Effective gene expression prediction from sequence by integrating long-range interactions.
Nat Methods
; 18(10): 1196-1203, 2021 10.
Article
en En
| MEDLINE
| ID: mdl-34608324
ABSTRACT
How noncoding DNA determines gene expression in different cell types is a major unsolved problem, and critical downstream applications in human genetics depend on improved solutions. Here, we report substantially improved gene expression prediction accuracy from DNA sequences through the use of a deep learning architecture, called Enformer, that is able to integrate information from long-range interactions (up to 100 kb away) in the genome. This improvement yielded more accurate variant effect predictions on gene expression for both natural genetic variants and saturation mutagenesis measured by massively parallel reporter assays. Furthermore, Enformer learned to predict enhancer-promoter interactions directly from the DNA sequence competitively with methods that take direct experimental data as input. We expect that these advances will enable more effective fine-mapping of human disease associations and provide a framework to interpret cis-regulatory evolution.
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
ADN
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Regulación de la Expresión Génica
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Bases de Datos Genéticas
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Epigénesis Genética
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Aprendizaje Automático
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Red Nerviosa
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Animals
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Humans
Idioma:
En
Revista:
Nat Methods
Asunto de la revista:
TECNICAS E PROCEDIMENTOS DE LABORATORIO
Año:
2021
Tipo del documento:
Article
País de afiliación:
Reino Unido