Deep learning of human polyadenylation sites at nucleotide resolution reveals molecular determinants of site usage and relevance in disease.
Deep learning of human polyadenylation sites at nucleotide resolution reveals molecular determinants of site usage and relevance in disease.
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DOI：
10.1038/s41467-023-43266-3
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发表时间：
2023-11-15
影响因子：
16.6
通讯作者：
Ji, Zhe
中科院分区：
文献类型：
--
作者：
Stroup, Emily Kunce;Ji, Zhe
The genomic distribution of cleavage and polyadenylation (polyA) sites should be co-evolutionally optimized with the local gene structure. Otherwise, spurious polyadenylation can cause premature transcription termination and generate aberrant proteins. To obtain mechanistic insights into polyA site optimization across the human genome, we develop deep/machine learning models to identify genome-wide putative polyA sites at unprecedented nucleotide-level resolution and calculate their strength and usage in the genomic context. Our models quantitatively measure position-specific motif importance and their crosstalk in polyA site formation and cleavage heterogeneity. The intronic site expression is governed by the surrounding splicing landscape. The usage of alternative polyA sites in terminal exons is modulated by their relative locations and distance to downstream genes. Finally, we apply our models to reveal thousands of disease- and trait-associated genetic variants altering polyadenylation activity. Altogether, our models represent a valuable resource to dissect molecular mechanisms mediating genome-wide polyA site expression and characterize their functional roles in human diseases. The authors develop deep learning models to identify genome-wide polyA sites at nucleotide resolution and calculate site strength. They further examine genomic parameters regulating site usage and reveal genetic variants altering polyA activity.
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影响因子：
7.7
作者：
Geisberg JV;Moqtaderi Z;Struhl K
通讯作者：
Struhl K
影响因子：
14.9
作者：
Arhin, GK;Boots, M;Wilusz, J
通讯作者：
Wilusz, J
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
64.8
作者：
Bycroft C;Freeman C;Petkova D;Band G;Elliott LT;Sharp K;Motyer A;Vukcevic D;Delaneau O;O'Connell J;Cortes A;Welsh S;Young A;Effingham M;McVean G;Leslie S;Allen N;Donnelly P;Marchini J
通讯作者：
Marchini J
DOI：
10.1073/pnas.2121488119
发表时间：
2022-01-25
影响因子：
11.1
作者：
Moqtaderi Z;Geisberg JV;Struhl K
通讯作者：
Struhl K