MOTIVATION: Comparison of read depths from next-generation sequencing between cancer and normal cells makes the estimation of copy number alteration (CNA) possible, even at very low coverage. However, estimating CNA from patients' tumour samples poses considerable challenges due to infiltration with normal cells and aneuploid cancer genomes. Here we provide a method that corrects contamination with normal cells and adjusts for genomes of different sizes so that the actual copy number of each region can be estimated. RESULTS: The procedure consists of several steps. First, we identify the multi-modality of the distribution of smoothed ratios. Then we use the estimates of the mean (modes) to identify underlying ploidy and the contamination level, and finally we perform the correction. The results indicate that the method works properly to estimate genomic regions with gains and losses in a range of simulated data as well as in two datasets from lung cancer patients. It also proves a powerful tool when analysing publicly available data from two cell lines (HCC1143 and COLO829). AVAILABILITY: An R package, called CNAnorm, is available at http://www.precancer.leeds.ac.uk/cnanorm or from Bioconductor. CONTACT: a.gusnanto@leeds.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Comparison of read depths from next-generation sequencing between cancer and normal cells makes the estimation of copy number alteration (CNA) possible, even at very low coverage. However, estimating CNA from patients' tumour samples poses considerable challenges due to infiltration with normal cells and aneuploid cancer genomes. Here we provide a method that corrects contamination with normal cells and adjusts for genomes of different sizes so that the actual copy number of each region can be estimated. RESULTS: The procedure consists of several steps. First, we identify the multi-modality of the distribution of smoothed ratios. Then we use the estimates of the mean (modes) to identify underlying ploidy and the contamination level, and finally we perform the correction. The results indicate that the method works properly to estimate genomic regions with gains and losses in a range of simulated data as well as in two datasets from lung cancerpatients. It also proves a powerful tool when analysing publicly available data from two cell lines (HCC1143 and COLO829). AVAILABILITY: An R package, called CNAnorm, is available at http://www.precancer.leeds.ac.uk/cnanorm or from Bioconductor. CONTACT: a.gusnanto@leeds.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Sarah Sandmann; Marius Wöste; Aniek O de Graaf; Birgit Burkhardt; Joop H Jansen; Martin Dugas Journal: Gigascience Date: 2020-11-02 Impact factor: 6.524