Copy-number analysis and inference of subclonal populations in cancer genomes using Sclust
| Autor: | Ulrich Lang, Viktor Achter, Yupeng Cun, Tsun-Po Yang, Martin Peifer |
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| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Alternative methods Massive parallel sequencing DNA Copy Number Variations Copy number analysis Cancer Inference Computational Biology Computational biology Biology Biostatistics medicine.disease Genome General Biochemistry Genetics and Molecular Biology 03 medical and health sciences 030104 developmental biology Basic knowledge Neoplasms medicine Cluster Analysis Humans Cluster analysis Software |
| Zdroj: | Nature protocols. 13(6) |
| ISSN: | 1750-2799 |
| Popis: | This protocol describes how to use Sclust, a method for copy-number analysis and mutational clustering, to identify subclonal populations in tumor samples. 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 |
| Databáze: | OpenAIRE |
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