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Curr Protoc Hum Genet. 2014 Apr 24;81:7.23.1-21. doi: 10.1002/0471142905.hg0723s81.

Using XHMM Software to Detect Copy Number Variation in Whole-Exome Sequencing Data.

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Division of Psychiatric Genomics and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York; Stanley Center for Psychiatric Research and Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts; Analytic and Translational Genetics Unit Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts.


Copy number variation (CNV) has emerged as an important genetic component in human diseases, which are increasingly being studied for large numbers of samples by sequencing the coding regions of the genome, i.e., exome sequencing. Nonetheless, detecting this variation from such targeted sequencing data is a difficult task, involving sorting out signal from noise, for which we have recently developed a set of statistical and computational tools called XHMM. In this unit, we give detailed instructions on how to run XHMM and how to use the resulting CNV calls in biological analyses.


Hidden Markov Model (HMM); copy number variation (CNV); data normalization; next-generation sequencing (NGS); principal component analysis (PCA)

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