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Nat Protoc. 2018 Jun;13(6):1488-1501. doi: 10.1038/nprot.2018.033. Epub 2018 May 24.

Copy-number analysis and inference of subclonal populations in cancer genomes using Sclust.

Author information

1
Department of Translational Genomics, Center for Integrated Oncology Cologne-Bonn, Medical Faculty, University of Cologne, Cologne, Germany.
2
Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany.
3
Computing Center, University of Cologne, Cologne, Germany.
4
Department of Informatics, University of Cologne, Cologne, Germany.

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.

PMID:
29844525
DOI:
10.1038/nprot.2018.033
[Indexed for MEDLINE]

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