cn.mops

cn.mops (Copy Number estimation by a Mixture Of PoissonS) is a data processing pipeline for copy number variations and aberrations (CNVs and CNAs) from next generation sequencing (NGS) data. The package supplies functions to convert BAM files into read count matrices or genomic ranges objects, which are the input objects for cn.mops. cn.mops models the depths of coverage across samples at each genomic position. Therefore, it does not suffer from read count biases along chromosomes. Using a Bayesian approach, cn.mops decomposes read variations across samples into integer copy numbers and noise by its mixture components and Poisson distributions, respectively. cn.mops guarantees a low FDR because wrong detections are indicated by high noise and filtered out. cn.mops is very fast and written in C++.

The author and maintainer of the R Bioconductor package is Guenter Klambauer ([email protected]).

Our analysis led us to the following : cn.mops provides a function to directly read in counts from a BAM file called "getReadCountsFromBAM". One can provide a reference sequence that should be analysed using the argument "refSeqName"(in our case, "chr4"). This function returns the reads as an instance of "GRanges" and one can also provide the length of initial segmentation of the genome in base pairs or binSize. It is recommended that if the coverage is low, the segments should be longer. We chose 10000 to stay consistent and account for the low coverage.

If you have two samples (tumor and normal) , you should use the function "referencecn.mops". The default parameters of cn.mops are optimized for CNVs in non-tumor samples and have to be adjusted to the tumor-vs-control setting. For our given dataset, we used "referencecn.mops" with certain parameters (see "cnv-cn.mops.R").

We can produce a segmentation plot with the results for chr4 as shown below :
The x-axis represents the genomic position and the on the y-axis we see the log ratio of the read counts and the copy number call of each segment.