Application of Nexus Copy Number Software for CNV Detection and Analysis

Abstract

Among human structural genomic variation, copy number variants (CNVs) are the most frequently known component, comprised of gains/losses of DNA segments that are generally 1 kb in length or longer. Array-based comparative genomic hybridization (aCGH) has emerged as a powerful tool for detecting genomic copy number variants (CNVs). With the rapid increase in the density of array technology and with the adaptation of new high-throughput technology, a reliable and computationally scalable method for accurate mapping of recurring DNA copy number aberrations has become a main focus in research. Here we introduce Nexus Copy Number software, a platform-independent tool, to analyze the output files of all types of commercial and custom-made comparative genomic hybridization (CGH) and single-nucleotide polymorphism (SNP) arrays, such as those manufactured by Affymetrix, Agilent Technologies, Illumina, and Roche NimbleGen. It also supports data generated by various array image-analysis software tools such as GenePix, ImaGene, and BlueFuse.

Increased CNV-Region deletions in mild cognitive impairment (MCI) and Alzheimer's disease (AD) subjects in the ADNI sample

“…The overall high rate of consensus calls across approaches, both cross-algorithm reliabilities using PennCNV and NGS-based confirmation, supports the accuracy of Nexus CNV detection algorithm [49,50]. Recently, Dellinger et al.…”

Genomic profiling by whole‐genome single nucleotide polymorphism arrays in Wilms tumor and association with relapse

“…The use of a dedicated segmentation algorithm (Nexus Copy Number TM ) on raw fluorescence data allowed us to define at a high resolution the boundaries of the regions most significantly associated with the clinical variables investigated. Such an algorithm is widely used for CNAs and allelic ratio anomalies detection, particularly in the field of cancer genome profiling (Chen et al, 2011;Duy et al, 2011;Halldorsdottir et al, 2011;Ismail et al, 2011;Paulson et al, 2011) with an overall high rate of consensus calls across studies that supports its accuracy (Darvishi, 2010;Matsuzaki et al, 2009). Although it has been recently reported that Nexus may be affected with an overcall of CNAs, especially with more relaxed analysis parameters (Dellinger et al, 2010) in the present study, the use of Nexus with conservative parameters (in particular, as for the significance threshold and the minimum number of probes per segment), allowed us to find a good trade off between sensitivity and specificity in CNAs detection (Darvishi, 2010).…”

Continuing difficulties in interpreting CNV data: lessons from a genome-wide CNV association study of Australian HNPCC/lynch syndrome patients

“…Significance testing for group differences in individual CNV frequencies was performed utilising Nexus Protocol 5 [53] for Nexus output, and t-test implemented in Stata (version 10, StataCorp, College Station, TX) for QuantiSNP results, where the predictor variable was the quantitative copy number at each locus. To control the family-wise type I error rate (FWER) across multiple tests, a significance threshold of p < 3.6E-06 was used for significance tests of individual CNV events; a threshold derived by dividing the desired FWER of 0.05 by the total number of non-overlapping CNVs tested for significance.…”