71in the human genome, which can also be used for the reliable identifi cation of large
copy-variable regions. Problems with the methods include sequencing biases that
lead certain regions of the genome to be over- or under- sampled, lowering their
resolution and ability to accurately identify the exact breakpoints of the variants.
Digital Array for CNV Detection
Most of these approaches are limited in resolution and can at best distinguish a
twofold (or 50 %) difference in CNV. CNVs can be studied by using digital array, a
nanofl uidic biochip capable of accurately quantitating genes of interest in DNA
samples. This technology is exquisitely sensitive and is capable of differentiating as
little as a 15 % difference in CNV or 6–7 copies of a target gene. Analysis of DNA
samples for their CYP2D6 copy numbers shows that the results are consistent with
those obtained by conventional techniques. In a screening experiment with breast
cancer and normal DNA samples, the ERBB2 gene was found to be amplifi ed in
about 35 % of breast cancer samples (Qin et al. 2008 ). Thus the use of the digital
array enables accurate measurement of gene copy numbers and is of signifi cant
value in CNV studies.
CNVer Algorithm for CNV Detection
An algorithm for CNV detection, called CNVer, supplements the depth-of-coverage
with paired-end mapping information, where mate pairs mapping discordantly to
the reference serve to indicate the presence of variation (Medvedev et al. 2010 ). It
combines the information from high-throughput sequencing within a unifi ed com-
putational framework called the donor graph, enabling the mitigation of sequencing
biases that cause uneven local coverage and accurately predict CNVs. CNVer was
used to detect 4879 CNVs in genome of a Yoruban individual. Most of the calls
(77 %) coincide with previously known variants within the Database of Genomic
Variants, while 81 % of deletion copy number variants previously known for this
individual coincide with one of our loss calls. Furthermore, it was demonstrated that
CNVer can reconstruct the absolute copy counts of segments of the donor genome
and evaluate the feasibility of using CNVer with low coverage datasets.
CNVnator for Discovery of CNVs and Genotyping
CNVnator has been developed for CNV discovery and genotyping from read-depth
analysis of personal genome sequencing (Abyzov et al. 2011 ). This method is based
on combining the established mean-shift approach with additional refi nements to