CICERO: a versatile method for detecting complex and diverse driver fusions using cancer RNA sequencing data.
Genome Biol
; 21(1): 126, 2020 05 28.
Article
en En
| MEDLINE
| ID: mdl-32466770
To discover driver fusions beyond canonical exon-to-exon chimeric transcripts, we develop CICERO, a local assembly-based algorithm that integrates RNA-seq read support with extensive annotation for candidate ranking. CICERO outperforms commonly used methods, achieving a 95% detection rate for 184 independently validated driver fusions including internal tandem duplications and other non-canonical events in 170 pediatric cancer transcriptomes. Re-analysis of TCGA glioblastoma RNA-seq unveils previously unreported kinase fusions (KLHL7-BRAF) and a 13% prevalence of EGFR C-terminal truncation. Accessible via standard or cloud-based implementation, CICERO enhances driver fusion detection for research and precision oncology. The CICERO source code is available at https://github.com/stjude/Cicero.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Programas Informáticos
/
Fusión Génica
/
Anotación de Secuencia Molecular
/
Neoplasias
Tipo de estudio:
Evaluation_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Genome Biol
Asunto de la revista:
BIOLOGIA MOLECULAR
/
GENETICA
Año:
2020
Tipo del documento:
Article
País de afiliación:
Estados Unidos
Pais de publicación:
Reino Unido