Copy-number analysis and inference of subclonal populations in cancer genomes using Sclust.
Nat Protoc
; 13(6): 1488-1501, 2018 06.
Article
em En
| MEDLINE
| ID: mdl-29844525
ABSTRACT
The genomes of cancer cells constantly change during pathogenesis. This evolutionary process can lead to the emergence of drug-resistant mutations in subclonal populations, which can hinder therapeutic intervention in patients. Data derived from massively parallel sequencing can be used to infer these subclonal populations using tumor-specific point mutations. The accurate determination of copy-number changes and tumor impurity is necessary to reliably infer subclonal populations by mutational clustering. This protocol describes how to use Sclust, a copy-number analysis method with a recently developed mutational clustering approach. In a series of simulations and comparisons with alternative methods, we have previously shown that Sclust accurately determines copy-number states and subclonal populations. Performance tests show that the method is computationally efficient, with copy-number analysis and mutational clustering taking <10 min. Sclust is designed such that even non-experts in computational biology or bioinformatics with basic knowledge of the Linux/Unix command-line syntax should be able to carry out analyses of subclonal populations.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Bioestatística
/
Biologia Computacional
/
Variações do Número de Cópias de DNA
/
Neoplasias
Tipo de estudo:
Guideline
Limite:
Humans
Idioma:
En
Revista:
Nat Protoc
Ano de publicação:
2018
Tipo de documento:
Article