Computational workflow for functional characterization of COVID-19 through secondary data analysis.
STAR Protoc
; 2(4): 100873, 2021 12 17.
Article
en En
| MEDLINE
| ID: mdl-34746856
ABSTRACT
Standard transcriptomic analyses cannot fully capture the molecular mechanisms underlying disease pathophysiology and outcomes. We present a computational heterogeneous data integration and mining protocol that combines transcriptional signatures from multiple model systems, protein-protein interactions, single-cell RNA-seq markers, and phenotype-genotype associations to identify functional feature complexes. These feature modules represent a higher order multifeatured machines collectively working toward common pathophysiological goals. We apply this protocol for functional characterization of COVID-19, but it could be applied to many other diseases. For complete details on the use and execution of this protocol, please refer to Ghandikota et al. (2021).
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Programas Informáticos
/
Biología Computacional
/
Flujo de Trabajo
/
Transcriptoma
/
Análisis de Datos
/
SARS-CoV-2
/
COVID-19
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
STAR Protoc
Año:
2021
Tipo del documento:
Article
País de afiliación:
Estados Unidos