Title
Correcting for cancer genome size and tumour cell content enables better estimation of copy number alterations from next-generation sequence data.
Abstract
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.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).An R package, called CNAnorm, is available at http://www.precancer.leeds.ac.uk/cnanorm or from Bioconductor.a.gusnanto@leeds.ac.ukSupplementary data are available at Bioinformatics online.
Year
DOI
Venue
2012
10.1093/bioinformatics/btr593
Bioinformatics
Keywords
Field
DocType
available data,tumour cell content,next-generation sequence data,supplementary data,aneuploid cancer genomes,cancer genome size,uk supplementary information,normal cell,lung cancer patient,simulated data,contamination level,better estimation,copy number alteration,actual copy number
Genome,Data mining,Genome size,Computer science,Bioconductor,Data sequences,Bioinformatics,Cancer,Copy Number Alteration,R package
Journal
Volume
Issue
ISSN
28
1
1367-4811
Citations 
PageRank 
References 
29
2.22
8
Authors
5
Name
Order
Citations
PageRank
Arief Gusnanto110511.78
Henry M Wood2322.73
Yudi Pawitan330126.55
Pamela Rabbitts4322.73
Stefano Berri5494.39