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++.

Author: Guenter Klambauer

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" whereas if you have more than 1 tumor samples and more than 1 normal samples, you should use case "cn.mops". For our given dataset , we used "referencecn.mops"

a detailed R script for the above can be found at: "cnv-cn.mops.R"