Detection of copy number variants (CNV) from exome sequencing samples, including unpaired samples. The package implements a hidden Markov model which uses positional covariates, such as background read depth and GC-content, to simultaneously normalize and segment the samples into regions of constant copy count.
exomeCopy fits a hidden Markov model to observed read counts using covariates. It returns the Viterbi path, the most likely path of hidden states, which is the predicted copy count at each window.
exomeCopy is designed to run on read counts from consecutive
genomic ranges on a single chromosome, as it tries to identify higher
or lower read depth relative to a baseline. Please see the vignette
for an example of how to prepare input data for
to loop the function over multiple chromosomes and samples, and how to
extract the resulting predicted CNVs.
Love, Michael I.; Mysickova, Alena; Sun, Ruping; Kalscheuer, Vera; Vingron, Martin; and Haas, Stefan A. (2011) "Modeling Read Counts for CNV Detection in Exome Sequencing Data," Statistical Applications in Genetics and Molecular Biology: Vol. 10 : Iss. 1, Article 52. DOI: 10.2202/1544-6115.1732 http://cmb.molgen.mpg.de/publications/Love_2011_exomeCopy.pdf.
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