Portal de Periódicos da CAPES

Comprehensive assessment of array-based platforms and calling algorithms for detection of copy number variants

2011; Nature Portfolio; Volume: 29; Issue: 6 Linguagem: Inglês

10.1038/nbt.1852

1546-1696

Dalila Pinto, Katayoon Darvishi, Xinghua Shi, Diana Rajan, Diane Rigler, Tomas Fitzgerald, Anath C. Lionel, Bhooma Thiruvahindrapuram, Jeffrey R. MacDonald, Ryan E. Mills, Aparna Prasad, Kristin Noonan, Susan Gribble, Elena Prigmore, Patricia K. Donahoe, Richard S. Smith, Ji Hyeon Park, Matthew E. Hurles, Nigel P. Carter, Charles Lee, Stephen W. Scherer, Lars Feuk,

Molecular Biology Techniques and Applications

When embarking on a microarray-based study of genomic copy number variation, what's helpful for navigating the myriad of available array platforms and data analysis approaches? Pinto et al. evaluate six samples from healthy controls in triplicate on commonly used combinations of commercial arrays and analytic tools, providing realistic comparisons of performance. We have systematically compared copy number variant (CNV) detection on eleven microarrays to evaluate data quality and CNV calling, reproducibility, concordance across array platforms and laboratory sites, breakpoint accuracy and analysis tool variability. Different analytic tools applied to the same raw data typically yield CNV calls with <50% concordance. Moreover, reproducibility in replicate experiments is <70% for most platforms. Nevertheless, these findings should not preclude detection of large CNVs for clinical diagnostic purposes because large CNVs with poor reproducibility are found primarily in complex genomic regions and would typically be removed by standard clinical data curation. The striking differences between CNV calls from different platforms and analytic tools highlight the importance of careful assessment of experimental design in discovery and association studies and of strict data curation and filtering in diagnostics. The CNV resource presented here allows independent data evaluation and provides a means to benchmark new algorithms.