cn.MOPS: Mixture Of PoissonS for discovering Copy Number variations in next generation sequencing data
cn.MOPS is an algorithm
that accurately detects copy number variations in next generation
sequencing data in a study of multiple samples.
Quantitative analyses of next generation sequencing (NGS)
data, such as the detection of copy number variations
(CNVs), remain challenging. Current methods detect CNVs
as changes in the depth of coverage along chromosomes.
Technological or genomic variations in the depth of coverage
thus lead to a high false discovery rate (FDR), even upon
correction for GC content. In the context of association
studies between CNVs and disease, a high FDR means
many false CNVs, thereby decreasing the discovery power of
the study after correction for multiple testing. We propose
“Copy Number estimation by a Mixture Of PoissonS”
(cn.MOPS), a data processing pipeline for CNV detection
in NGS data. In contrast to previous approaches, cn.MOPS
incorporates modeling of depths of coverage across samples
at each genomic position. Therefore, cn.MOPS is not
affected by read count variations along chromosomes. Using
a Bayesian approach, cn.MOPS decomposes variations in
the depth of coverage across samples into integer copy
numbers and noise by means of its mixture components
and Poisson distributions, respectively. The noise estimate
allows for reducing the FDR by filtering out detections
having high noise which are likely to be false detections. We
compared cn.MOPS with the five most popular methods
for CNV detection methods in NGS data using four
benchmark data sets: (1) simulated data, (2) NGS data
from a male HapMap individual with implanted CNVs from
the X chromosome, (3) data from HapMap individuals
with known CNVs, (4) high coverage data from the
1000 Genomes Project. cn.MOPS outperformed its five
competitors in terms of precision (1–FDR) and recall for
both gains and losses in all benchmark data sets.
Please cite:
Günter Klambauer, Karin Schwarzbauer, Andreas Mayr, Djork-Arné Clevert, Andreas Mitterecker, Ulrich Bodenhofer, Sepp Hochreiter.
"cn.MOPS: mixture of Poissons for discovering copy number variations in next generation
sequencing data with a low false discovery rate." Nucleic Acids Research 2012 40(9); doi:10.1093/nar/gks003.
Abstract
Application areas: Although the original publication of cn.MOPS included only human whole genome sequencing data, cn.MOPS has been shown to work well for non-human genomes, for haploid genomes, for exome sequencing data, and for single cell sequencing. See links to publications below:
- cn.MOPS for copy number detection in Whole Genome Sequencing data Link1
- cn.MOPS for copy number detection in non-human species Link1
- cn.MOPS for copy number detection in haploid and bacterial genomes Link1 Link2 Link3
- cn.MOPS for copy number detection in Exome Sequencing data Link1 Link2 Link3
- cn.MOPS for copy number detection in Single Cell Sequencing data Link1
Paper:
Supplementary Notes:
Citation:
Official Link & DOI:
-
http://nar.oxfordjournals.org/content/40/9/e69
- DOI: 10.1093/nar/gks003
Download the R-package:
- Available at Bioconductor:
cn.mops R package
Additional normalization functions:
Copy number analysis of German outbreak strain E. Coli EHEC O104:H4
Datasets:
The benchmarking data sets used in our publication can be downloaded below.
- Simulated data with different coverages:
ReadCountsSimulated.RData PositionsSimulated.RData ReadCountsSimulatedHighCoverage.RData (R 2.14 checked) - Benchmarking data set with implanted CNVs from the X chromosome:
ReadCountsBenchmark.RData PositionsBenchmark.RData (R 2.14 checked)