Open Access Highly Accessed Open Badges Method

EXCAVATOR: detecting copy number variants from whole-exome sequencing data

Alberto Magi1*, Lorenzo Tattini12*, Ingrid Cifola3, Romina D’Aurizio4, Matteo Benelli5, Eleonora Mangano3, Cristina Battaglia36, Elena Bonora7, Ants Kurg8, Marco Seri7, Pamela Magini7, Betti Giusti1, Giovanni Romeo7, Tommaso Pippucci7, Gianluca De Bellis3, Rosanna Abbate1 and Gian Franco Gensini1

Author Affiliations

1 Department of Clinical and Experimental Medicine, University of Florence, Florence, Italy

2 Laboratory of Molecular Genetics, G. Gaslini Institute, Genoa, Italy

3 Institute for Biomedical Technologies, National Research Council, Segrate, Milano, Italy

4 Laboratory of Integrative Systems Medicine (LISM), Institute of Informatics and Telematics and Institute of Clinical Physiology, National Research Council, Pisa, Italy

5 Diagnostic Genetic Unit, Careggi Hospital, Florence, Italy

6 Dipartimento di Biotecnologie Mediche e Medicina Traslazionale (BIOMETRA), University of Milan, Milan, Italy

7 Medical Genetics Unit, Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy

8 Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia

For all author emails, please log on.

Genome Biology 2013, 14:R120  doi:10.1186/gb-2013-14-10-r120

Published: 30 October 2013


We developed a novel software tool, EXCAVATOR, for the detection of copy number variants (CNVs) from whole-exome sequencing data. EXCAVATOR combines a three-step normalization procedure with a novel heterogeneous hidden Markov model algorithm and a calling method that classifies genomic regions into five copy number states. We validate EXCAVATOR on three datasets and compare the results with three other methods. These analyses show that EXCAVATOR outperforms the other methods and is therefore a valuable tool for the investigation of CNVs in largescale projects, as well as in clinical research and diagnostics. EXCAVATOR is freely available at webcite.