Bioinformatický seminár

Tue 12 Apr. 2011, 17:20
I-9

Title: Yoon et al. Sensitive and accurate detection of copy number variants using read depth of coverage
Speaker: Pavol Kmeč

Methods for the direct detection of copy number variation (CNV)
genome-wide have become effective instruments for identifying genetic risk
factors for disease. The application of next-generation sequencing
platforms to genetic studies promises to improve sensitivity to detect
CNVs as well as inversions, indels, and SNPs. New computational approaches
are needed to systematically detect these variants from genome sequence
data. Existing sequence-based approaches for CNV detection are primarily
based on paired-end read mapping (PEM) as reported previously by Tuzun et
al. and Korbel et al. Due to limitations of the PEM approach, some classes
of CNVs are difficult to ascertain, including large insertions and
variants located within complex genomic regions. To overcome these
limitations, we developed a method for CNV detection using read depth of
coverage. Event-wise testing (EWT) is a method based on significance
testing. In contrast to standard segmentation algorithms that typically
operate by performing likelihood evaluation for every point in the genome,
EWT works on intervals of data points, rapidly searching for specific
classes of events. Overall false-positive rate is controlled by testing
the significance of each possible event and adjusting for multiple
testing. Deletions and duplications detected in an individual genome by
EWT are examined across multiple genomes to identify polymorphism between
individuals. We estimated error rates using simulations based on real
data, and we applied EWT to the analysis of chromosome 1 from paired-end
shotgun sequence data (30x) on five individuals. Our results suggest that
analysis of read depth is an effective approach for the detection of CNVs,
and it captures structural variants that are refractory to established
PEM-based methods.