cn.MOPS: mixture of Poissons for discovering copy number variations in next generation sequencing data
Sprache des Titels:
HGV 2011 Proceedings
The quantitative analysis of next generation sequencing (NGS) data like the detection of copy
number variations (CNVs) is still challenging. Current methods detect CNVs as changes of read
densities along chromosomes, therefore they are prone to a high false discovery rate (FDR)
because of technological or genomic read count variations, even after GC correction. A high
FDR means many wrongly detected CNVs that are not associated with the disease considered
in a study, though correction for multiple testing must take them into account and thereby
decreases the study's discovery power.
We propose "Copy Number estimation by a Mixture Of PoissonS" (cn.MOPS) for CNV detection
from NGS data, which constructs a model across samples at each genomic position, therefore it
is not affected by read count variations along chromosomes. In a Bayesian framework,
cn.MOPS decomposes read variations across samples into integer copy numbers and noise by
its mixture components and Poisson distributions, respectively. The more the data drives the
posterior away from a Dirichlet prior corresponding to copy number two, the more likely the data
is caused by a CNV, and, the larger is the informative/non-informative (I/NI) call. cn.MOPS
detects a CNV in the DNA of an individual by a region with large I/NI calls. I/NI call based CNV
detection gurantees a low FDR because wrong detections are less likely for large I/NI calls.
We compare cn.MOPS with the five most popular CNV detection methods for NGS data at three
benchmark data sets: (1) artificial, (2) NGS data from a male HapMap individual with implanted
CNVs from the X chromosome, and (3) the HapMap phase 2 individuals with known CNVs. At
all benchmark data sets cn.MOPS outperformed its five competitors with respect to precision (1-
FDR) and recall both at gains and losses.