个体的迁移学习允许eDICE成功预测个体特异性表观遗传变异，即使在给定供体中未映射的组织中也是如此。这些结果突出了基于机器学习的插补方法在推进个性化表观基因组学方面的潜力。作者提出了eDICE，这是一种基于注意力的模型，可以准确估算观察到的表观遗传景观的缺失部分，并表明eDICE可用于预测EN-TEX数据集中的个体特异性表观基因组变异。
Epigenetic modifications are dynamic mechanisms involved in the regulation of gene expression. Unlike the DNA sequence, epigenetic patterns vary not only between individuals, but also between different cell types within an individual. Environmental factors, somatic mutations and ageing contribute to epigenetic changes that may constitute early hallmarks or causal factors of disease. Epigenetic modifications are reversible and thus promising therapeutic targets for precision medicine. However, mapping efforts to determine an individual’s cell-type-specific epigenome are constrained by experimental costs and tissue accessibility. To address these challenges, we developed eDICE, an attention-based deep learning model that is trained to impute missing epigenomic tracks by conditioning on observed tracks. Using a recently published set of epigenomes from four individual donors, we show that transfer learning across individuals allows eDICE to successfully predict individual-specific epigenetic variation even in tissues that are unmapped in a given donor. These results highlight the potential of machine learning-based imputation methods to advance personalized epigenomics. The authors present eDICE, an attention-based model that enables accurate imputation of missing portions of the observed epigenetic landscape, and show that eDICE can be used to predict individualspecific epigenomic variation in the EN-TEx dataset.
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影响因子：
46.9
作者：
Ernst J;Kellis M
通讯作者：
Kellis M
影响因子：
16.6
作者：
Durham TJ;Libbrecht MW;Howbert JJ;Bilmes J;Noble WS
通讯作者：
Noble WS
影响因子：
48
作者：
Hoffman, Michael M.;Buske, Orion J.;Wang, Jie;Weng, Zhiping;Bilmes, Jeff A.;Noble, William Stafford
通讯作者：
Noble, William Stafford
影响因子：
64.5
作者：
Rozowsky, Joel;Gao, Jiahao;Gerstein, Mark
通讯作者：
Gerstein, Mark
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64.8
作者：
Roadmap Epigenomics Consortium;Kundaje A;Meuleman W;Ernst J;Bilenky M;Yen A;Heravi-Moussavi A;Kheradpour P;Zhang Z;Wang J;Ziller MJ;Amin V;Whitaker JW;Schultz MD;Ward LD;Sarkar A;Quon G;Sandstrom RS;Eaton ML;Wu YC;Pfenning AR;Wang X;Claussnitzer M;Liu Y;Coarfa C;Harris RA;Shoresh N;Epstein CB;Gjoneska E;Leung D;Xie W;Hawkins RD;Lister R;Hong C;Gascard P;Mungall AJ;Moore R;Chuah E;Tam A;Canfield TK;Hansen RS;Kaul R;Sabo PJ;Bansal MS;Carles A;Dixon JR;Farh KH;Feizi S;Karlic R;Kim AR;Kulkarni A;Li D;Lowdon R;Elliott G;Mercer TR;Neph SJ;Onuchic V;Polak P;Rajagopal N;Ray P;Sallari RC;Siebenthall KT;Sinnott-Armstrong NA;Stevens M;Thurman RE;Wu J;Zhang B;Zhou X;Beaudet AE;Boyer LA;De Jager PL;Farnham PJ;Fisher SJ;Haussler D;Jones SJ;Li W;Marra MA;McManus MT;Sunyaev S;Thomson JA;Tlsty TD;Tsai LH;Wang W;Waterland RA;Zhang MQ;Chadwick LH;Bernstein BE;Costello JF;Ecker JR;Hirst M;Meissner A;Milosavljevic A;Ren B;Stamatoyannopoulos JA;Wang T;Kellis M
通讯作者：
Kellis M