Comprehensive assessment of array-based platforms and calling algorithms for detection of copy number variants
By D. Pinto, K. Darvishi, Xinghua Shi, D. Rajan, Diane Rigler, Tomas W. Fitzgerald, A. Lionel, B. Thiruvahindrapuram, J. MacDonald, Ryan E. Mills and 12 more. Nature Biotechnology, 2011
431 citations recorded by the source corpus.
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.
Subfields: Genetics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/6d3784c0a1914f3d98baa84f492a2f2bd39c3fec
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In Genetics
Citations in the corpus
- De novo CNV analysis implicates specific abnormalities of postsynaptic signalling complexes in the pathogenesis of schizophrenia (2011, methodology): “28 However, relative enrichment for de novos among cases was similar for large de novos > 500 kb (2.1% vs 0.8%, P = 0.0014) as it was for small de novos 200 kb (2.3% vs 0.9%, P = 0.0035), and the overall size distribution of case de novos was not shifted towards smaller CNVs (Figure 2 and…”