DiscoverSL: an R package for multi-omic data driven prediction of synthetic lethality in cancers.
Bioinformatics
; 35(4): 701-702, 2019 02 15.
Article
en En
| MEDLINE
| ID: mdl-30059974
ABSTRACT
SUMMARY:
Synthetic lethality is a state when simultaneous loss of two genes is lethal to a cancer cell, while the loss of the individual genes is not. We developed an R package DiscoverSL to predict and visualize synthetic lethality in cancers using multi-omic cancer data. Mutation, copy number alteration and gene expression data from The Cancer Genome Atlas project were combined to develop a multi-parametric Random Forest classifier. The effects of selectively targeting the predicted synthetic lethal genes is tested in silico using shRNA and drug screening data from cancer cell line databases. The clinical outcome in patients with mutation in primary gene and over/under-expression in the synthetic lethal gene is evaluated using Kaplan-Meier analysis. The method helps to identify new therapeutic approaches by exploiting the concept of synthetic lethality. AVAILABILITY AND IMPLEMENTATION DiscoverSL package with user manual and sample workflow is available for download from github url https//github.com/shaoli86/DiscoverSL/releases/tag/V1.0 under GNU GPL-3. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Programas Informáticos
/
Mutaciones Letales Sintéticas
/
Genes Letales
/
Neoplasias
Tipo de estudio:
Guideline
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2019
Tipo del documento:
Article
País de afiliación:
Estados Unidos