Inferring cancer progression from Single-Cell Sequencing while allowing mutation losses.
Bioinformatics
; 37(3): 326-333, 2021 04 20.
Article
em En
| MEDLINE
| ID: mdl-32805010
ABSTRACT
MOTIVATION In recent years, the well-known Infinite Sites Assumption has been a fundamental feature of computational methods devised for reconstructing tumor phylogenies and inferring cancer progressions. However, recent studies leveraging single-cell sequencing (SCS) techniques have shown evidence of the widespread recurrence and, especially, loss of mutations in several tumor samples. While there exist established computational methods that infer phylogenies with mutation losses, there remain some advancements to be made. RESULTS:
We present Simulated Annealing Single-Cell inference (SASC) a new and robust approach based on simulated annealing for the inference of cancer progression from SCS datasets. In particular, we introduce an extension of the model of evolution where mutations are only accumulated, by allowing also a limited amount of mutation loss in the evolutionary history of the tumor the Dollo-k model. We demonstrate that SASC achieves high levels of accuracy when tested on both simulated and real datasets and in comparison with some other available methods. AVAILABILITY AND IMPLEMENTATION The SASC tool is open source and available at https//github.com/sciccolella/sasc. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Análise de Célula Única
/
Neoplasias
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article