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Published on: August 15, 2019
Anotación integradora de variantes de 1092 humanos: aplicación a la genómica del cáncer
Ekta Khurana1,2, Yao Fu1, Vincenza Colonna3,4
1Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.
Science (New York, N.Y.)
|October 5, 2013
Resumen
Identificar las variantes genéticas dañinas en los genomas personales es difícil. Este estudio utiliza patrones de polimorfismo para encontrar variantes dañinas, incluidas las no codificantes, y desarrolla una herramienta (FunSeq) para identificar los impulsores del cáncer.
Área de la Ciencia:
- La genómica es la genómica.
- La bioinformática es la bioinformática.
- Biología Molecular Biología Molecular
Sus antecedentes:
- La interpretación de variantes genéticas, particularmente en regiones no codificantes, presenta un desafío significativo en la genómica personal.
- Comprender el impacto funcional de las variaciones genéticas es crucial para diagnosticar enfermedades y desarrollar terapias dirigidas.
Objetivo del estudio:
- Desarrollar un método para identificar las variantes genéticas perjudiciales, incluidas las no codificantes, utilizando patrones de polimorfismos.
- Para crear una herramienta computacional (FunSeq) para predecir mutaciones no codificantes en los genomas del cáncer.
Principales métodos:
- Análisis de patrones de polimorfismo en regiones con anotaciones funcionales en 1092 genomas humanos.
- Validación experimental de las variantes nocivas candidatas identificadas.
- Desarrollo y aplicación de la herramienta computacional FunSeq a los genomas del cáncer.
Principales resultados:
- Identificación de regiones sensibles a la mutación ("ultra sensibles") y variantes que interrumpen la unión del factor de transcripción ("interruptores de motivos").
- Correlación entre variantes en regiones de alta centralidad de la red y efectos perjudiciales.
- Aplicación exitosa de FunSeq para identificar conductores no codificantes candidatos en genomas de cáncer.
Conclusiones:
- Los patrones de polimorfismos en las regiones funcionales pueden identificar eficazmente las variantes perjudiciales, incluidas las no codificantes.
- La herramienta FunSeq ayuda en el descubrimiento de mutaciones no codificantes del controlador, avanzando la investigación de la genómica del cáncer.
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